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10.3389/fpsyg.2024.1354545
|
Assessing self-determined motivation for drinking alcohol via the Comprehensive Relative Autonomy Index for Drinking
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Introduction: Self-Determination Theory (SDT) examines human motivation in multiple domains; however, the only existing measure assessing SDT-informed behavioral regulations for drinking focuses on responsible drinker behaviors, rather than drinking per se, which is important given the alignment between SDT and harm reduction approaches to alcohol use. The aim of this study was to test the structural validity of the SDT-informed Comprehensive Relative Autonomy Index for Drinking (CRAI-Drinking) among college students.Methods: Participants included two convenience samples with a total of 630 adult drinkers (Mage = 21.5, 55% female, 88% undergraduates). Participants rated drinking behavioral regulations on the 24 original CRAI-Drinking items on a 5-point Likert Scale. Multi-dimensional scaling analyses and factor analyses were used to investigate the underlying autonomy continuum and factor structure of the CRAI-Drinking.Results: In Sample 1 (n = 274), multi-dimensional scaling analyses confirmed that CRAI-Drinking item and subscale order aligned with SDT's autonomy continuum. Confirmatory factor analyses supported a five factor, 19-item model of the CRAI-Drinking with factors for intrinsic, identified, positive introjected, external, and amotivation regulations (Cronbach's α: 0.68–0.85). In Sample 2 (n = 356), a confirmatory factor analysis confirmed that the 19-item model fit was comparable to Sample 1.Discussion: This study provides evidence for the structural validity of CRAI-Drinking scores for assessing SDT-based behavioral regulations for drinking in adults.
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16641078
|
PSYCHOLOGY
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10.3389/frai.2024.1467218
|
SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification
|
Introduction: In clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echocardiogram view classification is mainly divided into supervised and semi-supervised methods. The supervised echocardiogram view classification methods have worse generalization performance due to the difficulty of labeling echocardiographic images, while the semi-supervised echocardiogram view classification can achieve acceptable results via a little labeled data. However, the current semi-supervised echocardiogram view classification faces challenges of declining accuracy due to out-of-distribution data and is constrained by complex model structures in clinical application.Methods: To deal with the above challenges, we proposed a novel open-set semi-supervised method for echocardiogram view classification, SPEMix, which can improve performance and generalization by leveraging out-of-distribution unlabeled data. Our SPEMix consists of two core blocks, DAMix Block and SP Block. DAMix Block can generate a mixed mask that focuses on the valuable regions of echocardiograms at the pixel level to generate high-quality augmented echocardiograms for unlabeled data, improving classification accuracy. SP Block can generate a superclass pseudo-label of unlabeled data from the perspective of the superclass probability distribution, improving the classification generalization by leveraging the superclass pseudolabel.Results: We also evaluate the generalization of our method on the Unity dataset and the CAMUS dataset. The lightweight model trained with SPEMix can achieve the best classification performance on the publicly available TMED2 dataset.Discussion: For the first time, we applied the lightweight model to the echocardiogram view classification, which can solve the limits of the clinical application due to the complex model architecture and help cardiologists diagnose heart diseases more efficiently.
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26248212
|
AI
|
10.3389/feduc.2024.1478541
|
Play, reflect, cultivate social and emotional learning: a pathway to pre-service teacher SEL through playful pedagogies
|
Playful pedagogies, rooted in experiential learning, integrate play, humor, spontaneity, and levity to create engaging educational experiences. Playful pedagogies have been shown to support adults' emotional resilience and sense of belonging while reducing stress and anxiety. Despite these benefits, their use in education preparation programs (EPPs) remains underexplored. Given the increasing focus on teacher social and emotional learning (SEL), playful pedagogies hold significant potential for equipping future educators with the skills needed to foster both their own and their students' SEL growth. This paper advocates for a shift in teacher education from predominantly lecture-based instruction to a model that incorporates joy, humor, and experiential learning. We propose integrating playful pedagogies with a reflective learning cycle to enhance SEL competencies among pre-service teachers. Specifically, we introduce a conceptual model that combines a four-level pyramid of playful learning with an iterative reflection process. By integrating playful pedagogy into EPPs, we aim to foster resilience, creativity, and collaboration among future teachers, empowering them to create inclusive learning environments that nurture their students' holistic development.
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2504284X
|
EDUCATION
|
10.1007/s44196-024-00612-7
|
Image Registration Using the Arithmetic Optimization Algorithm for Robotic Visual Servoing
|
Visual servoing using image registration is a method employed in robotics to control the movement of a system using visual information. In this context, we propose a new intensity-based image registration algorithm (IBIR) that uses information derived from images acquired at different times or from different views to determine the parameters of the geometric transformations needed to align these images. The Arithmetic Optimization Algorithm (AOA) is used to optimize these parameters, minimizing the difference between the images to be aligned. The proposed algorithm, Intensity-Based Image Registration via Arithmetic Optimisation Algorithm (IBIRAOA), is robust to image data fluctuations and perturbations and can avoid local optima. Simulation results prove the importance and efficiency of the proposed algorithm in terms of computation time and similarity of aligned images compared to other methods based on various metaheuristics. In addition, our results confirm a significant improvement in the trajectory of the wheeled mobile robot, thus reinforcing the overall effectiveness of our method in practical navigation and robotic control applications.
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18756883
|
AI
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10.3389/feduc.2024.1468747
|
Innovative assessment based on multimedia proofs and social network sharing for introductory engineering courses
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Training and education quality are crucial worldwide even more in the area of technical vocation. The assessment validates the performance of the training and education process, however, the design of assessments that validate the acquisition of certain knowledge, abilities or competences is still unsolved and remains open for research. In this paper, the advance in new technologies is used in the assessment process in a step ahead. Since social media is a big part of the daily life for lots of students, an assessment method is proposed that uses social media as the learning motivation. In consequence, the proposed assessment method is completely aligned with the student way of life and this fact motivates the student learning process since generating new high impact social media contents is a very challenging task. The paper shows the results of an assessment innovation project in which the authors develop a methodology for evaluating laboratory practices that measures knowledge of introductory concepts in automatic control engineering courses. The innovation aims to actively involve students in the generation of social media resources through modern technologies that are attractive to them. Under this methodology, students are tasked with creating a series of short videos explaining how to solve a specific problem or elucidate a concept covered in theory lessons. These videos are shared with their peers via a social network. Lecturers evaluate videos considering the quality in the explanation of technical concepts and impact in the social network. Results show that the proposed assessment methodology increases student motivation compared to the traditional assessment process and increases marks.
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2504284X
|
EDUCATION
|
10.3389/frai.2024.1464690
|
Fostering effective hybrid human-LLM reasoning and decision making
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The impressive performance of modern Large Language Models (LLMs) across a wide range of tasks, along with their often non-trivial errors, has garnered unprecedented attention regarding the potential of AI and its impact on everyday life. While considerable effort has been and continues to be dedicated to overcoming the limitations of current models, the potentials and risks of human-LLM collaboration remain largely underexplored. In this perspective, we argue that enhancing the focus on human-LLM interaction should be a primary target for future LLM research. Specifically, we will briefly examine some of the biases that may hinder effective collaboration between humans and machines, explore potential solutions, and discuss two broader goals—mutual understanding and complementary team performance—that, in our view, future research should address to enhance effective human-LLM reasoning and decision-making.
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26248212
|
AI
|
10.3389/feduc.2024.1505020
|
Democracy is at risk: beliefs of Chilean teachers about the transmission of hate speech in teacher education
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Conversations about hate speech are a complex issue. It is not a new problem; on the contrary, society has been confronted with hate speech against specific communities at different moments. The present study aims to investigate the beliefs of Chilean teachers working in teacher education and their relationship with hate speech that may have occurred in their practice. The methodology was quantitative. The participants were 200 teachers. The data collection instrument was a survey to determine teachers’ beliefs. The results showed that teachers expressed concern about the problem and stated that action must be taken to combat hate speech. At the same time, they argued that their colleagues perpetuate and reproduce hate speech in their practice, which is also a complex situation that needs to be addressed. Finally, there is also a controversy about the limits of freedom of expression.
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2504284X
|
EDUCATION
|
10.3389/frai.2024.1472411
|
The sociolinguistic foundations of language modeling
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In this article, we introduce a sociolinguistic perspective on language modeling. We claim that language models in general are inherently modeling varieties of language, and we consider how this insight can inform the development and deployment of language models. We begin by presenting a technical definition of the concept of a variety of language as developed in sociolinguistics. We then discuss how this perspective could help us better understand five basic challenges in language modeling: social bias, domain adaptation, alignment, language change, and scale. We argue that to maximize the performance and societal value of language models it is important to carefully compile training corpora that accurately represent the specific varieties of language being modeled, drawing on theories, methods, and descriptions from the field of sociolinguistics.
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26248212
|
AI
|
10.3389/fpsyg.2024.1502222
|
Relationship between physical activity and college students’ life satisfaction: the chain mediating effect of psychological resilience and negative emotions
|
Objective: As the academic pressure, employment competition and mental health problems faced by college students are becoming more and more prominent, paying attention to and improving the quality of life and well-being of college students has become an important issue of widespread concern in all walks of life. This study focuses on the correlation between physical activity and college students’ life satisfaction.Methods: A cross-sectional survey method was applied to 326 college students, using the Physical Activity Rating Scale, the Psychological Resilience Scale, the Depression-Anxiety-Stress Scale, and the Life Satisfaction Scale. For data analysis, demographic analysis of variance, correlation analysis, and chain mediating effect test were conducted sequentially.Results: There were significant differences in psychological resilience, negative emotions, and life satisfaction by gender, and psychological resilience by grade level; there were significant correlations between physical activity and psychological resilience, negative emotions, and life satisfaction among college students (r = 0.541, p < 0.001; r = −0.379, p < 0.001; r = 0.435, p < 0.001); and psychological resilience, negative emotions had significant mediating and chain mediating effects between physical activity and life satisfaction, where the mediating effect of psychological resilience was significantly stronger than the mediating effect of negative emotions and the chain mediating effect of both.Conclusion: There was a correlation between physical activity and life satisfaction among college students, and this relationship was partially mediated by psychological resilience and negative emotions.
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16641078
|
PSYCHOLOGY
|
10.3389/frai.2024.1450477
|
Clinical entity-aware domain adaptation in low resource setting for inflammatory bowel disease
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The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text. Our research addresses the imperative for robust biomedical entity extraction, focusing specifically on inflammatory bowel disease (IBD). Leveraging novel domain-specific pre-training and entity-aware masking strategies with contrastive learning, we fine-tune and adapt a general language model to be better adapted to IBD-related information extraction scenarios. Our named entity recognition (NER) tool streamlines the retrieval process, supporting annotation, correction, and visualization functionalities. In summary, we developed a comprehensive pipeline for clinical Dutch NER encompassing an efficient domain adaptation strategy with domain-aware masking and model fine-tuning enhancements, and an end-to-end entity extraction tool, significantly advancing medical record curation and clinical workflows.
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26248212
|
AI
|
10.3389/fpsyg.2024.1546881
|
Editorial: (Ir)Relevance in education: individuals as navigators of dynamic information landscapes
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Involve (1) surface features, such as colour or shape, (2) visual/auditory/tactile attractiveness, (3) information source, (4) intrinsic and extraneous cognitive load (i.e., the load induced by the complexity of the learning materials and the instruction-induced load, respectively; Chen et al., 2023), and (5) the relationships between concurrently available components of information (e.g., the foreground to the background, the colorful to the black-and-white). The individual-level processes rely on (1) individual goals and meaning ascribed to given information, (2) cognitive processes such as stimulus-driven (bottom-up) and goal-directed (top-down) attention, working memory, and longterm memory, (3) germane cognitive load (the amount of cognitive resources, e.g., working memory, devoted to the task at hand; Korbach et al., 2017), (4) metacognitive processes, (5) previous experience, (6) affect, (7) motivation, and ( 8) attitudes. The context of information relevance comprises a myriad of features that are not embedded within the goal-driven activity, but nevertheless influence individual performance, such as (1) time constraints and (2) the sociocultural background of learners, teachers, and researchers. Of note, information relevance changes dynamically, that is, hinges not only on individual goals, but also on the outcomes of individual actions that preceded the present instance in which the individual is judging information relevance.Numerous theoretical accounts of information relevance were conceived over the last century of psychological, educational, and computer science research, spanning decision making and judgment, attention and memory, critical information literacy, problem solving, and other. Some of these accounts, such as Cognitive Load Theory (Sweller, 2011), Self-Regulated Learning (Panadero, 2017), Leont'ev's Activity Theory (Leont'ev, 1979), or Cognitive-Affective Theory of Learning with Media (Moreno, 2005), guided the research presented in the Research Topic, partly overlapping with Figure 1. To our best knowledge, however, no theory has, to date, comprehensively accounted for all aspects of information relevance. Hopefully, the present Editorial and the Research Topic will inspire drafting and testing such novel, comprehensive theories that are yet to be developed.The Désiron and Schneider examined how high school and university students responded to colorful design when dealing with relevant information. The study built upon the Cognitive Load Theory, the Cognitive-Affective Theory of Learning with Media, and the Emotional Design Hypothesis to assess whether colorful design correlated with higher learning outcomes, and whether contrasting colors further lowered cognitive load. The results suggested that colorful designs indeed correlated with higher performance, and that color contrast lowered the participant-perceived extraneous but not the intrinsic cognitive load.Greeves and Oz looked into differences in relevance judgments of YouTube videos between college instructors and students. Despite several similarities across groups, such as prioritizing video accuracy, content creators' expertise, or video duration, the students seemed to value additional features that would suggest community support for the content and the creator far more than the instructors.Leclerq et al. employed analogical card sorting tasks to examine whether 4-to 6-year-old preschoolers could learn to use self-cueing strategies such as labeling and pointing to transfer rules across these tasks. In line with expectations, children trained on such strategies were more likely to spontaneously use them on the analogical task.Lederer et al. assessed judging relevance of anecdotal, correlational, and experimental evidence in causal reasoning in preservice teachers and psychology students. Despite typical differences in methodological training across educational and psychological study programs, the authors found comparable performance levels across the two groups.Rhodes et al. offered a new perspective on relevance of problem solving tasks by highlighting the importance of sociocultural factors on the researcher's and the participant's side. The authors recommended a checklist for researchers who wish to develop new problem solving tasks.("what?"), subject ("for whom?"), asserter ("according to whom?), and a purpose ("to what end?") when examining the relevance of learning key mathematical concepts for high school students. Despite initial low levels of self-perceived relevance of such concepts, the students who participated in the study were shown to assert the relevance of the key concepts after learning about real-life applications and using their own imagination.The present Research Topic offers a broad outlook on information relevance judgments in educational and professional settings, but it suffered some limitations. Future research on information relevance should aim at developing comprehensive theoretical frameworks of information relevance, increasingly involve both young and aging participants, not only students and beginner professionals, and foster relevant collaborations beyond the WEIRD context.
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16641078
|
PSYCHOLOGY
|
10.3389/fonc.2024.1470824
|
Radiomics in rectal cancer: current status of use and advances in research
|
Rectal cancer is a leading cause of morbidity and mortality among patients with malignant tumors in China. In light of the advances made in therapeutic approaches such as neoadjuvant therapy and total mesorectal excision, precise preoperative assessment has become crucial for developing a personalized treatment plan. As an emerging technology, radiomics has gained widespread application in the diagnosis, assessment of treatment response, and analysis of prognosis for rectal cancer by extracting high-throughput quantitative features from medical images. Radiomics thus demonstrates considerable potential for optimizing clinical decision-making. In this paper, we reviewed recent research focusing on advances in the use of radiomics for managing rectal cancer. The review covers TNM staging of tumors, assessment of neoadjuvant therapy outcomes, and survival prediction. We also discuss the challenges and prospects for future developments in translational medicine, particularly the need for data standardization, consistent feature extraction methodologies, and rigorous model validation.
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2234943X
|
ONCOLOGY
|
10.3389/frai.2025.1481338
|
Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review
|
Background and objective: Very preterm infants are highly susceptible to Neurodevelopmental Impairments (NDIs), including cognitive, motor, and language deficits. This paper presents a systematic review of the application of Machine Learning (ML) techniques to predict NDIs in premature infants.Methods: This review presents a comparative analysis of existing studies from January 2018 to December 2023, highlighting their strengths, limitations, and future research directions.Results: We identified 26 studies that fulfilled the inclusion criteria. In addition, we explore the potential of ML algorithms and discuss commonly used data sources, including clinical and neuroimaging data. Furthermore, the inclusion of omics data as a contemporary approach employed, in other diagnostic contexts is proposed.Conclusions: We identified limitations and emphasized the significance of employing multimodal data models and explored various alternatives to address the limitations identified in the reviewed studies. The insights derived from this review guide researchers and clinicians toward improving early identification and intervention strategies for NDIs in this vulnerable population.
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26248212
|
AI
|
10.3389/feduc.2024.1416255
|
Hispanic-serving HBCUs: towards an anti-colonial meso-relevant theory of organizational identity in sacred spaces of Black education
|
Introduction: This study addresses demographic changes at HBCUs and proposes an anti-colonial organizational framework for Historically Black emerging Hispanic Serving Institutions (HB-eHSIs, also referred to as Hispanic-serving HBCUs) to support both Black and Brown students while preserving the historic mission of HBCUs.Methods: We use qualitative methodology and rely on 45–60 minute semi-structured interviews with 15 faculty and administrators from three Historically Black emerging HSIs in Texas to develop the proposed organizational framework.Results: Findings are highlighted through four key tenets, each operationalized based on themes from extant literature and the practices and organizational logics of Black and Brown faculty and staff at HBeHSIs: 1. Tending to white settler colonialism, 2. Tending to fiscal precarity, 3. Tending to sacred spaces, 4. Tending to fallacious notions of essentialism.Discussion: The proposed framework aims to foster solidarity between Black and Brown students and challenge oppressive systems through a radically inclusive approach to serving both communities. Recommendations include reexamining leadership structures, forming coalitions, and creating consortiums to support HBCUs’ evolving needs and diverse student populations. Findings also emphasize the need for dual federal designation for HB-eHSIs to secure funding and legitimacy.
|
2504284X
|
EDUCATION
|
10.1186/s40594-025-00526-6
|
Exploring how course social and cultural environmental features influence student engagement in STEM active learning courses: a control–value theory approach
|
Background: Active learning, on average, increases student performance in STEM courses. Yet, there is also large variation in the effectiveness of these implementations. A consistent goal of active learning is moving students towards becoming active constructors of their knowledge. This emphasis means student engagement is of central importance. Thus, variation in student engagement could help explain variation in outcomes from active learning. In this study, we employ Pekrun’s Control–Value Theory to examine the impact of four aspects of course social and cultural environments on student engagement. This theory posits that social and cultural features of the course environment influence students’ appraisals of their ability to control their academic outcomes from the course and the value they see in those outcomes. Control and value in turn influence the emotions students experience in the course and their behaviors. We selected four features of the course environment suggested in the literature to be important in active learning courses: course goal structure, relevance of course content, students’ trust in their instructor, and perceived course competition. Results: We surveyed students in 13 introductory STEM courses. We used structural equation modeling to map how features of the course environment related to control, value, and academic emotions, as well as how control, value, and academic emotions influenced engagement. We found engagement was positively related to control and value as well as the emotion of curiosity. Engagement was negatively related to the emotion of boredom. Importantly, features of the course environment influenced these four variables. All features influenced control: goal structure, relevance, and instructor trust increased it, while competition decreased it. All features except competition were related positively to value. Relevance and instructor trust increased curiosity. Goal structure, relevance, and instructor trust all reduced boredom, while competition increased it. Conclusion: Overall, our study suggests that the way instructors structure the social and cultural environment in active learning courses can impact engagement. Building positive instructor–student relationships, reducing course competition, emphasizing mastery and the relevance of the course to students can all increase engagement in course activities.
|
21967822
|
EDUCATION
|
10.3389/fonc.2024.1502185
|
Trifluridine/tipiracil regimen in combination with bevacizumab for metastatic colorectal cancer in the third line: an expert opinion
|
The prolongation of survival along with the preservation of quality of life, possibly avoiding harmful cumulative toxicities, is the primary therapeutic aim for patients with metastatic colorectal cancer (mCRC) in the third-line setting. Several therapeutic options are now available, although some differences across countries in drug approval and the optimal therapeutic sequencing associated with each peculiar patient subgroup represent a clinical challenge for oncologists. Among various options, the SUNLIGHT trial showed how the combination of trifluridine/tipiracil (FTD/TPI) with bevacizumab is effective with an easily manageable toxicity profile compared to FTD/TPI alone. Of note, the efficacy is confirmed independently from KRAS mutational status and also for patients who had breaks in anti-vascular endothelial growth factor (anti-VEGF) therapy. Herein, we describe the current state of the art in the landscape of treatments after the second progression in mCRC. Based on a critical review of the literature aimed to guide clinicians in their daily decision-making, we point out that the combination of FTD/TPI with bevacizumab produces a clinical benefit in unselected mCRC patients. Therefore, the FTD/TPI plus bevacizumab regimen can represent a new standard of care for the treatment of patients with refractory mCRC who have progressed after two lines of therapy.
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2234943X
|
ONCOLOGY
|
10.3390/ai6020022
|
Just-in-Time News: An AI Chatbot for the Modern Information Age
|
This study advances AI-powered news delivery by introducing an innovative chatbot capable of providing personalized news summaries and real-time event analysis. This approach addressed a critical gap identified through a comprehensive review of 52 AI chatbot studies. Unlike prior models limited to static information retrieval or predefined interactions, this chatbot harnesses generative AI and real-time data integration to deliver a dynamic and tailored news experience. Its unique architecture combines conversational AI, robotic process automation (RPA), a comprehensive news database (989,432 reports from 2342 sources spanning 27 October 2023 to 30 September 2024), and a large language model (LLM). Within this architecture, LLM generates dynamic queries against the News database for obtain tailored News for the users. Hence, this approach interprets user intent, and delivers LLM-based summaries of the fetched tailored news. Empirical testing with 35 users across 321 diverse news queries validated its robustness in navigating a combinatorial classification space of 53,916,650 potential news categorizations, achieving an F1-score of 0.97, recall of 0.99, and precision of 0.96. Deployed on Microsoft Teams and as a standalone web app, this research lays the foundation for transformative AI applications in news analysis, promising to revolutionize news consumption and empower a more informed citizenry.
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26732688
|
AI
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10.3389/frai.2024.1428716
|
Ocular Biometry OCR: a machine learning algorithm leveraging optical character recognition to extract intra ocular lens biometry measurements
|
Given close relationships between ocular structure and ophthalmic disease, ocular biometry measurements (including axial length, lens thickness, anterior chamber depth, and keratometry values) may be leveraged as features in the prediction of eye diseases. However, ocular biometry measurements are often stored as PDFs rather than as structured data in electronic health records. Thus, time-consuming and laborious manual data entry is required for using biometry data as a disease predictor. Herein, we used two separate models, PaddleOCR and Gemini, to extract eye specific biometric measurements from 2,965 Lenstar, 104 IOL Master 500, and 3,616 IOL Master 700 optical biometry reports. For each patient eye, our text extraction pipeline, referred to as Ocular Biometry OCR, involves 1) cropping the report to the biometric data, 2) extracting the text via the optical character recognition model, 3) post-processing the metrics and values into key value pairs, 4) correcting erroneous angles within the pairs, 5) computing the number of errors or missing values, and 6) selecting the window specific results with fewest errors or missing values. To ensure the models’ predictions could be put into a machine learning-ready format, artifacts were removed from categorical text data through manual modification where necessary. Performance was evaluated by scoring PaddleOCR and Gemini results. In the absence of ground truth, higher scoring indicated greater inter-model reliability, assuming an equal value between models indicated an accurate result. The detection scores, measuring the number of valid values (i.e., not missing or erroneous), were Lenstar: 0.990, IOLM 500: 1.000, and IOLM 700: 0.998. The similarity scores, measuring the number of equal values, were Lenstar: 0.995, IOLM 500: 0.999, and IOLM 700: 0.999. The agreement scores, combining detection and similarity scores, were Lenstar: 0.985, IOLM 500: 0.999, and IOLM 700: 0.998. IOLM 500 was annotated for ground truths; in this case, higher scoring indicated greater model-to-annotator accuracy. PaddleOCR-to-Annotator achieved scores of detection: 1.000, similarity: 0.999, and agreement: 0.999. Gemini-to-Annotator achieved scores of detection: 1.000, similarity: 1.000, and agreement: 1.000. Scores range from 0 to 1. While PaddleOCR and Gemini demonstrated high agreement, PaddleOCR offered slightly better performance upon reviewing quantitative and qualitative results.
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26248212
|
AI
|
10.1186/s40359-024-02297-1
|
Boy’s love fans versus non-fans in the sexual identity and neural response in the digital age’s young females
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With the omnipresence of online social media, Boys’ Love (BL) culture has found a burgeoning audience among young females. However, we know very little about the audience of this online cultural phenomena, also the potential implications of BL culture to female remain under-explored. Study 1 conducted a survey to investigate the BL audience’s demography data and attitudes to homosexual ect. The results of the questionnaire analysis showed that the sexual orientation and psychological gender of the female BL audiences are more diverse. In addition, we also find the audience spend a lot of time on BL. Study 2 focused on the BL senior fans to explore the neural and behavioral response of female while looking at Boys’ Love(BL) stimuli and Heterosexual love stimuli by fNIRS. Behavioral results showed that there was no main effect of reaction time and accuracy between the BL-fans and non-BL-fans. Neural results confirmed that the Oxy-Hb responses for BL-love stimuli in BL-fans was significantly lower than the non-BL-fans. In addition, the interaction effect showed that the Oxy-Hb responses was significantly higher for BL-love stimuli than for heterosexual love stimuli in non-BL-fans, and no difference was found in BL-fans. This finding, maybe along with the discovery that the more pornography a person was exposed to, the higher the brain dopamine threshold, and the subsequent weakening of the neural response to sexual stimulation. The research leads to the conclusion that long term exposed to Boys’ Love may decrease the reward sensitivity to BL stimuli and weakens the brain’s response of the right ventrolateral prefrontal cortex (rVLPFC) to BL stimuli.
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20507283
|
PSYCHOLOGY
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10.3389/fpsyg.2024.1515406
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Identification of stress factors in returning migrants in Latvia
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This study investigates the psychological stress factors faced by return migrants before, during, and after their return to Latvia. Employing a Grounded Theory methodology, we conducted in-depth interviews with 21 return migrants and identified five key themes: pre-return context, identity, perceived social support, psychological wellbeing, and factors that help or hinder re-adjustment. Notably, psychological stress prior to return often exceeds post-return stress, highlighting the critical yet understudied pre-return phase. Key contributors to return migration stress include unmet expectations, feelings of alienation, identity struggles, and inadequate institutional support. By highlighting these stress factors, this research not only enhances the understanding of return migration from a psychological standpoint but also lays the foundational groundwork for the development of a comprehensive theoretical framework that encompasses a broader spectrum of factors influencing return migration stress. The study advocates for a holistic approach to supporting return migrants, emphasizing the integration of psychological resources with practical assistance to foster successful reintegration into their home country.
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16641078
|
PSYCHOLOGY
|
10.3389/frai.2024.1496066
|
Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision
|
Introduction: Advancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields, including agriculture, this progress has outpaced the availability of sufficiently diverse and high-quality datasets, which now serve as a limiting factor. While many agricultural use cases appear feasible with current compute resources and ML algorithms, the lack of reusable hardware and software components, referred to as cyberinfrastructure (CI), for collecting, transmitting, cleaning, labeling, and training datasets is a major hindrance toward developing solutions to address agricultural use cases. This study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field.Methods: Data were collected during the 2023 growing season from three agricultural research locations across Ohio. The dataset includes 1 terabyte (TB) of multimodal data, comprising Unmanned Aerial System (UAS) imagery (RGB and multispectral), as well as soil and weather sensor data. The two primary crops studied were corn and soybean, which are the state's most widely cultivated crops. The data collected and processed from this study were used to train ML models to make predictions of crop growth stage, soil moisture, and final yield.Results: The exercise of processing this dataset resulted in four CI components that can be used to provide higher accuracy predictions in the agricultural domain. These components included (1) a UAS imagery pipeline that reduced processing time and improved image quality over standard methods, (2) a tabular data pipeline that aggregated data from multiple sources and temporal resolutions and aligned it with a common temporal resolution, (3) an approach to adapting the model architecture for a vision transformer (ViT) that incorporates agricultural domain expertise, and (4) a data visualization prototype that was used to identify outliers and improve trust in the data.Discussion: Further work will be aimed at maturing the CI components and implementing them on high performance computing (HPC). There are open questions as to how CI components like these can best be leveraged to serve the needs of the agricultural community to accelerate the development of ML applications in agriculture.
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26248212
|
AI
|
10.3389/fpsyg.2024.1458460
|
The impact of artistic sports on academic self-efficacy
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Introduction: Artistic sports have a more positive impact on adolescents on the basis of basic sports. This study delves into the beneficial effects of Artistic sports compared to basic sports in enhancing academic self-efficacy in college students, and investigates the mediating roles of mindfulness, social anxiety, and academic procrastination in this process.Methods: A questionnaire survey was conducted among students in some universities in Gansu Province, collecting a total of 1,976 online questionnaires, including 263 males and 1,713 females, with 1,543 participants in Artistic sports courses and 433 participants in basic sports. Data processing was carried out using SPSS 26.0 software and its plugin PROCESS.Results: The analysis results indicate significant differences in mindfulness, social anxiety, academic procrastination, and academic self-efficacy among different types of sports training (ps < 0.05); significant correlations were found among all variables (ps < 0.001). Sports training types can directly predict academic self-efficacy (β = 0.069, t = 3.155, p < 0.01), further confirming that sports training types can directly predict academic self-efficacy. Moreover, mindfulness, social anxiety, and academic procrastination play a chain mediating role between Artistic sports and academic self-efficacy.Discussion: These findings highlight the potential value of Artistic sports in enhancing academic self-efficacy and provide practical guidance for education policymakers, school administrators, teachers, parents, and students to promote adolescent academic and psychological health development. It is recommended to enhance the promotion and training of Artistic sports.
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16641078
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PSYCHOLOGY
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10.3389/frai.2024.1488359
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Commentary: Implications of causality in artificial intelligence
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Luís Cavique's (2024) article, "Implications of Causality in Artificial Intelligence," presents a compelling case for the importance of causalAI. By focusing on cause-and-effect relationships rather than mere correlations, causalAI offers a pathway to more transparent, fair, and reliable AI systems. Cavique argues that causalAI is the least criticized approach compared to responsible AI, fair AI, and explainable AI, largely due to its scientific rigor and potential to reduce biases. However, despite its promise, causalAI is not without challenges. This commentary aims to assess some of these limitations and potential criticisms of causalAI as presented by Cavique, arguing that while it holds substantial promise, its implementation and practical application may be more complex and fraught with difficulties than the author suggests.One of the primary challenges with causalAI lies in its complexity. CausalAI requires a deep understanding of causal inference and advanced statistical techniques, making it less accessible to most AI developers (Cox Jr., 2023). Unlike correlation-based methods, which are widely understood and now relatively easy to implement, causal models demand a high level of expertise. Arguably, only a select group of experts can effectively design, implement, and interpret these models. This complexity can create barriers to entry for many organizations and individuals who might want to engage in developing or using causalAI for benefiting from the transparency and fairness that causalAI promises. This could exacerbate existing disparities in AI literacy, and capacitation, and epistemic justice, potentially leading to an increased form of AI elitism, where only those with advanced skills, knowledge, and wealth of resources can fully participate in or critique causalAI development. This situation could undermine the broader goal of making its benefits accessible to a wide audience.CausalAI's reliance on high-quality, detailed data presents another significant challenge. Establishing causal relationships requires data that not only captures correlations but also provides the context needed to infer causality (Vallverdú, 2024). In many real-world applications, such data is either unavailable or prohibitively expensive to obtain. Causal AI requires high-quality data that captures both correlations and context (Vallverdú, 2024). In practice, such data is often scarce or costly, posing challenges for establishing accurate causal relationships Additionally, even when data is available, it may be incomplete or biased in ways that could skew causal inferences. The assumptions underlying causal models also warrant critical examination. CausalAI models often assume that all relevant variables have been identified and correctly measured. However, in practice, unmeasured confounders-variables that influence both the cause and effect-can distort causal estimates, leading to incorrect conclusions and as Rawal et al (2024) put it there is a lack of ground truth for validation. This reliance on potentially faulty assumptions could result in AI systems that, while appearing transparent and fair, are actually based on flawed reasoning. Furthermore, the process of identifying and validating causal relationships can be resource intensive and time-consuming. This raises questions about the scalability of causalAI, particularly in dynamic environments where data is constantly evolving, and causal relationships may shift over time. The effort required to maintain accurate causal models could outweigh the benefits, especially in fast-paced industries where quick decision-making is critical.Scalability is a major challenge for causal AI, as building and validating models is complex and resource-intensive. These models often require tailored adjustments for new contexts, limiting their generalizability compared to correlation-based methods. Scalability is a crucial consideration in the deployment of AI systems, and causalAI may struggle in this area. The process of building and validating causal models is not only complex, but also resource-intensive. As Cavique rightly notes, causalAI requires meticulous identification of causal variables and relationships, which may not easily generalize across different contexts or applications, particularly in sectors requiring a major data curation effort (such as the healthcare sector). This limitation could hinder the practical application of causalAI in scenarios where scalability and adaptability are key. Specificity required by causal models may limit their ability to generalize across different datasets or environments. While correlation-based models can often be applied broadly with minimal adjustments, causal models may need to be tailored to the particularities of each new situation. This lack of generalizability could make causalAI less appealing in settings where adaptability is needed.CausalAI is lauded for its potential to improve fairness and transparency in AI systems, but these benefits are not guaranteed. The causal relationships identified by AI systems are not immune to the biases present in the underlying data. If the data reflects existing societal biases or power dynamics, the causal models derived from it may inadvertently reinforce these issues. Put more Even when accurately identifying cause-and-effect relationships, they may perpetuate societal biases, potentially reinforcing inequities if not designed inclusively.simply, a causal model trained on biased data might correctly identify a causal relationship but still perpetuate unjust outcomes. Moreover, the iInterpretation of causal models can be influenced by the subjective perspectives of those designing or using them (Mittelstadt et al., 2019)-especially if the design of CausalAI is not inclusive and transparent, allowing for the active participation of stakeholders. This subjectivity introduces another layer of potential bias, as different stakeholders may have...
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26248212
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AI
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10.3389/frai.2025.1506042
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Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems
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Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. This approach aims to improve the classification accuracy of EMG signals while substantially reducing computational costs, offering valuable implications for all EMG-related processes on such data. The proposed methodology involves extracting time and frequency domain features from twelve channels of EMG signals, followed by dimensionality reduction using techniques such as PCA, LDA, PPCA, Lasso and GPLVM, and classification using an ANN. Our investigation revealed that LDA is not appropriate for this dataset. The dimensionality reduction models did not have any significant effect on the accuracy, but the computational cost decreased significantly. In individual comparisons, GPLVM had the shortest computational time (29 s), which was significantly less than that of all the other models (p < 0.05), with PCA following at approximately 35 s and Relief at approximately 57 s, while PPCA took approximately 69 s, and Lasso exhibited higher computational costs than all the models but lower computational costs than did the original set. Using the best-performing features, all possible sets of 2, 3, 4 and 5 features were tested, and the 5-feature set exhibited the best performance. This research demonstrates the effectiveness of dimensionality reduction and feature selection in improving the accuracy of movement recognition in myoelectric control.
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26248212
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AI
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10.3389/frai.2025.1518850
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Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system
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A security inspection system exemplifies human-machine collaboration, and enhancing its safety and reliability through advanced technology remains a key research priority. While deep learning has incrementally improved the autonomous capabilities of security inspection equipment for automatic contraband detection, a gap persists between current technological capabilities and practical implementation. Recognizing that humans excel at learning, reasoning, and collaborating, while artificial intelligence offers normative, repeatable, and logical processing, we propose a human-in-the-loop hybrid augmented intelligence approach. This approach addresses the practical needs of security inspection systems by introducing a hybrid decision-making method that leverages two distinct strategies: “Reject-priority” and “Clear-priority.” These strategies play complementary roles in bolstering the decision-making process’s overall performance. Comparative experiments on a dataset from a specific security inspection site confirmed the hybrid method’s effectiveness, drawing several conclusions. This “Hybrid decision-making” method not only enhances risk perception, thereby widening the safety margin of the security inspection system, but also reduces the need for human labor, leading to increased efficiency and reduced labor costs. Additionally, it is less time-consuming, further improving the system’s overall efficiency. By integrating human and machine intelligence, this method significantly boosts decision-making effectiveness. Tailored to their unique characteristics, the method based on “Reject-priority” strategy is particularly well-suited for security inspection scenarios that demand stringent safety protocols, while the “Clear-priority” method is ideal for scenarios with high-volume traffic flow, where efficiency is paramount. As the volume of collected data grows, this approach will enable seamless adaptation of the method to evolving application needs.
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26248212
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AI
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10.1007/s00432-025-06088-y
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The role of cGAS-STING pathway in the development of radiation-induced lung injury
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Background and purpose Radiation-induced lung injury (RILI) limits the efficacy of thoracic radiotherapy. However, the underlying mechanism of RILI remains unclear. cGAS-STING pathway is reported to be involved in the recognization of cytosolic dsDNA and various inflammatory diseases. This study aimed to investigate the role of cGAS-STING pathway in the development of RILI. Materials and methods A pre-clinical mouse model of RILI was established by whole thorax irradiation and confirmed using H&E and Masson’s trichrome staining. STING agonist (DMXAA) and antagonist(C-176) were administrated to modulate cGAS-STING pathway in vivo. Western blot and ELISA were used to determine the expression levels of different proteins. Results Quantitation analysis showed dsDNA accumulation in lung tissue and western blot showed the up-regulation of cGAS and STING protein level post-irradiation, indicating pathway activation. Histological evaluation showed that C-176 administration ameliorated radiation-induced pulmonary inflammation and fibrosis, while DMXAA exhibited contrary effects. In further in vitro study, the release of dsDNA induced by radiation led to the activation of cGAS-STING pathway in RAW 264.7 cells, resulting in the polarization into M1 phenotype and pro-inflammatory production. Conclusion In summary, our data demonstrated a link between cGAS-STING pathway and the development of RILI, indicating its potential application in clinic.
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14321335
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ONCOLOGY
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10.3389/frai.2024.1472236
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Enhancing Africa’s agriculture and food systems through responsible and gender inclusive AI innovation: insights from AI4AFS network
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The integration of artificial intelligence (AI) technologies into agriculture holds urgent and transformative potential for enhancing food security across Sub-Saharan Africa (SSA), a region acutely impacted by climate change and resource constraints. This paper examines experiences from the Artificial Intelligence for Agriculture and Food Systems (AI4AFS) Innovation Research Network, which provided funding to innovative projects in eight SSA countries. Through a set of case studies, we explore AI-driven solutions for pest and disease detection across crops such as cashew, maize, tomato, and cassava, including a real-time health monitoring tool for Nsukka Yellow pepper. Using participatory design, and key informant interview, robust monitoring and evaluation, and incorporating ethical frameworks, the research prioritizes gender equality, social inclusion, and environmental sustainability in AI development and deployment. Our results demonstrate that responsible AI practices can significantly enhance agricultural productivity while maintaining low carbon footprints. This research offers a unique, localized perspective on AI’s role in addressing SSA’s agricultural challenges, with implications for global food security as demand rises and environmental resources shrink. Key recommendations include establishing robust policy frameworks, strengthening capacity-building efforts, and securing sustainable funding mechanisms to support long-term AI adoption. This work provides the global community, policymakers, and stakeholders with critical insights on establishing ethical, responsible, and inclusive AI practices that can be adapted to similar agricultural contexts worldwide, contributing to sustainable food systems on an international scale.
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26248212
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AI
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10.3389/feduc.2025.1555200
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Editorial: Networks and knowledge brokering: advancing foundations, inviting complexity
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Across this special issue, the contributing articles illuminate how knowledge brokerage and relational networks can be harnessed-and sometimes challenged-to strengthen evidenceinformed policy and practice in education. Their findings offer new insights into the interplay of theoretical concepts, methodological approaches, and ethical imperatives that shape this complex terrain. Several contributions highlight the distinctive roles and practices of knowledge brokers. For instance, Malin and Shewchuk (2024) emphasize that knowledge brokers are not merely neutral intermediaries; rather, they are "actors whose activities and decisions must be understood contextually-e.g., in relation to the communities that are being connected and to brokers' placement within systems" (p. 3). Similarly, Caduff et al. (2024) explore how brokers' relational ecosystems both broaden and constrain their ability to mobilize resources and facilitate innovation through the strong and weak social ties they cultivate.In pushing beyond conventional frameworks, some articles spotlight relational networks as sites of strategic innovation. Bohannon et al. (2024) demonstrate how boundary infrastructures, such as co-designed professional learning opportunities and flexible organizational routines, help rural districts adapt and learn in dynamic contexts. Turner et al. (2024) extend this line of thought by mapping social networks related to mental health supports in schools. Their analysis reveals how patterns of interaction and trust-building open or close pathways for critical knowledge flows.Equity and ethics also figure prominently. Malin and Shewchuk (2024) advocate for an equity-centered lens, urging brokers to foreground issues of representation, power, and justice in their work. This stance resonates with Friesen and Brown's (2024) exploration of teacherleaders' professional learning, where the growth of confidence and capabilities is tied closely to the careful, context-sensitive design of relational activities that honour diverse perspectives.Methodologically, these studies introduce varied research designs-ranging from social network analysis to in-depth qualitative case studies-that yield a rich understanding of how knowledge moves through and transforms educational ecosystems. Collectively, the articles underscore a need for more approaches that capture complexity rather than oversimplify.In terms of implications, the authors suggest that policymakers, leaders, and practitioners who aim to strengthen ties between research, policy, and practice must attend to the subtleties of relationships, resources, and values. Rather than a technical fix, advancing equitable and impactful knowledge brokerage requires sustained reflection, dialogue, and openness to contextspecific adaptations.In recent years, scholars and practitioners have recognized that addressing complex issues-ranging from mental health supports in schools to rural capacity-building-cannot be achieved by simplistic, top-down evidence dissemination alone. There is a renewed emphasis on building relational infrastructures that acknowledge the multi-level interplay of policies, practices, and diverse forms of expertise (MacKillop et al., 2020). The articles presented in this research topic both reinforce and deepen this perspective. By examining relational ecosystems, boundary infrastructures, and equity-centered approaches, they suggest that knowledge brokerage and relational networks are integral elements of educational change, not just beneficial add-ons. Their collective insights resonate with an emerging scholarship that views relational networks as essential to leveraging complexity and mobilizing knowledge in service of local and global educational aims (Penuel et al., 2020;Rodway et al., 2021).For policymakers and practitioners, these findings imply that designing more flexible, equity-aware systems is crucial. Rather than imposing standardized reforms, leaders might consider strategies such as co-designing professional learning that respects multiple knowledge systems and power differentials. Such approaches can help ensure that local expertise is not overshadowed by distant authorities-a point highlighted when Bohannon et al. (2024) found that "even the best-intentioned external partners must negotiate shared ownership with rural educators" (p. XX).For researchers, there is a fertile landscape for future inquiries. Comparative, crossdisciplinary work could elucidate how relational networks evolve in varying socio-political contexts. Longitudinal research might track the lasting impacts of network-based interventions, while other methods-such as critical ethnographies or participatory action research-could surface subtle power imbalances that shape learning processes over time. These studies prompt a renewed attentiveness to the human, relational dimension of educational change. The educational challenges faced worldwide call for approaches to change that value complexity and contextual nuance. By continuing to explore this terrain and by refining methodologies to capture the contours and dimensions of knowledge brokerage in relational networks, educational communities can move closer to realizing meaningful, sustained improvements that are both evidence-informed and locally resonant.
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2504284X
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EDUCATION
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10.3389/feduc.2024.1421716
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Spatial and social relevance perceptions by pre-service teachers of learning about oil palm management as a local or nonlocal socioscientific issue
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Introduction: Pre-service teachers (PST)' perceived relevance of learning about environmental socioscientific issues (SSI) can be an indicator for their motivation to act as change agents. Until now, science education (research) has often addressed the relevance for learning about SSI insufficiently differentiated regarding spatial and social dimensions. However, theoretical frameworks suggest that such differentiation enhances meaningful teaching and learning. This study investigated how local, national, and global subdimensions of spatial relevance as well as individual, societal, and professional subdimensions of social relevance influence PST' relevance perceptions of learning about SSI. Additionally, we examined how relevance perceptions vary depending on whether the SSI is local or nonlocal to PST. We specifically investigated Indonesian PST' relevance perceptions of learning about oil palm management (OPM), a local SSI for PST of one university and a nonlocal SSI for PST of two other universities.Methods: The PST participated in a 5-week socioscientific inquiry-based educational unit on OPM in curricular courses (N = 111). We followed a mixed-method approach, employing measurements of utility value. Utility value is a specific construct of perceived relevance, which refers to the usefulness of learning about objects for a person's life, profession, and society. Quantitatively, we conducted pretest-posttest-follow-up surveys on PST' perceived utility value for learning about OPM over time. Qualitatively, we analyzed responses to a utility value reflection task that was integrated into the unit.Results: Overall, the unit increased PST' utility value over time. Local PST perceived lower utility value for learning about OPM than nonlocal PST. In the task responses, local PST referred more to the local subdimension, whereas nonlocal PST referred more to the national subdimension. Nonlocal PST' societal and professional utility value increased stronger over time compared to local PST.Discussion: We discuss potential reasons for local PST' lower relevance perceptions, e.g., personal experiences and skepticism through local embeddedness. Our findings on relevance perceptions among local and nonlocal PST underscore the importance of spatial- and social-sensitive SSI education. We point out practical implications for promoting relevance perceptions considering local and nonlocal PST. Moreover, we suggest research directions for more differentiated relevance research in science education.
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2504284X
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EDUCATION
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10.3389/fpsyg.2024.1463191
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Acceptance of sexual attraction and its link to psychological distress and sexual offending among pedohebephilic clients: results from a preliminary analysis
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Introduction: Pedohebephilic disorder is characterized by intense sexual urges or fantasies involving children, which can lead to distress or sexual behavior with children. While theoretical and qualitative accounts suggest that accepting one’s pedohebephilic sexual interests may help mitigate both distress and problematic behaviors, the only published quantitative study to date has linked acceptance with behavior but did not analyze its effect on distress.Methods: We examined the relationship between acceptance of sexual interests and child sexual abuse (CSA), the use of child sexual exploitation material (CSEM), and psychological distress in 238 pedohebephilic and teleiophilic men outside the judicial system (i.e., in the “Dunkelfeld”).Results: Compared to teleiophilic individuals, pedohebephilic individuals showed lower acceptance of their sexual interests. No significant differences were found between groups regarding past sexual offending. In a subsample of 197 pedohebephilic individuals (n = 197), correlations with recent sexual behavior were minimal. In another subsample of pedohebephilic men (n = 84) with data on psychological distress, increased acceptance was associated with decreased psychological distress, although this association weakened among those reporting recent offenses.Discussion: Acceptance of one’s sexual interests is associated with reduced distress in pedohebephilic disorder among non-offending individuals. However, its role among offending individuals remains unclear. Efforts to improve measuring the acceptance of one’s sexual interests and further explore its role in pedohebephilic disorder are warranted.
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16641078
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PSYCHOLOGY
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10.3389/fonc.2025.1500042
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Clinical analysis of different intestinal reconstruction methods after primary cytoreductive surgery combined with rectal resection for advanced ovarian cancer
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Objective: To compare different intestinal reconstruction methods after intestinal resection for advanced ovarian malignancy.Methods: Retrospective data of patients with advanced ovarian malignancy were collected and then assigned into three groups: primary intestinal anastomosis, protective enterostomy and colostomy. General clinical characteristics, intraoperative findings and postoperative outcomes were compared between the three groups.Results: A total of 530 cases were included for final analysis. The colostomy group had a lower serum albumin level, larger volume of ascites, higher likelihood of multiple intestinal resections and lower likelihood of rectal resection, lower peritoneal cancer index, more intraoperative blood loss, transfusions and infusions, lower likelihood of optimal cytoreductive surgery and shorter interval time to chemotherapy than the other two groups (p < 0.05). The primary intestinal anastomosis group exhibited a larger blood transfusion volume, higher incidence rates of anastomotic leak and electrolyte disturbance, and longer times to first flatus, first feeding and drain removal than the other two groups (p < 0.05).Conclusions: Colostomy can be adopted for advanced ovarian cancer patients with a large ascites volume, hypoproteinemia, large intraoperative blood and fluid loss volumes, multiple intestinal resections, anastomoses located below the peritoneal reflection, high PCI and suboptimal cytoreductive surgery. For patients with good intraoperative and postoperative outcomes, one anastomosis, an anastomosis located above the peritoneal reflection, low PCI or optimal cytoreductive surgery, intestinal anastomosis can be carried out to restore the normal physiological function of the intestine. For patients with a large volume of ascites (≥500 mL), multiple anastomoses or an anastomosis located below the peritoneal reflection, intestinal anastomosis combined with protective enterostomy has an advantage over intestinal anastomosis alone.
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2234943X
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ONCOLOGY
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10.1007/s00432-025-06094-0
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Bibliometric analysis of liposarcomas treatment from 2004 to 2023
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Background Liposarcomas are mesenchymal malignant tumors characterized by varying degrees of adipocytic differentiation that comprises approximately 20% of soft tissue sarcomas. Despite advancements in this field, there remains a need for a comprehensive understanding of the mechanisms, diagnosis, and treatment of liposarcomas. Currently, there is a lack of bibliometric surveys on the development trajectory of liposarcomas treatment, research hotspots, and author and team collaboration. Methods In this study, we obtained publications from the Web of Science database from 2004 to 2023, with a specific focus on the treatment of liposarcomas. By utilizing bibliometric methods, the data were processed to facilitate visual analysis of various aspects, including authors, countries, institutions, cocitations, keywords, references, and gene characteristics. Results The number of publications on liposarcomas treatment has increased over the past two decades, from 39 in 2004 to 232 in 2023, with the United States of America contributing the most publications. Among the institutions, the Memorial Sloan Kettering Cancer Center had the highest volume of 87 publications. Notably, Alessandro Gronchi published 63 articles on the treatment of liposarcomas in the last 20 years. Cancers is the journal with the highest number of 57 publications. High-frequency keywords in these publications included “soft tissue sarcoma”, “liposarcoma”, “retroperitoneal sarcoma”, “surgery”, “dedifferentiated liposarcoma”, “trabectedin” and “radiotherapy”. Recent trends, identified through strong citation bursts from 2020 to 2023, include next-generation sequencing, radiotherapy, and patient-derived cell lines. High-frequency genes in the liposarcomas treatment field include TP53, MDM2, CDK4, DDIT3, and CD274. Conclusions The treatment of liposarcomas has garnered increasing attention worldwide in the last 20 years. The treatment approach has shifted from surgical resection to multidisciplinary therapy. The molecular and biological characteristics of different tumor subtypes have attracted more research attention, providing an important reference for the choice of treatment. The findings of this study contribute to providing a comprehensive understanding of liposarcomas treatment among researchers. Moreover, they offer valuable perspectives that can guide future research.
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14321335
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ONCOLOGY
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10.1186/s40359-025-02411-x
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Psychometric properties of the Chinese version of the body image life disengagement questionnaire in a sample of adolescents
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The negative consequences of body image concerns manifest in ways such as negative emotional experiences, eating disorders, and problems with social life. The Body Image Life Disengagement Questionnaire (BILD-Q) is an instrument for assessing the impact of body image concerns specifically on adolescents’ life disengagement. The objective of this study is to create a Chinese version of the BILD-Q and assess its validity and reliability with Chinese adolescents. A total of 593 adolescents were recruited, of whom 316 (Sample 1) completed only the BILD-Q and 277 (Sample 2) completed the BILD-Q, Eating Attitudes Test (EAT), and Body Appreciation Scale-2 (BAS-2). Data from Sample 1 were used for the item analysis, exploratory factor analysis (EFA), and test-retest reliability, while data from Sample 2 were used for the BILD-Q’s confirmatory factor analysis (CFA) and associations of BILD-Q with EAT and BAS-2. Both samples were used together for calculating descriptive statistics, measurement invariance, and internal consistency. EFA and CFA were used to verify the single-factor structure of the BILD-Q. Measurement invariance across genders was verified by multi-group CFA. The reliability of the instrument was verified using Cronbach’s alpha and the intraclass correlation coefficient (ICC). Finally, the convergent validity of the instrument was verified by correlating the BILD-Q scores with the EAT and BAS-2 scores. The results support a single-factor structure for the Chinese version of the BILD-Q, with good reliability (Cronbach’s alpha = 0.888, ICC value = 0.759). Gender invariance was established: no significant differences were found in BILD-Q scores between the male and female groups. Life disengagement was positively correlated with eating disorder psychopathology and negatively correlated with body appreciation, supporting the convergent validity of the BILD-Q. The Chinese version of the BILD-Q has strong psychometric properties when used with Chinese adolescents and can be used to assess the impact of body image concerns on their life disengagement.
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20507283
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PSYCHOLOGY
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10.3389/fonc.2025.1482050
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Advances in the treatment of glioma-related signaling pathways and mechanisms by metformin
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Metformin (MET) is a commonly used drug for the treatment of type 2 diabetes in the department of endocrinology. In recent years, due to the few clinically effective treatment options including glioma, some scholars have proposed the possibility of metformin in the treatment of glioma, and studies have shown that metformin has a certain inhibitory effect on this tumor. This review explores the multiple mechanisms through which metformin exerts its antitumor effects, focusing on signaling pathways such as AMPK/mTOR, ferroptosis, autophagy, apoptosis and chloride ion channels (CLIC1). Metformin’s inhibition of glioma proliferation involves complex cellular processes, including mitochondrial dysfunction, increased reactive oxygen species (ROS) production, and modulation of immune responses. Additionally, metformin affects glioma stem cells by inhibiting key pathways, including STAT3, mTOR, and AKT, and altering the tumor microenvironment. While preclinical studies suggest that metformin enhances radiosensitivity and reduces tumor recurrence, its clinical application remains in early stages, with further studies needed to optimize dosing regimens and understand its full therapeutic potential. This review provides a comprehensive analysis of metformin’s molecular mechanisms in glioma treatment and highlights its potential as a novel therapeutic strategy, especially for treatment-resistant gliomas.
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2234943X
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ONCOLOGY
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10.3389/feduc.2024.1419362
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Improving learning experience through process re-engineering: Khan Academy localization into Azerbaijani
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The localization of online educational platforms brings many benefits to the students and teachers such as access to different types of textual and video content. Nonetheless, it demands time and capital resources to localize any content. The aim of this research was to re-engineer the entire localization process of Khan Academy content into the Azerbaijani language and evaluate its impact on users’ learning experience. For this purpose, we implemented process re-engineering’s cycle of successive steps. Additionally, we carried out a survey to investigate the new localization process’s effect on users’ learning experience. Our study found that making the localization process more efficient decreased the time and resources needed. Additionally, this improved process positively affected how users experienced learning on the platform.
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2504284X
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EDUCATION
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10.3389/fonc.2025.1497195
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Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms
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Among brain tumors, glioblastoma (GBM) is the most common and the most aggressive type, and brain metastases (BMs) occur in 20%–40% of cancer patients. Even with intensive treatment involving radiotherapy and surgery, which frequently leads to cognitive decline due to doses on healthy brain tissue, the median survival is 15 months for GBM and about 6 to 9 months for BM. Despite these treatments, GBM patients respond heterogeneously as do patients with BM. Following standard of care, some patients will respond and have an overall survival of more than 30 months and others will not respond and will die within a few months. Differentiating non-responders from responders as early as possible in order to tailor treatment in a personalized medicine fashion to optimize tumor control and preserve healthy brain tissue is the most pressing unmet therapeutic challenge. Innovative computer solutions recently emerged and could provide help to this challenge. This review will focus on 52 published research studies between 2013 and 2024 on (1) the early characterization of treatment efficacy with biomarker imaging and radiomic-based solutions, (2) predictive solutions with radiomic and artificial intelligence-based solutions, (3) interest in other biomarkers, and (4) the importance of the prediction of new treatment modalities’ efficacy.
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2234943X
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ONCOLOGY
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10.1007/s00432-025-06101-4
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Biomarker microRNA-371a-3p - expression in malignancies other than germ-cell tumours
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Purpose: microRNA-371a-3p (M371) is considered a highly sensitive and specific serum biomarker of testicular germ cell tumours (GCTs). However, little is known about the expression of M371 in nontesticular malignancies (NTMs), so far. As knowledge about the expression of the marker in other malignancies is a prerequisite for the clinical application of the test we aimed to explore the M371 expression in other cancers. Methods: M371 serum levels were measured in 137 patients with NTM of 12 different neoplastic entities using the IVDR-certified M371-Test for quantitative real-time PCR. Median M371 serum levels and percentages of M371 level elevations were calculated for the entire NTM group and for entity-specific subgroups. The results were compared with GCT patients (n = 20) and with tumour-free male controls (n = 20) using descriptive statistical methods. Results: Eight patients with NTMs had M371 serum level elevations, corresponding to a false-positive rate (FPR) of 5.84% (95% confidence intervals (CIs) 2.55–11.18%). Expression rates in GCTs and controls were 100% and zero, respectively. Thus, the specificity of the M371-Test for GCT is 94.90% (95% CI 90.21–97.77%) when all NTMs and tumour-free controls are considered. Remarkably, three out of 5 patients with multiple myeloma had elevated M371 levels. Conclusion: The false-positive rate of the M371-Test in other malignancies than GCT is very low, and almost identical with that in healthy males, corresponding to a high specificity of 94.9% for detection of GCT. The surprising finding of M371 elevations in patients with multiple myeloma needs further investigation.
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14321335
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ONCOLOGY
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10.3389/frai.2025.1455341
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Artificial intelligence applied to diabetes complications: a bibliometric analysis
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Background and aims: Artificial intelligence (AI)-driven medical assistive technology has been widely used in the diagnosis, treatment and prognosis of diabetes complications. Here we conduct a bibliometric analysis of scientific articles in the field of AI in diabetes complications to explore current research trends and cutting-edge hotspots.Methodology: On April 20, 2024, we collected and screened relevant articles published from 1988 to 2024 from PubMed. Based on bibliometric tools such as CiteSpace, Vosviewer and bibliometix, we construct knowledge maps to visualize literature information, including annual scientific production, authors, countries, institutions, journals, keywords and research hotspots.Results: A total of 935 articles meeting the criteria were collected and analyzed. The number of annual publications showed an upward trend. Raman, Rajiv published the most articles, and Webster, Dale R had the highest collaboration frequency. The United States, China, and India were the most productive countries. Scientific Reports was the journal with the most publications. The three most frequent diabetes complications were diabetic retinopathy, diabetic nephropathy, and diabetic foot. Machine learning, diabetic retinopathy, screening, deep learning, and diabetic foot are still being researched in 2024.Conclusion: Global AI research on diabetes complications is expected to increase further. The investigation of AI in diabetic retinopathy and diabetic foot will be the focus of research in the future.
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26248212
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AI
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10.3389/feduc.2025.1518917
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Australia's progress toward SDG4 targets for school-age students with disability
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Australia's progress toward achieving Sustainable Development Goal 4 (SDG4) for students with disability reveals both challenges and opportunities. Despite existing disability discrimination legislation, systemic barriers persist within government and non-government schooling sectors. A lack of a coordinated national strategy, combined with fragmented policies, has constrained efforts to promote inclusive education, leaving students with disability underserved, particularly in regional, rural, and remote areas. Underinvestment in mainstream schools has also created disparities in educational access and quality. Moreover, inadequate training of classroom teachers in these schools has continued to restrict the implementation of inclusive and individualized approaches, limiting educational outcomes for this student group. These students therefore continue to experience lower success and completion rates than their peers. This paper emphasizes the urgent need for systemic reforms, including targeted investments and a national policy framework aligned with SDG4, to address these issues. We argue that to achieve equitable, inclusive, and quality education for all students, collaborative effort across all levels of government and education sectors is required for Australia to realize sustainable progress toward its international commitments.
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2504284X
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EDUCATION
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10.1186/s40594-025-00530-w
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Retention in engineering pathways: an ecological belonging intervention supports help-seeking and continued enrollment
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Background: The demand for engineers in the workforce continues to rise, which requires increased retention and degree completion at the undergraduate level. Engineering educators need to better understand opportunities to retain students in engineering majors. A strong sense of belonging in engineering represents one important contributor to persistence. However, research has not investigated how academic help-seeking behaviors relate to belonging and downstream outcomes, such as persistence in engineering. Interventions to support and develop belonging show promise in increasing student retention, with particularly positive influences on women, Black, Latino/a/x, and indigenous students. As part of a larger research project, a quasi-experimental intervention to develop a classroom ecology of belonging was conducted at a large Midwestern university in a required first-year, second-semester engineering programming course. The 45-min intervention presented students with stories from past students and peers to normalize academic challenges within the ecology of the classroom as typical and surmountable with perseverance, time, and effort. Results: With treatment (n = 737) and control (n = 689) participant responses, we investigated how the intervention condition affected students' comfort with seeking academic help and feeling safe being wrong in class as influences on belonging. Using path analysis, a form of structural equation modeling, we measured the influence of these attitudinal variables on belonging and the influence of belonging beyond a student’s grade point average on enrollment as an engineering major the following fall. The path analysis supports the importance of academic help-seeking and feeling safe to be wrong for belonging, as well as the importance of belonging on continued enrollment. A group path analysis compared the treatment and control groups and demonstrated the positive impact of the intervention on enrollment for the treatment participants. Conclusions: The analyses demonstrate the importance of academic help-seeking in students’ sense of belonging in the classroom with implications for identifying effective tools to improve students’ sense of belonging through supporting help-seeking behaviors.
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21967822
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EDUCATION
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10.1007/s00432-025-06104-1
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Individual management and prognostic assessment for long-term outcomes using a novel classification system of craniopharyngiomas: a retrospective study of single institution
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Purpose This study aims to propose a classification system to more accurately understand the features and nature of different CPs, to investigate the correlation between different topographies of CPs and their surgical outcomes. Methods A retrospective analysis was conducted on 91 surgically resected CPs. They were categorized into six types based on their location and origin. Simultaneously, the patients were divided into four categories based on the degree of pituitary stalk(PS) preservation postoperatively. Statistical analysis was performed to compare the variables among the different tumor type groups. Results A total of 91 patients were included. The follow-up data for 59 cases were complete. Tumor volume varied significantly, with the suprasellar-third ventricle type II and ectopic type exhibiting larger volumes (P < 0.05). The choice of surgical approach differed significantly. The recurrence rates were significantly lower for the intrasellar-suprasellar type, suprasellar-third ventricle type II, and third ventricle type (P < 0.05). Patients with intra-stalk tumor growing pattern have a lower degree of PS preservation than those with peri-stalk pattern (P < 0.05). Patients’ BMI after surgery was generally higher than before, and the incidence of pituitary dysfunction increased significantly. The proportion of long-term endocrine dysfunction was significantly higher in patients with complete disconnection of PS compared to those with preservation of the PS(P < 0.05). Conclusions This system holds significant importance in foretelling the rates of recurrence, alterations in postoperative body weight, long-term endocrine status, and potential complications. Furthermore, this study identified preoperative pituitary function status and specific surgical approaches as potential protective factors.
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14321335
|
ONCOLOGY
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10.3389/frai.2025.1446590
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Strategic technological innovation through ChatMu: transforming information accessibility in Muhammadiyah
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This study examines the effectiveness of the ChatMu application in improving access to information for members of Muhammadiyah, a prominent socio-religious organization. The research employs a mixed-methods approach, combining qualitative and quantitative analyses to evaluate the application’s performance, usability, and user satisfaction. Findings reveal that ChatMu significantly enhances the accessibility and accuracy of Muhammadiyah-related information, highlighting its potential as an innovative tool for addressing community-specific information needs. However, several usability challenges were identified, including navigation inefficiencies and inconsistencies in content delivery. These limitations suggest the need for further refinement to optimize user experience and functionality. Despite these issues, ChatMu demonstrates strong capabilities in providing relevant and reliable information, fostering digital literacy, and supporting information dissemination within the Muhammadiyah community. The study concludes that ChatMu represents a promising application of chatbot technology in empowering communities through improved access to knowledge. Future development efforts should focus on comprehensive usability testing, maintaining information relevance, and incorporating advanced interactive features to enhance engagement. With continuous improvements, ChatMu has the potential to become an effective medium for advancing literacy and knowledge-sharing in the Muhammadiyah community.
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26248212
|
AI
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10.1007/s44196-025-00741-7
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An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data
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One of the widening perils in network security is the Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) ecosystem. This paper presents an enhanced Intrusion Detection System (IDS) through the proposal of an enhanced version of the long short-term memory (LSTM) model to detect DDoS attacks using honeypot-generated data. The proposed model aggregates the Conv1D, Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Gated Recurrent Unit (Bi-GRU), and dropout layers to extract temporal and spatial features from IoT traffic effectively. We tested the efficacy of the proposed system on a real-world IoT-DH dataset, which showed a remarkable accuracy of 99.41%, with an AUC score of 0.9999. A comparative analysis with other baseline models, such as LSTM, Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feedforward Neural Network (FNN), and Temporal Convolutional Network (TCN), proved that enhanced LSTM outperformed the other models. This indicates the robustness of the proposed model in correctly detecting DDoS attacks with high generalization capability for unseen traffic data. The contribution of this paper will be an addition to the deep learning techniques applied for the solution of intrusion detection systems (IDS), which will also allow the building and implementation of more efficient security mechanisms in IoT environments.
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18756883
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AI
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10.3389/feduc.2025.1427083
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Adaptive learning in bionics: transforming science education
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Introduction: Adaptive learning platforms offer innovative teaching approaches by tailoring educational content to individual learner’s needs, abilities, and paces.Methods: This study investigates the effects of an adaptive digital learning platform on user experience, motivation, and learning outcomes among 56 sixth-grade students from two German grammar schools. Students completed three bionics-focused modules— “polar bear”, “heat transfer”, and “temperature and heat”—integrated into science lessons. Data from questionnaires and performance tests assessed prior knowledge, learning success, cognitive activation, and situational interest.Results: The findings indicate that 98% of students found digital media motivating, with 81% favoring a hybrid mix of traditional and digital teaching methods. Positive emotional responses were reported by 62% of participants, though 38% experienced uncertainty. The “polar bear” module achieved the highest learning gain (+41%), followed by “heat transfer” (+23%) and “temperature and heat” (+13%) module.Discussion: These results suggest that adaptive digital learning platforms can enhance learning outcomes and cognitive engagement, particularly when the content aligns with student interests and needs.
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2504284X
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EDUCATION
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10.1007/s00432-025-06107-y
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Psychosocial distress, perceived need and utilization of psycho- social support services in patients in the early phase after the first cancer diagnosis
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Purpose: Due to the growing number of new oncological diagnosis and the accompanying psychosocial burden, needs-based psycho-oncological care is important. Adequate planning of psycho-oncological support services is therefore becoming increasingly important. In order to better implement psycho-oncological support services, we investigate psychosocial distress, perceived need and utilization of psycho-oncological support offers in newly diagnosed cancer patients. Methods: Based on a multicenter prospective study, we assessed the cross-sectional data on psychosocial distress, perceived need and utilization of psycho- social support in patients with different tumor entities within 2 months after initial diagnosis. Psychosocial distress was assessed using the Distress Thermometer (DT). Results: Of 1,003 eligible patients who completed the questionnaire (53.0% men, mean age 60.3 years) 39.7% (n = 390) showed above-threshold psychosocial stress (DT: scores ≥ 5) and 21% (n = 207) indicated a perceived need for psycho- social support. 13.5% (n = 136) showed both, psychosocial distress and perceived need for psycho- social support. 15.2% (n = 150) out of all participating patients used psycho-oncology service, 60.7% (n = 597) were willing to accept such an offer. Women were significantly more likely to be psychosocially distressed and to express a need for support. They were also significantly more likely to seek and be willing to accept psycho- social support. Conclusion: Although most patients would accept a psycho- social service, regardless of whether there is psychosocial distress or a need is perceived, the actual utilization was relatively low. It can therefore be assumed that barriers, e.g. structural or personal ones, prevent access. These should be investigated in more detail in future studies.
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14321335
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ONCOLOGY
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10.3389/frai.2025.1398885
|
From Llama to language: prompt-engineering allows general-purpose artificial intelligence to rate narratives like expert psychologists
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Introduction: Artificial intelligence (AI) has tremendous potential for use in psychology. Among the many applications that may benefit from development of AI applications is narrative-personality assessment. Use of these tools and research methods is notably time-consuming and resource intensive. AI has potential to address these issues in ways that would greatly reduce clinician and researcher burden. Nonetheless, it is unclear if current AI models are sufficiently sophisticated to perform the complex downstream tasks, such as narrative assessment.Methodology: The purpose of this study is to explore if an expert-refined prompt generation process can enable AI-empowered chatbots to reliably and accurately rate narratives using the Social Cognition and Object Relations scales – Global Rating Method (SCORS-G). Experts generated prompt inputs by engaging in a detailed review of SCORS-G training materials. Prompts were then improved using an systematic process in which experts worked with Llama-2-70b to refine prompts. The utility of the prompts was then tested on two AI-empowered chatbots, ChatGPT-4 (OpenAI, 2023) and CLAUDE-2-100k, that were not used in the prompt refinement process.Results: Results showed that the refined prompts allowed chatbots to reliably rate narratives at the global level, though accuracy varied across subscales. Averaging ratings from two chatbots notably improved reliability for the global score and all subscale scores. Experimentation indicated that expert-refined prompts outperformed basic prompts regarding interrater reliability and absolute agreement with gold standard ratings. Only the expert-refined prompts were able to generate acceptable single-rater interrater reliability estimates.Discussion: Findings suggest that AI could significantly reduce the time and resource burdens on clinicians and researchers using narrative rating systems like the SCORS-G. Limitations and implications for future research are discussed.
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26248212
|
AI
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10.3389/fonc.2025.1514009
|
Biomarkers of inflammation and colorectal cancer risk
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Globally, colorectal malignancy ranks among the most prevalent forms of cancer and stands as the third principal cause of cancer-associated mortality. Recent studies indicate that inflammatory processes play a significant role in the initiation and advancement of various malignancies, colorectal cancer included. It explores inflammatory biomarkers, with C-reactive protein (CRP) being a key focus. While CRP’s elevation during inflammation is linked to tumorigenesis, studies on its association with CRC risk are inconsistent, showing gender and methodological differences. Interleukin-6 (IL-6), TNF - α, and their receptors also play roles in CRC development, yet research findings vary. Adiponectin and leptin, secreted by adipocytes, have complex associations with CRC, with gender disparities noted. In terms of screening, non-invasive methods like fecal occult blood tests (FOBTs) are widely used, and combining biomarkers with iFOBT shows potential. Multi-omics techniques, including genomics and microbiomics, offer new avenues for CRC diagnosis. Overall, while evidence highlights the significance of inflammatory biomarkers in CRC risk prediction, larger prospective studies are urgently needed to clarify their roles due to existing inconsistencies and methodological limitations.
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2234943X
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ONCOLOGY
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10.1186/s40594-024-00524-0
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Facilitation of students’ disembedding in an online visual arts and mathematics education program
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Disembedding is a crucial spatial thinking skill in visual arts and mathematics education. It is important in creating and analyzing artworks by separating a figure from its background, as well as for solving geometric problems where shapes must be viewed from new perspectives. Drawing upon research in psychology, arts, and mathematics education, the present study aimed to facilitate students’ disembedding in an online educational program employing the teaching experiment methodology. This program utilized concrete movement artworks, particularly those by Max Bill. Seven sixth-grade students participated remotely in this program, utilizing GeoGebra Classroom. The analysis of video data (talks and drawings) and written notes over three sessions revealed that this online educational program, which was designed for the specific context of visual arts and mathematics, offered students opportunities for the individual and group observation of diverse artworks, the tracing of shape contours, and guided attention to new perceptual organizations of shapes through prompting questions. Overall, this had the potential to facilitate students’ disembedding. This overall process challenged students’ initial simplistic shape organizations based on Gestalt principles, leading to the identification of primary and secondary structures, as well as reversible figures. This research sheds light on the concept of disembedding skills rooted in Gestalt psychology, and its connection to the figure-ground phenomenon observed in both artistic and mathematical contexts. This research offers theoretical and practical contributions. First, it suggests an emerging trajectory of disembedding and proposes methods for nurturing students’ disembedding skills. Second, this study serves as an example for art and mathematics educators in schools and informal learning environments (e.g., art museums) to support students’ spatial thinking. This study contributes to the development of educational programs that facilitate students’ spatial thinking in the context of STEAM (Science, Technology, Engineering, Arts, and Mathematics) education.
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21967822
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EDUCATION
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10.3389/feduc.2025.1523797
|
A systematic review of the utility of assistive technologies for SEND students in schools
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The systematic review investigates the effect of various educational technologies on the learning outcomes of diverse student populations, particularly focusing on assistive technology interventions for students with disabilities. The comprehensive analysis covers literature from 2012 to 2023. The study highlights the potential of AR and assistive technologies in fostering inclusive and engaging learning environments. Despite positive findings, the review emphasizes the imperative for further research to refine the implementation of these technologies and enhance their effectiveness. The systematic review of five databases provides crucial insights into the effectiveness of various assistive technologies. Mobile devices, iPads, and AR interventions emerge as frequently utilized tools. Research activity peaked in 2013 and 2018 and subsequently declined. Twelve studies focus on Autism Spectrum Disorder and emphasize the prioritization of ASD in assistive technology interventions. The research highlights the importance of adopting a holistic perspective on educational inclusion, emphasizing collaborative efforts among teachers, diverse teaching methods, and technology integration. Despite the promise shown by assistive technologies, the review acknowledges their limitations and advocates for ongoing research and innovation to refine their application across diverse educational contexts. The findings stress the importance of a nuanced interpretation of evidence, considering the challenges posed by the limited number of eligible studies. The review calls for careful consideration of future research directions to bolster the comprehensiveness and reliability of evidence synthesis in assistive technology interventions for students with disabilities.
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2504284X
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EDUCATION
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10.3390/ai6020040
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Deep Learning and Reinforcement Learning for Assessing and Enhancing Academic Performance in University Students: A Scoping Review
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University students often face challenges in managing academic demands and difficulties like time management, task prioritization, and effective study strategies. This scoping review investigates the application of Deep Learning (DL) and Reinforcement Learning (RL) in evaluating and enhancing academic performance, focusing on their practical applications, limitations, and future potential. Using PRISMA guidelines, 27 empirical studies published between 2014 and 2024 were analyzed. These studies utilized advanced DL and RL technologies, including neural networks and adaptive algorithms, to support personalized learning and performance prediction across diverse university contexts. Key findings highlight DL’s ability to accurately predict academic outcomes and identify at-risk students, with models achieving high accuracy in areas like dropout prediction and language proficiency assessments. RL proved effective in optimizing learning pathways and tailoring interventions, dynamically adapting to individual student needs. The review emphasizes significant improvements in grades, engagement, and learning efficiency enabled by AI-driven systems. However, challenges persist, including scalability, resource demands, and the need for transparent and interpretable models. Future research could focus on diverse datasets, multimodal inputs, and long-term evaluations to enhance the applicability of these technologies. By integrating DL and RL, higher education can foster personalized, adaptive learning environments, improving academic outcomes and inclusivity.
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26732688
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AI
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10.3389/frai.2025.1523390
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Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning
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Intensity-Modulated Radiation Therapy requires the manual adjustment to numerous treatment plan parameters (TPPs) through a trial-and-error process to deliver precise radiation doses to the target while minimizing exposure to surrounding healthy tissues. The goal is to achieve a dose distribution that adheres to a prescribed plan tailored to each patient. Developing an automated approach to optimize patient-specific prescriptions is valuable in scenarios where trade-off selection is uncertain and varies among patients. This study presents a proof-of-concept artificial intelligence (AI) system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to guide IMRT planning and achieve optimal, patient-specific prescriptions in aligned with a radiation oncologist's treatment objectives. We developed an in-house ANFIS-AI system utilizing Prescription Dose (PD) constraints to guide the optimization process toward achievable prescriptions. Mimicking human planning behavior, the AI system adjusts TPPs, represented as dose-volume constraints, to meet the prescribed dose goals. This process is informed by a Fuzzy Inference System (FIS) that incorporates prior knowledge from experienced planners, captured through “if-then” rules based on routine planning adjustments. The innovative aspect of our research lies in employing ANFIS's adaptive network to fine-tune the FIS components (membership functions and rule strengths), thereby enhancing the accuracy of the system. Once calibrated, the AI system modifies TPPs for each patient, progressing through acceptable prescription levels, from restrictive to clinically allowable. The system evaluates dosimetric parameters and compares dose distributions, dose-volume histograms, and dosimetric statistics between the conventional FIS and ANFIS. Results demonstrate that ANFIS consistently met dosimetric goals, outperforming FIS with a 0.7% improvement in mean dose conformity for the planning target volume (PTV) and a 28% reduction in mean dose exposure for organs at risk (OARs) in a C-Shape phantom. In a mock prostate phantom, ANFIS reduced the mean dose by 17.4% for the rectum and by 14.1% for the bladder. These findings highlight ANFIS's potential for efficient, accurate IMRT planning and its integration into clinical workflows.
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26248212
|
AI
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10.3389/feduc.2025.1528924
|
Conceptual model of sustainable development of pedagogical staff competences in quality assurance of higher education
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Creating strategies to improve the quality of higher education is the key task of higher education institutions in the conditions of rapid changes in the content of competencies, in particular IT-competencies, as well as in the conditions of turbulence in the external environment, in particular epidemics, military conflicts, etc. The article describes the conceptual model of sustainable development of teachers’ competences in ensuring the quality of higher education in higher education institutions. The sustainability of the development of teachers’ methodological competences is ensured by monitoring their activities based on the results of professional development and acquisition of the appropriate level of competence, as well as on the results of teaching by these teachers of relevant educational components in academic groups of higher education students. In order to reduce the risks of failing to pass the course, some participants of the competence project proposed to form a pool of potential project participants. Methods of optimization theory were used to solve the problem of selecting participants in the competence project. Also, the methodology of project management and pedagogical modeling was used to form the structure of the competence project and to plan its implementation. The assessment of methodological competences of university lecturers is crucial for building sustainable inter-university scientific and educational communities. Based on the results of the pilot implementation of the first stage of the competence project, project participants were selected on the basis of this conceptual model and training was organized to improve the level of competence in the field of education quality management. The results of training of teachers according to the described conceptual model allow to increase the level of their methodological competence. The obtained result requires clarification after completing the second stage of the competency-based project. Thus, the authors proposed an innovative approach to improving the level of competence of teachers of higher education institutions, which is focused on the effective assimilation of learning outcomes not only directly by teachers, but also by students of academic groups in which these teachers teach.
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2504284X
|
EDUCATION
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10.3389/feduc.2025.1512557
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Engagement factors affect academic success through study approaches among physical education and sport university students: a mediation analysis
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Introduction: University students should engage with the study and ensure they adopt productive study approaches, but the nature of relationships between engagement and study approaches are under-researched. The study aimed to investigate how emotional, cognitive, and behavioral engagement affect academic success through study approaches among physical education and sports students.Methods: Online forms were submitted by 488 students in physical education and sports (age range 19–25 years, Mean = 21 ± 1.5 year). They completed surveys regarding their academic engagement, study approaches, and grade point average (GPA). Analyses of associations were conducted through linear regression analysis and mediation analysis.Results: Results from the linear regression analysis showed correlations between academic engagement factors, study approach variables, and GPA, with higher GPA correlating with higher scores on behavioral engagement, cognitive engagement, surface theory task, and deep theory task, and with lower scores on surface practical task. The analysis of total and direct effects revealed positive associations between all academic engagement factors and GPA. Emotional engagement exhibited a positive association with GPA mediated by study approaches. All engagement dimensions appear to influence academic success among these students.Conclusion: The influence of emotional engagement on academic success appears in part to be operating through its effects on study approaches. The study can enable educators in monitoring and enhancing student engagement, thereby supporting students in their pursuit of high academic performance in physical education and sport.
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2504284X
|
EDUCATION
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10.3390/cancers17040705
|
Correlation of GNAS Mutational Status with Oncologic Outcomes in Patients with Resected Intraductal Papillary Mucinous Neoplasms
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Background: Intraductal papillary mucinous neoplasms (IPMNs) are pre-malignant pancreatic lesions that may progress to invasive pancreatic ductal adenocarcinoma (PDAC). IPMN-associated invasive carcinoma (iIPMN) has been associated with more favorable survival outcomes compared to non-iIPMN-derived PDAC. Here, we aim to investigate the genetic landscape of IPMNs to assess their relevance to oncologic outcomes. Methods: This retrospective study used a large single-institution prospectively maintained database. Patients who underwent curative-intent pancreatic resection between 2016 and 2022 with histologically confirmed diagnosis of IPMN were included. Demographic, pathologic, molecular, and oncologic outcome data were recorded. Kaplan–Meier survival analyses were performed. PDAC data from public genetic databases were used for mutational correlation analysis. p-value ≤ 0.05 was considered as significant. Results: A total of thirty-nine patients with resected IPMN with complete clinical and sequencing data were identified and included in the final cohort. The male-to-female distribution was 21:18, and the mean age was 70.1 ± 9.1 years. GNAS mutations occurred in 23.1% of patients, and 89.7% of patients had iIPMN. In iIPMN patients, GNAS mutation was strongly associated with improved disease-free survival: all GNAS-mutant patients survived to follow-up with significantly fewer recurrences than in GNAS wild-type (WT) patients (p = 0.013). Mutated GNAS closely co-occurred with wild-type KRAS (p < 0.001), and further analysis of large genomic PDAC datasets validated this finding (OR 3.47, p < 0.0001). Conclusions: Our study suggests prognostic value of mutational status in malignant resected IPMNs. WT GNAS, mutant P53, and mutant KRAS each correlate with recurrence and decreased survival. Further studies are required to validate these preliminary observations.
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20726694
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ONCOLOGY
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10.1186/s40594-025-00532-8
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Quantifying ento-literacy: development and validation of an international insect-focused attitude and knowledge survey instrument
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Background: In an era of precipitous insect declines, effective entomology education is especially needed to support firsthand knowledge of nature. Understanding what students know and feel about insects is instrumental to teaching and curriculum development. This study describes the development and validation of a new survey instrument, EntoEdu, measuring ‘entomology literacy’, based on attitude and knowledge, in a cross-cultural context. For the survey validation we use data collected from students in Czechia (CZ), a country known for its entomophilia, and the United States of America (US) to demonstrate the utility of this survey and to address the questions: how do entomology attitude and knowledge differ across national affiliation and study domain, and how are entomology attitude and knowledge correlated in the context of these differences? Results: Based on responses from 635 first-year college students, we demonstrate high reliability and evidence of validity of the EntoEdu instrument. Factor analysis supports five independent attitudinal categories within the instrument: Intention to Engage with Insects, Attitude toward Behavior, Control Belief, Hobby, and Disgust. In this study population, average attitude scores did not differ with nationality, whereas knowledge scores were higher in CZ than in the US. In both countries, attitude and knowledge scores were higher among biology students than those in other study domains, and attitude and knowledge were positively correlated. Conclusions: The EntoEdu instrument, based on globally recognizable insect taxa, ecology, and behavior, has been developed for broad utility in assessing attitudes toward and knowledge of insects at the post-secondary level, with potential for use at both lower (K-12) and higher (advanced university) levels. The instrument is presented here in two language versions and can be translated into additional languages for comparison of results across test populations in additional countries. In our initial test population we find attitude and knowledge to be correlated, both of which are influenced by nationality, with Czechs more knowledgeable about insects than their US counterparts. We anticipate that this instrument will facilitate entomology assessment to help tailor biology education programs to students’ actual, rather than assumed, entomology knowledge and attitudes, and for tracking change over time.
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21967822
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EDUCATION
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10.3389/fpsyg.2025.1522098
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Perceived restorativeness and environment quality in relation to well-being, residential satisfaction, and sense of community: an analysis in Northeast Italy
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Introduction: Residential satisfaction consists of pleasure derived from living in a place according to one’s needs, expectations, and outcomes. The present study examines the role of sociodemographic variables, perceived residential quality indicators, and restorativeness in predicting i) well-being, ii) residential satisfaction, and iii) sense of communities in northeast Italy.Methods: A total of 100 residents (47 women) in various cities in northeast Italy and 211 (112 women) residents in Piazzola sul Brenta (PD) took part in two studies. They answered demographic questions about self-reported restorativeness, residential environment quality, residential satisfaction, mental well-being, and sense of community.Results: After accounting for age, gender, and income, the results showed that perceived restorativeness enhances sense of community in the Northeast and Piazzola sul Brenta samples and predicts psychological well-being and residential satisfaction in Piazzola sul Brenta. Architectural and functional aspects contribute, respectively, to residential satisfaction and sense of community in both samples, and functional factors predict residential satisfaction for the Northeast sample. Place attachment plays a positive role in residential satisfaction and sense of community in the Northeast and Piazzola sul Brenta.Discussion: The study reveals a link between perceived restorativeness and residential satisfaction and well-being, providing insight for professionals and policy to improve urban quality.
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16641078
|
PSYCHOLOGY
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10.1186/s40359-025-02477-7
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Parental psychological control and adolescent smartphone addiction: roles of reactance and resilience
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Problematic smartphone use is a prevalent issue addressed in this study. The research delves into factors associated with problematic smartphone use, employing the self-determination theory. Specifically, the study analyzes the relationship between parental psychological control and problematic smartphone use and investigates psychological reactance as a mediating factor. Moreover, psychological resilience is considered a moderating factor in the relationship between parental psychological control and problematic smartphone use, based on the diathesis-stress model and cognitive model of resilience. A total of 1300 (M = 14.22, SD = 1.29) Chinese adolescents were surveyed in a cross-sectional study. They completed self-report questionnaires including the Parental Psychological Control Questionnaire, the Smartphone Addiction Scale, the Psychological Resistance Scale, and the Adolescent Resilience Scale. A moderated mediation model was examined to test predictions. Correlation analysis reveals a positive correlation between parental psychological control, psychological reactance, and problematic smartphone use, and a negative correlation with psychological resilience. Moderation mediation analysis demonstrates that psychological resilience diminishes the direct association between parental psychological control, psychological reactance, and problematic smartphone use, thereby mitigating their relationship. The findings support the moderation mediation model, indicating that psychological resilience plays a crucial role in safeguarding adolescents from the adverse effects of problematic smartphone use induced by parental psychological control.
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20507283
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PSYCHOLOGY
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10.1186/s40594-025-00533-7
|
A decade of research contributions and emerging trends in the International Journal of STEM Education
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In this editorial, we review 400 articles and reviews published in the International Journal of STEM Education during its first decade (2014–2023). Using bibliometric analysis, we examine these publications to assess the journal’s major contributions to STEM education research and identify emerging trends over the years. The results present a dynamic picture of the growth of STEM education, highlighting key topics, such as STEM integration, equity, and emerging technologies. These findings also reveal evolving “hot topics” that reflect the shifting interests of researchers in the field. This review suggests that many areas of STEM education research are still in the growth phase. We encourage readers to use these insights as a foundation for developing future research agendas and advancing STEM education globally.
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21967822
|
EDUCATION
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10.3389/feduc.2025.1467482
|
Research on task design in pre-service mathematics teacher education: a scoping review
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Tasks are central to every facet of mathematics education. In this scoping review, we bring together research focused on task design in the context of pre-service mathematics teacher education. We perform a qualitative content analysis of 112 peer-reviewed studies published between 2001 and 2023. The results of our review describe a diverse field of research, identify connections between works reflecting different demographics, aims, and methodological and theoretical commitments, and finally, through the application of the MEDSS task design action framework, foreground the different practical actions pre-service teaches must take to effectively design tasks. These include the practical actions of modifying, evaluating, developing, selecting, and sequencing (MEDSS) tasks. We believe the results of this study will be of value to mathematics teacher educators and researchers.
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2504284X
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EDUCATION
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10.3389/feduc.2025.1473331
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Continuing education of academic women in STEM: perspectives on mentoring and professional roles
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Despite ongoing efforts towards gender equity, the gender gap in STEM (Science, Technology, Engineering, and Mathematics) remains significant today. This article explores the motivations and perceptions of women in different professional roles within STEM fields regarding the importance of mentoring in fostering interest and participation in STEM careers, thus contributing to continuing engineering education. Based on qualitative data from 19 semi-structured interviews with women in managerial, research, teaching, and external academic and professional roles, the study delves into their motivations for pursuing STEM careers, their interest in promoting diversity, and the role of mentoring in supporting their professional development. The thematic analysis results are grouped into a hierarchical structure comprising one meta-theme, four primary, and six subthemes. The participants emphasized that their primary motivation for STEM involvement was contributing to society and promoting economic growth. Additionally, they advocated for greater diversity and challenged traditional gender roles in these areas. The participants highlighted the importance of closing the gender gap and recognizing the capabilities and new perspectives that women brought. Although these women faced obstacles such as glass ceilings, having a mentorship opportunity was identified as a critical tool for women’s empowerment and training. The insights contribute to advancing strategies for promoting gender equity and diversity in STEM fields, with implications for researchers, universities, and organizations seeking to support women’s participation and advancement in STEM careers. Further research is recommended to explore the perspectives of women in other roles and the effectiveness of mentoring programs in fostering gender diversity in STEM.
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2504284X
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EDUCATION
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10.3389/frai.2024.1510410
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Environment sustainability with smart grid sensor
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Environmental sustainability is a pressing global concern, with energy conservation and efficient utilization playing a key role in its achievement. Smart grid technology has emerged as a promising solution, facilitating energy efficiency, promoting renewable energy integration, and fostering consumer engagement. But the addition of intelligent sensors to these grids has the potential to greatly increase the level of sustainability initiatives. This paper highlights the role of smart grid sensors in addressing challenges like energy losses, demand-response limitations, and renewable energy integration. It explains how these sensors enable real-time monitoring, fault detection, and optimal load management to improve grid performance and reduce environmental impact. This also study looks at how AI with smart grid sensor can perform real-time data monitoring, optimal energy distribution, and proactive decision support from smart grid sensors might improve environmental sustainability. Furthermore, it examines advancements in sensor technologies in India, including pilot projects like the BESCOM initiative in Bangalore and Tata Power-DDL’s renewable energy trading in Delhi, to showcase their practical applications and outcomes. Smart sensors provide accurate tracking of energy usage trends, enhance load distribution, and advance the sensible application of renewable energy resources. These sensors aid in cutting down on energy waste and carbon emissions by interacting with customers and enabling demand-response systems. This study addresses the critical role of smart sensors in overcoming the shortcomings of conventional grids and guaranteeing a more resilient, efficient, and sustainable energy future through an extensive analysis of the literature. Grid-enabled systems, such as electric water heaters with sensor, can achieve energy savings of up to 29%. The integration of renewable energy sources through sensors enhances system efficiency, reduces reliance on fossil fuels, and optimizes supply and demand. Utilizing Internet of Things (IoT) technology enables precise monitoring of air quality, water consumption, and resource management, significantly improving environmental oversight. This integration can lead to a reduction in greenhouse gas emissions by up to 20% and water usage by 30%. Lastly, the paper discusses how integrating artificial intelligence with smart grid sensors can enhance predictive maintenance, energy management, and cybersecurity, further strengthening the case for their deployment.
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26248212
|
AI
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10.1186/s40359-025-02441-5
|
Validation of the Arabic version of the Gratitude Questionnaire (GQ-4) in a sample of non-clinical adults
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Although gratitude is a culturally-sensitive construct, it has yet received limited research attention in Arab countries, hence hindering the understanding of its features, correlates, and cross-cultural specificities. To fill this gap, we sought to examine the psychometric properties of an Arabic translation of the 6-item Gratitude Questionnaire (GQ) in an Arabic-speaking sample of adults from the general population of Lebanon. We conducted a web-based survey including 601 participants (mean age 29.91 ± 12.61, 62.7% females). The forward-backward translation method was used for the translation and adaptation of the GQ-6 into the Arabic language. Findings indicated that a four-item version of the GQ achieved adequate fit statistics with the removal of the two reverse-scored third and sixth items. We found a McDonald Omega coefficient for the total 4-item GQ (GQ-4) scores of 0.88, thus attesting for the good reliability of the scale. Multiple-group Confirmatory Factor Analysis showed that the scale structure was invariant across male and female respondents at the configural, metric, and scalar levels. Females exhibited significantly higher gratitude scores compared to males. Finally, discriminant validity of the Arabic GQ-4 was evidenced through positive significant correlations with social support levels. The Arabic adaptation of the GQ showed good psychometric qualities, suggesting that it is suitable for measuring people’s disposition toward gratitude in Arab backgrounds. Offering the Arabic GQ-4 as a brief, simple, cost-effective, valid, and reliable measure of gratitude to the Arabic-speaking community could help raise awareness about gratitude as a key component for achieving good mental health and wellbeing in Arab contexts.
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20507283
|
PSYCHOLOGY
|
10.3390/educsci15030268
|
Building Community Among K-8 Teachers Through a University-Educator Network Partnership
|
At this time of national divisiveness in the U.S., it is more important than ever for youth to have teachers who can facilitate critical conversations about race, immigration, gender, and other fraught issues in their classrooms. In this article, we detail how an innovative partnership among key education stakeholders in the state of Oregon fostered a sense of community and continued learning for kindergarten through eighth grade teachers to address these issues. We did so by developing and facilitating a professional development (PD) sequence focused on anti-racist critical literacy. More than 125 educators from 24 districts around the state participated in the sequence between 2021 and 2024. We begin by situating this work in the literature, then providing an overview of the partnership. Finally, we share the perspectives of 19 educators who spoke in interviews about their experience of the PD. We offer this as an example of how colleges of education can establish and nurture partnerships with other stakeholders to ensure that teachers feel supported in their efforts to further social justice, especially for those who lack community or administrative “backup”, as is the case for many educators in rural parts of the U.S.
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22277102
|
EDUCATION
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10.3390/ai6030043
|
GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer
|
Background: Histopathological images are often used to diagnose breast cancer and have shown high accuracy in classifying cancer subtypes. Prediction of gene expression from whole-slide images and spatial transcriptomics data is important for cancer treatment in general and breast cancer in particular. This topic has been a challenge in numerous studies. Method: In this study, we present a deep learning framework called GeNetFormer. We evaluated eight advanced transformer models including EfficientFormer, FasterViT, BEiT v2, and Swin Transformer v2, and tested their performance in predicting gene expression using the STNet dataset. This dataset contains 68 H&E-stained histology images and transcriptomics data from different types of breast cancer. We followed a detailed process to prepare the data, including filtering genes and spots, normalizing stain colors, and creating smaller image patches for training. The models were trained to predict the expression of 250 genes using different image sizes and loss functions. GeNetFormer achieved the best performance using the MSELoss function and a resolution of 256 × 256 while integrating EfficientFormer. Results: It predicted nine out of the top ten genes with a higher Pearson Correlation Coefficient (PCC) compared to the retrained ST-Net method. For cancer biomarker genes such as DDX5 and XBP1, the PCC values were 0.7450 and 0.7203, respectively, outperforming ST-Net, which scored 0.6713 and 0.7320, respectively. In addition, our method gave better predictions for other genes such as FASN (0.7018 vs. 0.6968) and ERBB2 (0.6241 vs. 0.6211). Conclusions: Our results show that GeNetFormer provides improvements over other models such as ST-Net and show how transformer architectures are capable of analyzing spatial transcriptomics data to advance cancer research.
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26732688
|
AI
|
10.3390/ai6030044
|
Artificial Intelligence Adoption in Public Administration: An Overview of Top-Cited Articles and Practical Applications
|
Background: The adoption of artificial intelligence (AI) in public administration (PA) has the potential to enhance transparency, efficiency, and responsiveness, ultimately creating greater public value. However, the integration of AI into PA faces challenges, including conceptual ambiguities and limited knowledge of the practical applications. This study addresses these gaps by offering an overview and categorization of AI research and applications in PA. Methods: Using a dataset of 3149 documents from the Scopus database, this study identifies the top 200 most-cited articles based on citation per year. It conducts descriptive and content analyses to identify the existing state, applications, and challenges regarding AI adoption. Additionally, selected AI use cases from the European Commission’s database are categorized, focusing on their contributions to public value. The analysis centers on three governance dimensions: internal processes, service delivery, and policymaking. Results: The findings provide a categorized understanding of AI concepts, types, and applications in PA, alongside a discussion of best practices and challenges. Conclusion: This study serves as a resource for researchers seeking a comprehensive overview of the current state of AI in PA and offers policymakers and practitioners insights into leveraging AI technologies to improve service delivery and operational efficiency.
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26732688
|
AI
|
10.3389/frai.2025.1557894
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Predicting therapy dropout in chronic pain management: a machine learning approach to cannabis treatment
|
Introduction: Chronic pain affects approximately 30% of the global population, posing a significant public health challenge. Despite their widespread use, traditional pharmacological treatments, such as opioids and NSAIDs, often fail to deliver adequate, long-term relief while exposing patients to risks of addiction and adverse side effects. Given these limitations, medical cannabis has emerged as a promising therapeutic alternative with both analgesic and anti-inflammatory properties. However, its clinical efficacy is hindered by high interindividual variability in treatment response and elevated dropout rates.Methods: A comprehensive dataset integrating genetic, clinical, and pharmacological information was compiled from 542 Caucasian patients undergoing cannabis-based treatment for chronic pain. A machine learning (ML) model was developed and validated to predict therapy dropout. To identify the most influential factors driving dropout, SHapley Additive exPlanations (SHAP) analysis was performed.Results: The random forest classifier demonstrated robust performance, achieving a mean accuracy of 80% and a maximum of 86%, with an AUC of 0.86. SHAP analysis revealed that high final VAS scores and elevated THC dosages were the most significant predictors of dropout, both strongly correlated with an increased likelihood of discontinuation. In contrast, baseline therapeutic benefits, CBD dosages, and the CC genotype of the rs1049353 polymorphism in the CNR1 gene were associated with improved adherence.Discussion: Our findings highlight the potential of ML and pharmacogenetics to personalize cannabis-based therapies, improving adherence and enabling more precise management of chronic pain. This research paves the way for the development of tailored therapeutic strategies that maximize the benefits of medical cannabis while minimizing its side effects.
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26248212
|
AI
|
10.3389/feduc.2025.1477509
|
Becoming a resilient scientist series: an intervention program
|
Compared to the general population, science trainees experience challenges and heightened stressors that often lead to adverse mental health outcomes. With COVID-19, the stressors of social distancing, isolation, truncated lab time, and uncertainty about the future have all likely exacerbated these issues. Now, more than ever, practical and effective interventions are vitally needed to address the core causes of stress among science trainees and increase their resilience. This paper introduces a new resilience program targeted to biomedical trainees and scientists - Becoming a Resilient Scientist Series (BRS), a 5-part workshop complemented by facilitated group discussions all aimed at bolstering resilience, particularly in the context of academic and research environments. To assess the program’s efficacy, participants completed resilience measures and related assessments before and after completing the series. The results suggest that BRS is associated with improvements in trainee resilience (primary outcome) and with reductions in perceived stress, anxiety, and work-related presenteeism, as well as enhancements in adaptability, self-awareness, and self-efficacy (secondary outcomes). Furthermore, program participants reported a high level of satisfaction, a strong willingness to recommend the program to others, and perceived positive changes in their resilience skills. To the best of our knowledge, this is the first resilience program designed explicitly for biomedical trainees and scientists, tailored to their unique professional culture and work environment.
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2504284X
|
EDUCATION
|
10.3390/cancers17050750
|
Clinical Characteristics and Long-Term Prognosis of Colorectal Mucosa-Associated Lymphoid Tissue Lymphoma According to the Endoscopic Classification and Treatment Modality: A Multicenter Study
|
Background/Objectives: The clinical characteristics of colorectal mucosa-associated lymphoid tissue (MALT) lymphoma remain poorly defined, and there is no standardized treatment for the disease. Therefore, we investigated the clinical characteristics of colorectal MALT lymphoma and its prognosis based on different treatment modalities. Methods: A retrospective analysis was performed on patients diagnosed with colorectal MALT lymphoma from 2003 to 2021 across six hospitals in Korea’s Busan–Ulsan–Gyeongnam area. Macroscopic findings classified all cases into polyposis type, mass-forming type, subepithelial lesion type, and inflammatory type. Results: Fifty-one patients were enrolled. The median age was 59 years, and 27 patients (52.9%) were male. Five patients (9.8%) were stage IV at initial diagnosis. As for the endoscopic type, the polyposis type was the most common (39.2%). There was no statistically significant difference in disease progression according to the endoscopic type (p = 0.813). Three cases of disease progression were confirmed in stage I after treatment, and one of them died due to disease progression. No disease progression was identified in other stages. According to the treatment modality, disease progression was identified in 1 of 16 patients who underwent endoscopic resection and 2 of 16 patients who underwent chemotherapy. There was no disease progression in the observation group. However, there was no statistically significant difference in disease progression according to treatment modality (p = 0.889). Conclusions: Colorectal MALT lymphoma showed good prognosis regardless of the initial stage, endoscopic type, or treatment modality.
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20726694
|
ONCOLOGY
|
10.3389/fonc.2025.1532421
|
Clinical outcomes of avelumab and pembrolizumab in advanced urothelial cancer: an observational multicenter retro-prospective study on patients undergoing treatment in clinical practice (AVePEm study)
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Introduction and objectives: Patients (pts) with metastatic urothelial carcinoma (mUC) gain substantial benefit from immunotherapy exposure. If they do not experience disease progression after 4-6 cycles of first-line platinum-based chemotherapy (PBC), they may benefit from immunotherapy as maintenance treatment with Avelumab; otherwise, Pembrolizumab is an approved second-line therapy after disease progression on first-line chemotherapy. However, no clinical trial data currently demonstrate which treatment strategy offers superior survival outcomes.Patients and methods: This is a multicenter, observational, retro-prospective study involving pts with mUC who did not progress after 4-6 cycles of PBC: GroupA received Avelumab and GroupB Pembrolizumab. The primary endpoints were overall survival (OS) and progression-free survival (PFS), with neutrophil-to-lymphocyte ratio (NLR) ≥3 at the baseline of PBC and at the start of immunotherapy in predicting outcome, adverse events (AEs), subsequent therapies after the immunotherapy strategy, and costs associated with these treatments as secondary endpoints.Results: From August 2019 to October 2024, we identified 30 pts. Of those, 53% were in GroupA and 47% in GroupB. The mOS in GroupA was 27 mo and in GroupB 26 mo and the mPFS of immunotherapy was 7.5 mo and 5.5 mo. At the time of data analysis, 33% (n=10) of pts were alive and 27% (n=8) on treatment, with 38% (n=3) still receiving Avelumab, and 50% (n=4) and 12% (n=1) on subsequent therapies after Avelumab and Pembrolizumab, respectively. Approximately 55% of patients in both groups had a baseline neutrophil-to-lymphocyte ratio (NLR) ≥3 at the baseline of PBC. No statistically significant association was found between NLR, whether considered as a continuous or dichotomous variable, and overall survival or progression free survival. Both treatments were well tolerated, with Grade 3 AEs in 1 pt on Avelumab and 3 on Pembrolizumab, and no Grade 4 AEs reported.Conclusions: The two immunotherapy strategies showed no significant differences in OS and PFS. Of note, more pts were on Avelumab treatment at the data cut-off. AEs were similar in the two groups. Further investigation and follow-up are warranted to gain definitive conclusions on optimal mUC management in the era of immunotherapy and immunoconjugates.
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2234943X
|
ONCOLOGY
|
10.3389/frai.2025.1527299
|
A novel approach to Indian bird species identification: employing visual-acoustic fusion techniques for improved classification accuracy
|
Accurate identification of bird species is essential for monitoring biodiversity, analyzing ecological patterns, assessing population health, and guiding conservation efforts. Birds serve as vital indicators of environmental change, making species identification critical for habitat protection and understanding ecosystem dynamics. With over 1,300 species, India's avifauna presents significant challenges due to morphological and acoustic similarities among species. For bird monitoring, recent work often uses acoustic sensors to collect bird sounds and an automated bird classification system to recognize bird species. Traditional machine learning requires manual feature extraction and model training to build an automated bird classification system. Automatically extracting features is now possible due to recent advances in deep learning models. This study presents a novel approach utilizing visual-acoustic fusion techniques to enhance species identification accuracy. We employ a Deep Convolutional Neural Network (DCNN) to extract features from bird images and a Long Short-Term Memory (LSTM) network to analyze bird calls. By integrating these modalities early in the classification process, our method significantly improves performance compared to traditional methods that rely on either data type alone or utilize late fusion strategies. Testing on the iBC53 (Indian Bird Call) dataset demonstrates an impressive accuracy of 94%, highlighting the effectiveness of our multi-modal fusion approach.
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26248212
|
AI
|
10.1186/s40359-025-02433-5
|
Career adaptability and graduates’ mental health: the mediating role of occupational future time perspective in higher education in China
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This study examines the mediating role of Occupational Future Time Perspective (OFTP) in the relationship between Career Adaptability and Mental Health among college graduates. Using a three-month, three-time-point survey of Chinese graduates (N = 905, ages 25–30), we found that Career Adaptability has a significant direct effect on Mental Health. Among OFTP dimensions, Focus on Opportunities emerged as a key mediator, highlighting its role in linking Career Adaptability to positive mental health outcomes. However, Perceived Remaining Time and Focus on Limitations did not show significant mediation effects. These findings underscore the value of fostering opportunity-focused perspectives in career counseling and educational interventions to support graduates’ mental health.
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20507283
|
PSYCHOLOGY
|
10.3389/fonc.2025.1547054
|
Efficacy and prognostic impact of preoperative risk factors for salvage liver transplantation and repeat hepatectomy in patients with early-stage recurrent hepatocellular carcinoma: a propensity score-matched analysis
|
Background: The optimal treatment strategy for recurrent hepatocellular carcinoma (rHCC) remains unclear. This study is based on cases of rHCC after liver resection, aiming to evaluate the influence of preoperative risk factors on the long-term prognosis of patients with rHCC by comparing patients who underwent salvage liver transplantation (SLT) with those who underwent repeat hepatectomy (RH).Methods: We retrospectively analyzed 401 consecutive patients with rHCC who underwent SLT or RH between March 2015 and December 2022. Next, we performed propensity score matching, subgroup analyses, and both univariate and multivariate analyses. In addition, Kaplan–Meier analysis was used to estimate the overall survival (OS) and recurrence-free survival (RFS) after recurrence.Results: The 1-, 3-, and 5-year OS and RFS rates in the SLT group were significantly higher than those in the RH group (p=0.0131 and p=0.0010, respectively), and similar results were observed after propensity score matching. In the presence of zero or one risk factors, the OS and RFS in the SLT group were significantly better than those in the RH group (p=0.0386 and p=0.0117, respectively). However, in the presence of two to four risk factors, no significant differences in OS or RFS were detected between the two groups (p=0.1119 and p=0.1035, respectively).Conclusion: Our analysis identified a number of risk factors that were strongly correlated with a long term prognosis for patients with rHCC who underwent SLT and RH: multiple tumors, a maximum tumor diameter ≥5 cm, microvascular invasion, and a recurrence time ≤2 years. Our findings provide important reference guidelines for organ allocation and clinical decision-making.
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2234943X
|
ONCOLOGY
|
10.3390/educsci15030299
|
Metacognitive Monitoring in Written Communication: Improving Reflective Practice
|
Educational programs aimed at developing metacognitive skills usually focus on students, neglecting the development of teachers by teaching metacognitively aware instructional methods. The effectiveness of such development programs is well-established, but there is a gap between research findings and their application in schools. A framework for a training program was developed in the context of an international partnership project aimed at enhancing the metacognitive abilities of both children and teachers. The final form of classroom activities was developed at the country level using action research methods with the involvement of teachers. After implementing a 3-week educational program involving 35 experimental and 19 control groups from Romanian public schools, a comparison of pre- and post-test scores indicated a significant increase in the number of children in the experimental group with improved efficiency in metacognitive monitoring in reading. Teachers’ metacognitive awareness significantly improved after the Teacher Training Program, as indicated by a comparison of the pre- and post-training results of the Metacognitive Awareness Inventory for Teachers (MAIT). No correlation was found between teachers’ development scores (as expressed by differences between pre- and post-intervention MAIT results) and the number of students from their classes whose progress in metacognitive monitoring significantly increased. The cyclical process of the action research methodology proved to be useful for increasing the efficiency of the intervention program. However, due to methodological limitations, the results are primarily interpretable within a local context. The results confirm expert recommendations aimed at integrating the targeted development of metacognitive teaching skills into both pre-service and in-service teacher training programs.
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22277102
|
EDUCATION
|
10.3390/ai6030049
|
Integration of Artificial Intelligence in K-12: Analysis of a Three-Year Pilot Study
|
A three-year pilot study investigated the effectiveness of artificial intelligence (AI) as a motivational tool for teaching programming concepts within the Croatian Informatics curriculum. The study was conducted in schools through the extracurricular activity EDIT CodeSchool with the Development of Intelligent Web Applications (RIWA) module. Twelve schools in Split-Dalmatia County in the Republic of Croatia participated, resulting in 112 successfully completed student projects. The program consisted of two phases: (1) theoretical instruction with examples and exercises, and (2) project-based learning, where students developed final projects using JavaScript and the ml5.js library. The study employed project analysis and semi-structured student interviews to assess learning outcomes. Findings suggest that AI-enhanced learning can effectively support programming education without increasing instructional hours, providing insights for integrating AI concepts into existing curricula.
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26732688
|
AI
|
10.3390/educsci15030309
|
Self-Regulation and Teacher Feedback in Problem-Based Learning on the Water Hardness
|
Problem-Based Learning has been recognized as a fundamental approach in Science Education. Studies show that the success of this approach depends on students’ ability to self-regulate their learning and on teacher feedback. However, research on how these aspects interact in formal science teaching contexts remains limited. This study aims to address this gap by investigating two questions: (1) What self-regulation strategies are used by different student groups when solving a problem related to water hardness? (2) How do different types of teacher feedback influence students’ problem-solving processes? The study involved 27 students and their Physics and Chemistry teacher. Students participated in an activity that required solving a problem related to water hardness. Data were collected through audio recordings, and the content of the transcriptions was analyzed. The results showed connections between self-regulation strategies and teacher feedback during the problem-solving process. Groups with high participation employed diverse self-regulation strategies, successfully solved the problem, and received varied teacher feedback. The group with the lowest participation received the least feedback from the teacher. Future research should focus on examining how different types of teachers’ feedback during specific interventions for less-participative groups influence the development of their self-regulation strategies.
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22277102
|
EDUCATION
|
10.3390/cancers17050874
|
Real-World Insights into the Impact of Durvalumab on Stage III Unresectable Non-Small Cell Lung Cancer—A Narrative Review
|
Introduction and Aim: Stage III Non-Small Cell Lung Cancer (NSCLC) has a poor prognosis, with median survival ranging from 9 to 34 months. The PACIFIC trial demonstrated that durvalumab after platinum-based chemoradiotherapy (CRT) improves overall survival (OS) and progression-free survival (PFS). This review evaluates real-world evidence (RWE) on durvalumab’s efficacy and safety, focusing on patient characteristics, prognostic factors, treatment protocols, and outcomes beyond progression. Materials and Methods: A literature search of PubMed, Embase, and Google Scholar identified 49 observational studies published from January 2017 to August 2024 on unresectable stage III NSCLC. Clinical trials, early-stage disease, and alternative treatments were excluded. Results: Compared to the PACIFIC trial, real-world patients were older, had poorer ECOG performance (≥2), and more comorbidities like COPD. Despite this, durvalumab provided consistent survival benefits. Positive prognostic factors included non-squamous histology, high PD-L1 expression, and timely durvalumab initiation (≤42 days post-CRT). Most radiotherapy regimens mirrored PACIFIC (54–66 Gy). Concomitant CRT was used in 90% of cases, with sequential CRT for frail patients. Chemotherapy regimens varied. Immune-mediated pneumonitis was a major adverse event, with incidence rates between 15% and 100%. Severe cases led to treatment discontinuation, impacting survival. Treatment beyond progression remains uncertain, with limited benefits from immunotherapy rechallenge. Conclusions: RWE supports durvalumab’s efficacy, emphasizing the need for personalized treatment strategies and further research to improve long-term outcomes.
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20726694
|
ONCOLOGY
|
10.3390/ai6030051
|
Sentence Interaction and Bag Feature Enhancement for Distant Supervised Relation Extraction
|
Background: Distant supervision employs external knowledge bases to automatically match with text, allowing for the automatic annotation of sentences. Although this method effectively tackles the challenge of manual labeling, it inevitably introduces noisy labels. Traditional approaches typically employ sentence-level attention mechanisms, assigning lower weights to noisy sentences to mitigate their impact. But this approach overlooks the critical importance of information flow between sentences. Additionally, previous approaches treated an entire bag as a single classification unit, giving equal importance to all features within the bag. However, they failed to recognize that different dimensions of features have varying levels of significance. Method: To overcome these challenges, this study introduces a novel network that incorporates sentence interaction and a bag-level feature enhancement (ESI-EBF) mechanism. We concatenate sentences within a bag into a continuous context, allowing information to flow freely between them during encoding. At the bag level, we partition the features into multiple groups based on dimensions, assigning an importance coefficient to each sub-feature within a group. This enhances critical features while diminishing the influence of less important ones. In the end, the enhanced features are utilized to construct high-quality bag representations, facilitating more accurate classification by the classification module. Result: The experimental findings from the New York Times (NYT) and Wiki-20m datasets confirm the efficacy of our suggested encoding approach and feature improvement module. Our method also outperforms state-of-the-art techniques on these datasets, achieving superior relation extraction accuracy.
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26732688
|
AI
|
10.3389/frai.2025.1504281
|
Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence
|
Heart disease is a leading cause of mortality worldwide, making accurate early detection essential for effective treatment and management. This study introduces a novel hybrid machine-learning approach that combines transfer learning using the VGG16 convolutional neural network (CNN) with various machine-learning classifiers for heart disease detection. A conditional tabular generative adversarial network (CTGAN) was employed to generate synthetic data samples from actual datasets; these were evaluated using statistical metrics, correlation analysis, and domain expert assessments to ensure the quality of the synthetic datasets. The dataset comprises tabular data with 13 features, which are reshaped into an image-like format and resized to 224x224x3 to meet the input requirements of the VGG16 model. Feature extraction is performed using VGG16, and the extracted features are then fused with the original tabular data. This combined feature set is then used to train various machine learning models, including Support Vector Machines (SVM), Gradient Boosting, Random Forest, Logistic Regression, K-nearest neighbors (KNN), and Decision Trees. Among these models, the VGG16-Random Forest hybrid achieved notable results across all evaluation metrics, including 92% accuracy, 91.3% precision, 92.2% recall, 91.82% specificity, 92.2% sensitivity, and 91.75% F1-score. The hybrid models were also evaluated using unseen datasets to assess the generalizability of the proposed approaches, with the VGG16-Random Forest combination showing relatively promising results. Additionally, explainability is integrated into the model using SHAP values, providing insights into the contribution of each feature to the model’s predictions. This hybrid VGG16-ML approach demonstrates the potential for highly accurate and interpretable heart disease detection, offering valuable support in clinical decision-making processes.
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26248212
|
AI
|
10.3389/feduc.2025.1510872
|
Improving sense of belonging in biomedical engineering students through student-faculty lunches
|
Introduction: Full undergraduate experience in biomedical engineering should feature cordial interactions between students and faculty as well as a good sense of belonging. However, both factors remain elusive for many students, rendering their undergraduate experience suboptimal. We designed the organized student-faculty lunches to promote informal student-faculty interactions and the formation of belonging among the student participants.Methods: During each lunch, an average of four student participants were paired with one faculty and a student assistant. Lunches were provided at no cost to all participants. Invites for students were based on matching interests during recruitment. A mixed-methods survey, including eight identical Likert-scale questions and up to three free-response questions, was distributed three times: before, immediately after, and 1 month after the lunch. We collected a total of 42 responses for the post-survey and 28 responses for the one-month survey. Four students participated in a 30-minute interview. We used paired t-tests to analyze the Likert-scale questions across the three surveys. We performed regression analysis to quantify the equity in the outcomes of these lunches. We obtained guidelines for conducting these lunches in the future through regression analysis and thematic coding of the surveys and the interviews.Results: We found that the student-faculty lunches generated significant positive impact across all eight Likert-scale questions across three domains of belonging: academic, social, and personal space. Improvements in survey questions within the social and personal space domains tend to be longer lasting and more statistically significant. The regression analyses revealed that our interventions resulted in better parity in sense of belonging among students with different years of academic experience, ethnic identities, and gender identities. These analyses also suggest that the most effective lunch is conducted in the middle of the Winter quarter with an Assistant Professor. Coding analyses revealed that the students were highly satisfied with the lunches and the current format of facilitation, while noting the benefits of these lunches in reducing the interaction barriers between students and faculty. We intend to perform more qualitative analyses on aspects of equity and faculty demographics concerning their impact on the outcomes of these lunches.
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2504284X
|
EDUCATION
|
10.3389/feduc.2025.1553898
|
Integrating global perspectives in biomedical science education: the role of project-based learning in addressing Western-centric paradigms and enhancing student preparedness for global health challenges
|
Biomedical Sciences education has traditionally focused on Western paradigms, often overlooking the health challenges faced in less economically developed countries. Integrating global perspectives is essential, yet institutional guidelines lack clear directives for doing so. This perspective paper proposes a project-based learning (PBL) approach within undergraduate biomedical sciences modules, which focuses on tropical infectious diseases to promote decolonized learning by contrasting the Global North and South. In this model, students will work collaboratively to learn problem-solving techniques relevant to real-world issues like tropical diseases. Although in theory PBL is a useful way of learning, there are potential challenges with group dynamics and engagement. This paper discusses the various benefits and limitations of implementing this approach.
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2504284X
|
EDUCATION
|
10.1186/s40359-025-02439-z
|
Exploring gender differences in workload and job performance: insights from junior high school teachers
|
Notable gender disparities exist in the workload and performance of junior high school teachers, although the specific ways in which these disparities manifest have not been fully elucidated. This study examines how specific aspects of teachers’ workload are related to gender differences in aspects of work performance. The study used survey data from 1135 junior high school educators. Teacher workload was assessed using the NASA Task Load Index. Teachers’ work performance was evaluated in terms of task performance and contextual performance. Demographic data included gender, teaching experience, teaching grade, titles, school ownership, rural school designation, marital status, whether they had children, were internal teachers, were multidisciplinary teachers, and whether they were the main subject teachers. Oaxaca–Blinder decomposition was used to analyze the specific contribution and mechanism of workload to the gender gap in work performance. The findings revealed distinct gender differences in work performance, with male teachers demonstrating higher task performance and female teachers reporting higher contextual performance, which mediated the observed disparity. Further analysis indicated that marital status also plays a role, with single teachers experiencing a more pronounced gender gap. These insights signify that gender is a pivotal factor in junior high school teachers’ workload and performance. The study advocates for a deeper investigation within the “gender-workload-capacity development” framework to assist educators in making informed decisions and to foster a more equitable work environment.
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20507283
|
PSYCHOLOGY
|
10.1186/s40359-025-02522-5
|
Coarse matching was sufficient to capture attention by working memory representations unless matching features with the target
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Background: In most theoretical frameworks, the effectiveness of attentional selection relies significantly on the perceptual similarity between the target template and visual input. Nevertheless, ambiguity exists surrounding whether attentional capture triggered by irrelevant representations in Working Memory (WM) is influenced by the perceptual similarity levels of features between WM content and its matching distractors. Methods: We designed a hybrid WM and visual search task, varying such perceptual similarity of colors across three levels: exact, high-similar, and low-similar matching. To quantify the extent of the capture effect, we compared these conditions against a neutral baseline (i.e., completely different color) using eye movement and behavioral data in two experiments. Results: We consistently observed robust attentional capture effects across two experiments, evident in both eye movement indices and manual reaction times. In Experiment 1, where WM representations solely matched features to visual search distractors (task-irrelevant scenario), we found that changes in perceptual similarity did not influence attentional capture. Conversely, in Experiment 2, where WM representations had the potential to match the visual search target (task-relevant scenario), we observed a significantly more robust attentional capture effect for high-similar matching compared to low-similar matching conditions. Conclusions: These findings imply that coarse matching between distractors and WM contents is sufficient to capture attention, unless the matching features potentially correspond to the visual target. Furthermore, task relevance sharpens perceptual sensitivity to visual input, highlighting distinct mechanisms underlying attentional capture by irrelevant representations and target templates within WM.
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20507283
|
PSYCHOLOGY
|
10.1186/s40594-025-00537-3
|
The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches
|
This study investigates the impact of AI-assisted pair programming on undergraduate students’ intrinsic motivation, programming anxiety, and performance, relative to both human–human pair programming and individual programming approaches. A quasi-experimental design was conducted over two academic years (2023–2024) with 234 undergraduate students in a Java web application development course. Intact class sections were randomly assigned to AI-assisted pair programming (using GPT-3.5 Turbo in 2023 and Claude 3 Opus in 2024), human–human pair programming, or individual programming conditions. Data on intrinsic motivation, programming anxiety, collaborative perceptions, and programming performance were collected at three time points using validated instruments. Compared to individual programming, AI-assisted pair programming significantly increased intrinsic motivation (p < .001, d = 0.35) and reduced programming anxiety (p < .001), producing outcomes comparable to human–human pair programming. AI-assisted groups also outperformed both individual and human–human groups in programming tasks (p < .001). However, human–human pair programming fostered the highest perceptions of collaboration and social presence, surpassing both AI-assisted and individual conditions (p < .001). Mediation analysis revealed that perceived usefulness of the AI assistant significantly mediated the relationship between the programming approach and student outcomes, highlighting the importance of positive perceptions in leveraging AI tools for educational benefits. No significant differences emerged between the two AI models employed, indicating that both GPT-3.5 Turbo and Claude 3 Opus provided similar benefits. While AI-assisted pair programming enhances motivation, reduces anxiety, and improves performance, it does not fully match the collaborative depth and social presence achieved through human–human pairing. These findings highlight the complementary strengths of AI and human interaction: AI support can bolster learning outcomes, yet human partners offer richer social engagement. As AI capabilities advance, educators should integrate such tools thoughtfully, ensuring that technology complements rather than replaces the interpersonal dynamics and skill development central to effective programming education.
|
21967822
|
EDUCATION
|
10.1186/s40359-025-02513-6
|
Moral transgressions, psychological well-being, and family conflict in the context of the COVID-19 pandemic: The role of self-forgiveness
|
The COVID-19 pandemic led many individuals to experience moral transgressions, exacerbating feelings of guilt and remorse. This study explored the role of the self-forgiveness of such transgressions in explaining their associations with psychological well-being and family conflict. We hypothesized that (a) higher levels of self-forgiveness would be associated with greater psychological well-being and reduced family conflict, (b) the perceived relevance of moral transgressions would be positively associated with self-forgiveness and indirectly associated with psychological well-being and family conflict through the mediation of self-forgiveness, and (c) the relationships between the variables of interest could vary across age. Adults (N = 277; M age = 30.04) completed anonymous online questionnaires assessing the relevance of transgressions committed, forgiveness and unforgiveness of self, psychological well-being, and family conflict during the first COVID-19 lockdown in Italy. Structural equation modeling revealed that transgression relevance was positively associated with both forgiveness and unforgiveness of self, and indirectly related to psychological well-being and family conflict via self-forgiveness. Greater forgiveness of self was related to greater eudaimonic well-being, whereas greater unforgiveness of self was linked to increased family conflict and reduced eudaimonic well-being. The findings also indicated that age moderated the relationship between forgiveness of self and hedonic well-being, with the association weakening as age increased. The results highlight the importance of promoting self-forgiveness to enhance psychological resilience and familial stability, particularly during challenging times.
|
20507283
|
PSYCHOLOGY
|
10.1007/s44196-025-00755-1
|
Lattice-Based Decision Models for Green Urban Development: Insights from $$L_{q}*$$ q-Rung Orthopair Multi-fuzzy Soft Set
|
Location selection is a critical process in decision-making for projects that involve multiple criteria, such as urban planning, industrial site development, or green building projects. Multiple criteria decision making (MCDM) is a systematic approach that evaluates and ranks potential alternatives based on a set of often conflicting criteria. This study focuses on selecting the optimal urban location for a green building project by employing the \(L_{q}*\) q-rung orthopair multi-fuzzy soft-MCDM(\(L_{q}*\) q-ROMFS) techniques. The \(L_{q}*\) q-ROMFS set combines elements from two distinct theories with lattice ordering parameters: q-rung orthopair fuzzy set and multi-fuzzy soft set. It provides a mathematical framework with multiple parameters that effectively represents problems involving multi-dimensional data within a dataset. We expand this concept by establishing the algebraic structures of \(L_{q}*\) q-ROMFS sets, including properties like modularity and distributivity, while also analyzing their homomorphism under lattice mappings. Finally, leveraging the \(L_{q}*\) q-ROMFS matrix, we propose both a choice matrix and a weighted choice matrix to effectively address the selection of the optimal urban location for a green building project.
|
18756883
|
AI
|
10.1186/s40359-025-02494-6
|
The use of video feedback to promote developmentally supportive parent–child interactions with young children with ASD or at risk: study protocol for a randomized controlled trial (VIFEPOPA-RCT)
|
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication and interaction, and repetitive and restrictive behaviors and interests from an early age. ASD often negatively affects caregiver-child interactions, caregiver emotional well-being and self-efficacy, and quality of family life. Positive caregiver–child interactions are crucial for good developmental outcomes, leading to the development of Parent-Mediated Interventions (PMIs). PMIs tend to follow an expert model where professionals provide direct instruction on treatment techniques and parental behaviors. However, research supports a shift towards a more collaborative and reflective approach, using coaching strategies that highlight caregiver strengths and encourage self-reflection. This study tests a video-feedback intervention (VFI) with parents of young children at risk of ASD. A randomized controlled trial (RCT) with 60 families, recruited from Early Intervention Centers in Spain, meeting inclusion criteria: adequate use of internet, child aged 24–36 months with a high risk of ASD (M-CHAT-R score ≥ 8), and participant primary caregiver (mother or father) with high anxiety, depression, or parental stress (score ≥ 1 SD above M), and low or medium–low developmentally supportive parental behaviors (PICCOLO score ≤ 40). Families will be randomly assigned to an intervention group (receiving usual services plus VFI) or a control group (usual services). The intervention includes twelve bi-weekly 90-min sessions over six months, with the caregiver. Outcome measures include parenting behaviors, emotional state, self-efficacy, family quality of life, and child development collected at pre-intervention, post- intervention and six-month follow-up. The study will assess whether the intervention enhances developmentally supportive parental behaviors, emotional well-being, self-efficacy, and family quality of life, with a secondary positive impact on child development. If proven effective, it could be a cost-effective intervention with both short and long-term benefits. ClinicalTrials.gov Identifier NCT06604988. Registered on September 17, 2024. Retrospectively registered.
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20507283
|
PSYCHOLOGY
|
10.3389/feduc.2025.1523124
|
Basic psychological needs satisfaction as a mediator of the effects of a formative assessment practice on behavioral engagement and autonomous motivation
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Formative assessment has been suggested as a means of supporting student motivation. However, empirical studies have shown mixed effects of formative assessment interventions on students’ motivation, making it necessary to understand the mechanisms underlying these effects. We analyzed a formative classroom practice implemented by a 10th-grade first-language teacher during 7 months. Teacher logs, classroom observations and a teacher interview were used to collect data for characterizing the formative assessment practice. Changes in students’ satisfaction regarding the basic psychological needs of perceived autonomy, competence and relatedness, as well as changes in student motivation manifesting as engagement in learning activities and autonomous types of motivation, were measured by pre- and post-questionnaires in the intervention class and four comparison classes. Since the intraclass correlation values ICC(1) and ICC(2) were low, we treated the comparison classes as one group and t-tests were used in the significance testing of the differences in changes in psychological needs satisfaction and motivation between the intervention class and the comparison classes. Path analysis was conducted to investigate whether a possible influence of the intervention on autonomous motivation and behavioral engagement would be mediated by basic psychological needs satisfaction. The analysis of the classroom practice in the intervention class identifies that both teacher and students were proactive agents in formative assessment processes. The analysis of the quantitative data shows that students’ psychological needs satisfaction increased more in the intervention class than in the comparison classes, and that this needs satisfaction mediated an effect on students’ behavioral engagement and autonomous motivation.
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2504284X
|
EDUCATION
|
10.1007/s00432-025-06135-8
|
Impact of interdisciplinary tumor boards (ITB) and personalized treatment on survival outcomes in metastatic castration-resistant prostate cancer
|
Purpose: Interdisciplinary tumor boards (ITB) are essential in optimizing treatment recommendations for metastatic castration-resistant prostate cancer (mCRPC) by incorporating oncology guidelines, clinical trials, and patient-specific factors to ensure individualized care. This study examines clinical parameters that influence ITB recommendations, evaluates their adherence to guidelines, and assesses their impact on patient survival. Methods: In a retrospective analysis, data from 187 mCRPC patients discussed at an ITB in a tertiary care center in 2018 were evaluated. Patient- and disease-specific factors were correlated with adherence to National Comprehensive Cancer Network® (NCCN®) guidelines and overall survival (OS). The impact of clinical parameters on survival outcomes was assessed through univariate and multivariate analyses. Results: The median patient age was 72.8 years, with a median prostate-specific antigen (PSA) level of 65.0 ng/ml. Guideline-compliant recommendations were given in 42.9% of cases, while 57.1% received individualized recommendations. Clinical trial eligibility was noted in 24.8% of patients. Individualized ITB recommendations were associated with significantly longer OS (38.3 vs. 21.2 months, p = 0.03). Shorter OS correlated with renal impairment (p = 0.007), symptomatic metastases (p < 0.0001), and visceral metastases (p < 0.0001). Limitations include the retrospective design, lack of follow-up on therapy adherence, and absence of progression-free survival (PFS) data. Conclusion: ITB discussions improve survival in mCRPC patients, mainly due to personalized approaches and better access to clinical trials. Visceral and symptomatic metastases as well as renal impairment are risk factors for reduced OS, emphasizing the need for careful management of these high-risk patients. The results support the expanded use of ITB to improve mCRPC treatment outcomes.
|
14321335
|
ONCOLOGY
|
10.3389/fpsyg.2025.1532937
|
An encounter with death: a comparative thematic and content analysis of naturalistic DMT experiences and the near-death experience
|
Introduction: Classical near-death experiences (NDEs) refer to states of disconnected consciousness characterised by a range of features occurring in the context of being close to death. Various psychedelic substances, such as N,N-dimethyltryptamine (DMT), consistently replicate NDE features and may be considered ‘near-death-like experiences.’ However, a systematic qualitative analysis comparing the specifics of content with the broader themes of both psychedelic and NDEs has yet to be conducted.Methods: We report the third thematic and content analysis of the DMT experience from a naturalistic field study, focusing on themes related to death and dying. Based on 36 semi-structured interviews, this analysis is then directly compared, qualitatively and in terms of content frequency, with a novel extension of a previous thematic analysis of 34 written NDE narratives.Results: The ‘canonical NDE themes’ identified across the DMT experiences included Translocation, Bright Light(s), Sense of Dying, The Void, Disembodiment, Tunnel-like Structures, Light Being-esque Entities, Deceased Family, Life Review-like, and Hyper-empathic Experiences. A total of 95% of participants reported at least one of these. Twelve ‘less typical NDE motifs’ were also noted. Five classical NDE features were entirely absent from DMT, while DMT exhibited an even broader array of experience features that were absent from NDEs. DMT clearly shares a more basic phenomenological structure with NDEs but shows differences in the prevalence of certain features. Furthermore, DMT did not present any immediately recognisable linear sequencing of themes. Overall, DMT is distinctly unique in its qualitative content, characterised by its more prodigious and stereotypical nature, which includes kaleidoscopic, extraterrestrial, transcultural, fluctuating, and overwhelming elements.Discussion: When examining the comparability between DMT and NDEs at a fundamentally more nuanced level of qualitative content (as opposed to broad themes or questionnaire items), the two experiences clearly diverge. However, a minority of NDEs, which are themselves unique, do share significant content with DMT. Taken together, DMT could be considered an ‘NDE-mimetic.’ The weaker comparability is likely due not only to differences in context but also to the complex neural processes occurring near death, in which endogenous DMT may only play a small role. In light of this level of parallelism with NDEs, some potential clinical applications of DMT are also discussed.
|
16641078
|
PSYCHOLOGY
|
10.3389/frai.2025.1554325
|
The relevance of lead prioritization: a B2B lead scoring model based on machine learning
|
In business-to-business (B2B) companies, marketing and sales teams face significant challenges in identifying, qualifying, and prioritizing a large number of leads. Lead prioritization is a critical task for B2B organizations because it allows them to allocate resources more effectively, focus their sales force on the most viable and valuable opportunities, optimize their time spent qualifying leads, and maximize their B2B digital marketing strategies. This article addresses the topic by presenting a case study of a B2B software company's development of a lead scoring model based on data analytics and machine learning under the consumer theory approach. The model was developed using real lead data generated between January 2020 and April 2024, extracted from the company's CRM, which were analyzed and evaluated by fifteen classification algorithms, where the results in terms of accuracy and ROC AUC showed a superior performance of the Gradient Boosting Classifier over the other classifiers. At the same time, the feature importance analysis allowed the identification of features such as “source” and “lead status,” which increased the accuracy of the conversion prediction. The developed model significantly improved the company's ability to identify high quality leads compared to the traditional methods used. This research confirms and complements existing theories related to understanding the application of consumer behavior theory and the application of machine learning in the development of B2B lead scoring models. This study also contributes to bridging the gap between marketers and data scientists in jointly understanding lead scoring as a critical activity because of its impact on overall marketing strategy performance and sales revenue performance in B2B organizations.
|
26248212
|
AI
|
10.1007/s00432-025-06154-5
|
Semaglutide, a glucagon-like peptide-1 receptor agonist, inhibits oral squamous cell carcinoma growth through P38 MAPK signaling pathway
|
Aims Researches have shown that diabetes mellitus (DM) can promote the risk and progression of oral squamous cell carcinoma (OSCC). Semaglutide, a glucagon-like peptide-1 receptor agonist, is currently employed to treat type 2 diabetes mellitus (T2DM) and obesity. This study intends to explore the potential effects and mechanism of Semaglutide on OSCC. Methods The expression of GLP-1R in OSCC cells and tissues was evaluated by qRT-PCR, western blot and immunohistochemistry assays. Cell proliferation, invasion, migration and apoptosis abilities were determined by relevant experiments. Western blot was employed to verify the expression of relevant proteins and examine the effect of Semaglutide on the MAPK signaling pathway. The xenograft transplantation model of OSCC was established to examine the anti-cancer effects of Semaglutide in vivo and immunohistochemistry assays were performed on tumor tissues. Results GLP-1R expression was elevated in OSCC cells and tissues as compared with that in normal. Semaglutide effectively inhibited the proliferation, migration and invasion of OSCC cells while concurrently promoting apoptosis. Moreover, Semaglutide specifically activated the P38 MAPK signaling pathway without significant influence on ERK1/2 or SAPK/JNK, and its pro-apoptotic effects in OSCC cells was related to P38 pathway activation. Animal experiments verified the inhibitory effect of Semaglutide on OSCC tumors in mice. Conclusions Semaglutide exerts inhibitory actions on OSCC and may induce apoptosis in OSCC cells via the P38 MAPK signaling pathway. This study has significant implications for the treatment of patients with diabetes who are also afflicted by OSCC.
|
14321335
|
ONCOLOGY
|
10.3390/ai6030053
|
Trade-Offs in Navigation Problems Using Value-Based Methods
|
Deep Q-Networks (DQNs) have shown remarkable results over the last decade in scenarios ranging from simple 2D fully observable short episodes to partially observable, graphically intensive, and complex tasks. However, the base architecture of a vanilla DQN presents several shortcomings, some of which were mitigated by new variants focusing on increased stability, faster convergence, and time dependencies. These additions, on the other hand, bring increased costs in terms of the required memory and lengthier training times. In this paper, we analyze the performance of state-of-the-art DQN families in a simple partially observable mission created in Minecraft and try to determine the optimal architecture for such problem classes in terms of the cost and accuracy. To the best of our knowledge, the analyzed methods have not been tested on the same scenario before, and hence a more in-depth comparison is required to understand the real performance improvement they provide better. This manuscript also offers a detailed overview of state-of-the-art DQN methods, together with the training heuristics and performance metrics registered during the proposed mission, allowing researchers to select better-suited models to solving future problems. Our experiments show that Double DQN networks are capable of handling partially observable scenarios gracefully while maintaining a low hardware footprint, Recurrent Double DQNs can be a good candidate even when the resources must be restricted, and double-dueling DQNs are a well-performing middle ground in terms of their cost and performance.
|
26732688
|
AI
|
10.3390/educsci15030339
|
Procedural Learning in Mixed Reality: Assessing Cognitive Load and Performance
|
Immersive technologies offer promising advancements in medical education, particularly in procedural skill acquisition. However, their implementation often lacks a foundation in learning theories. This study investigates the application of the split-attention principle, a multimedia learning guideline, in the design of knot-tying procedural content using a mixed reality (MR) technology, specifically Microsoft HoloLens 2. A total of 26 participants took part in a between-group design experiment comparing integrated and split-source formats for learning arthroscopic knots, with the performance and the cognitive load assessed. The initial hypotheses were not confirmed, as results did not show significant differences in performance during recall, nor in extraneous and germane cognitive load. However, the findings on intrinsic cognitive load highlight the complexity of participant engagement and the cognitive demands of procedural learning. To better capture the split-attention effect, future research should address the high element interactivity in MR representations. The study provides some foundation for designing procedural simulation training that considers both learners’ needs and cognitive processes in highly immersive environments. It contributes to the ongoing exploration of instructional design in MR-based medical education, emphasizing both the potential and challenges of multimedia learning principles in advanced technological contexts.
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22277102
|
EDUCATION
|
10.3390/ejihpe15030032
|
Sex, Resilience and Psychological Well-Being in Mexican University Students
|
Mental health is currently highly relevant in society and one of the factors that could contribute to its improvement is psychological well-being, hence the importance of conducting studies that focus on analyzing variables that predict psychological well-being. Therefore, the goal of this research is to use models of structural equations to analyze the relationships among the variables of sex and resilience for psychological well-being. The total sample was 1190 Mexican university students, with an average age of 20.66 years (SD = 1.89). The results indicate that the resilience factors (strength and confidence, family support, and social support) are the variables with the greatest explanatory power on psychological well-being. It also highlights the mediating capacity of the strength and confidence factor between the other two resilience factors (family support, social support) and perceived psychological well-being. The implications of the study are that sex and two of the dimensions of resilience (family support and social support) show an indirect and positive effect on the perception of psychological well-being through the strength and confidence factor. Therefore, when implementing interventions to improve psychological well-being, these factors should be considered in order to have a greater positive impact on the population that is being studied. Future research should replicate these findings in larger samples.
|
22549625
|
PSYCHOLOGY
|
10.3389/frai.2025.1491958
|
Code generation system based on MDA and convolutional neural networks
|
Introduction: The software industry has rapidly evolved with high performance. This is owing to the implementation of good programming practices and architectures that make it scalable and adaptable. Therefore, a strong incentive is required to develop the processes that initiate this project.Method: We aimed to provide a platform that streamlines the development process and connects planning, structuring, and development. Specifically, we developed a system that employs computer vision, deep learning, and MDA to generate source code from the diagrams describing the system and the respective study cases, thereby providing solutions to the proposed problems.Results and discussion: The results demonstrate the effectiveness of employing computer vision and deep learning techniques to process images and extract relevant information. The infrastructure is designed based on a modular approach employing Celery and Redis, enabling the system to manage asynchronous tasks efficiently. The implementation of image recognition, text analysis, and neural network construction yields promising outcomes in generating source code from diagrams. Despite some challenges related to hardware limitations during the training of the neural network, the system successfully interprets the diagrams and produces artifacts using the MDA approach. Plugins and DSLs enhance flexibility by supporting various programming languages and automating code deployment on platforms such as GitHub and Heroku.
|
26248212
|
AI
|
10.3390/ai6030054
|
A Bibliometric Analysis on Artificial Intelligence in the Production Process of Small and Medium Enterprises
|
Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the manufacturing sector, such as the demand for customized products, market volatility and sustainability goals, and the integration of artificial intelligence and Big Data. This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, with no time limitation, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries. However, the adoption of modern technologies, particularly AI, can be challenging for them, due to the intrinsic structure of this type of enterprise, despite the positive effects obtained in large organizations.
|
26732688
|
AI
|
10.3390/ai6030055
|
Integrating Pose Features and Cross-Relationship Learning for Human–Object Interaction Detection
|
Background: The main challenge in human–object interaction detection (HOI) is how to accurately reason about ambiguous, complex, and difficult to recognize interactions. The model structure of the existing methods is relatively single, and the image input may be occluded and cannot be accurately recognized. Methods: In this paper, we design a Pose-Aware Interaction Network (PAIN) based on transformer architecture and human posture to address these issues through two innovations: A new feature fusion method is proposed, which fuses human pose features and image features early before the encoder to improve the feature expression ability, and the individual motion-related features are additionally strengthened by adding to the human branch; the Cross-Attention Relationship fusion Module (CARM) better fuses the three-branch output and captures the detailed relationship information of HOI. Results: The proposed method achieves 64.51%AProle#1, 66.42%AProle#2 on the public dataset V-COCO and 30.83% AP on HICO-DET, which can recognize HOI instances more accurately.
|
26732688
|
AI
|
10.3390/ai6030056
|
Emotion-Aware Embedding Fusion in Large Language Models (Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4) for Intelligent Response Generation
|
Empathetic and coherent responses are critical in automated chatbot-facilitated psychotherapy. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We introduce Emotion-Aware Embedding Fusion, a novel framework integrating hierarchical fusion and attention mechanisms to prioritize semantic and emotional features in therapy transcripts. Our approach combines multiple emotion lexicons, including NRC Emotion Lexicon, VADER, WordNet, and SentiWordNet, with state-of-the-art LLMs such as Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4. Therapy session transcripts, comprising over 2000 samples, are segmented into hierarchical levels (word, sentence, and session) using neural networks, while hierarchical fusion combines these features with pooling techniques to refine emotional representations. Attention mechanisms, including multi-head self-attention and cross-attention, further prioritize emotional and contextual features, enabling the temporal modeling of emotional shifts across sessions. The processed embeddings, computed using BERT, GPT-3, and RoBERTa, are stored in the Facebook AI similarity search vector database, which enables efficient similarity search and clustering across dense vector spaces. Upon user queries, relevant segments are retrieved and provided as context to LLMs, enhancing their ability to generate empathetic and contextually relevant responses. The proposed framework is evaluated across multiple practical use cases to demonstrate real-world applicability, including AI-driven therapy chatbots. The system can be integrated into existing mental health platforms to generate personalized responses based on retrieved therapy session data. The experimental results show that our framework enhances empathy, coherence, informativeness, and fluency, surpassing baseline models while improving LLMs’ emotional intelligence and contextual adaptability for psychotherapy.
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26732688
|
AI
|
10.3389/fpsyg.2025.1545943
|
From contemplation to serenity: how yoga meditation improves the mental health of female college students?
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Objective: This study aims to investigate the impact of yoga meditation on the mental health of female college students, focusing on how meditation improves emotional regulation, alleviates stress and strengthens psychological resilience.Methods: Employing a combination of quantitative assessment and qualitative analysis, the study measured participants’ emotional states, stress levels, and psychological resilience across multiple time points to track participants’ mental health changes dynamically. In-depth interviews and analysis of meditation journals were also conducted.Results: Yoga meditation significantly reduced anxiety, depression, and perceived stress while enhancing emotional regulation and self-awareness. Meditation positively influenced neuroplasticity, inducing beneficial changes in brain regions associated with emotional control and cognitive flexibility. Additionally, improved autonomic nervous system function was observed, with increased parasympathetic activity and reduced sympathetic response. Meditation strengthened psychological resilience in female college students, improved stress-coping strategies, and sustained positive mental health benefits even after the intervention.Conclusion: Yoga meditation is an effective mental health intervention, bolstering emotional regulation and reducing stress among female college students. Integrating yoga meditation into campus mental health programs is recommended to provide students with greater practice opportunities and personalized guidance.
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16641078
|
PSYCHOLOGY
|
10.3389/feduc.2025.1481708
|
Technologies applied to education in the learning of English as a second language
|
This systematic review, conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, evaluates the efficacy of emerging digital technologies—namely virtual reality (VR), augmented reality (AR), and adaptive learning technologies (ALT)—in enhancing vocabulary acquisition within English as a second language (ESL) education. By addressing a notable gap in the literature, this review explores how these technologies mitigate common learning challenges and improve educational outcomes. Through a critical analysis of recent empirical studies across diverse educational stages, it synthesizes findings to assess their impact on vocabulary retention and overall academic performance. The results indicate that these technologies enhance vocabulary acquisition and increase student motivation and engagement, significantly impacting educational practices and policymaking. This review highlights the transformative potential of VR, AR, and ALT in ESL education by providing immersive and personalized learning experiences that address traditional barriers in language acquisition.
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2504284X
|
EDUCATION
|
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