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10.1007/s44196-024-00676-5
|
Fuzzy Association Rule Mining for Personalized Chinese Language and Literature Teaching from Higher Education
|
Due to rapid information technology growth, teaching Chinese in higher education has changed, and Chinese literary majors have vigorously evolved. The key teaching difficulties are scalability, individualized teaching, and a lack of resources and methodologies. Research shows individualized education improves topic comprehension, cultural engagement, and learner interest. Fuzzy association rule mining uses fuzzy linguistic values and membership functions to provide more realistic results. Hence, an algorithm, EF-PCL2T, has been proposed to improve personalized Chinese language and literature teaching (PCL2T) using enhanced fuzzy (EF) Apriori association rule mining integrated with the genetic algorithm. Fuzzy Apriori association rule mining identified frequent itemsets with relevant learning patterns and produced applicable association rules from datasets with fuzzy or unclear information, capturing fluctuating itemset importance and providing a flexible representation of relationships to determine student preferences. From fuzzy-related data, a genetic algorithm optimizes skill sets and creates individualized lesson plans considering each student’s competency and preferences for adjusting to personalized teaching tactics. Testing shows that fuzzy enhancement association rule mining for the PCL2T model improves student retention, PET (personalized teaching efficiency), minimal support and confidence update with fuzzy rules, and student involvement compared to other state-of-the-art methods. Students agree that tailored Chinese language and literary instruction is possible. The improvement results show fuzzy rules with minimum confidence levels of 50% to 100%, highly correlated in this model, student retention ratio of 96%, improved assessment grade of various language skills by 40 marks, PTE analysis of 93%, and student involvement ratio of 97%.
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18756883
|
AI
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10.1007/s44196-024-00680-9
|
Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction
|
In this study, the research presents the Federated Learning Enhanced Multi-Layer Perceptron (Fed-MLP) Long Short-Term Memory that is suggested by the research. The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learning in an endeavor to augment the predictive competency. In the first step, we gather stock market indices from various financial organizations, using CAC40 stocks as the index for the French stock market. To guarantee data consistency and quality, pre-processing methods including linear interpolation and Z-score normalization are used. There are two types of models for each of the three basic elements within the Fed-MLP–LSTM, namely, MLP for feature extraction and LSTM for sequence modeling. Institutionally, each refining institution trains a local MLP–LSTM on the corpus specific to their institution, with only the model parameters being transferred to a central server through Federated Learning. A global model is created and updated through repeated training and totaling of parameters while preserving privacy of the data going to each node. In the performance evaluation, quantitative measures like Root-Mean-Square Error (RMSE), and accuracy are seven used. Hypothesis testing shows that we have good evidence to support that the proposed Fed-MLP–LSTM outperforms the other methods with the lowest RMSE of 0. 0108 and 98.3% of accuracy with reference to their respective cocaine molecule target. The proposed method is implemented in python. This suggests that using Federated Learning along with MLP and LSTM as the components of this vector enhanced the function increasing its capacity and reliability in predicting the trends of stocks. In conclusion, the present study suggests a sound solution for effective and secure stock market forecasting in collaboration environments that can find its use in the financial domains and securities businesses.
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18756883
|
AI
|
10.3389/feduc.2024.1451504
|
Enhancing English as a Foreign Language (EFL) learning in Saudi Arabia: the academic contribution of YouTube in EFL learning and cultural awareness
|
The incorporation of technology in English as a Foreign Language (EFL) classroom has drastically transformed conventional learning models by offering innovative ways to boost students’ engagement and improve learning outcomes. This study investigates the impact of using YouTube as an educational tool to enhance EFL instruction in Saudi Arabia. A total of 200 EFL students were divided into two groups: An experimental group which used You Tube as a source of learning and another group which used normal curriculum. A mixed-methods approach was employed, combining quantitative data from a paired-sample t-test with qualitative feedback from students. The results for paired-sample t-test computations revealed a statistically significant difference (p = 0. 003) across the proficiency level indicating improvement in students’ speaking and listening skills at higher proficiency levels. Of the qualitative answers, motivational and participation enhancement were overemphasized. The research points that the integration of YouTube in EFL classrooms can lead to a remarkable increase in language learning especially in the learning area of listening and speaking abilities among the students. These results offer practical implications for educators seeking to integrate multimedia tools into their teaching strategies to promote more interactive and effective learning experiences.
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2504284X
|
EDUCATION
|
10.1186/s40594-024-00515-1
|
From cognitive coach to social architect: shifts in learning assistants’ valued practices
|
Learning assistants (LAs) are undergraduate students who serve as instructional assistants in STEM classrooms. In addition to engaging in active practice, they take a pedagogy seminar and regularly meet with a content instructor. While aspects of LAs’ pedagogical beliefs and actions have been investigated, there remains a gap in understanding how LAs make sense of their new instructional roles and how they negotiate between their experiences as students and their responsibilities as instructors. This study uses a sequential, exploratory mixed-methods approach, which includes constant comparative open-coding, thematic analysis, and epistemic network analysis, to analyze 178 reflections written by 89 LAs across five terms at two institutions. Here, we identify each LA’s expressed goals and intended actions at the start and the end of their first term as an LA. Using a community of practice framework, we seek to explicate the shifts in these LAs’ values as they become more central members of the LA community. LAs’ expressed roles shift from being cognitive coaches, where they focus on student thinking, sense-making, and understanding of disciplinary concepts, to being social architects, where their focus shifts to attending to the aspects of the environment that can support productive interactions for learning. A social architect prioritizes goals related to mutual trust, respect, & approachability, understanding and learning about students, and creating a sense of belonging. Similarly, their intended actions emphasize compassion, understanding, and facilitating group discussion. While all LAs studied exhibited this shift, it manifested in different ways and to different extents, as illustrated in detail by four selected cases. These cases illustrate how the shifts coupled to a change in language around teaching, becoming more specific and contextual. LAs express a shift in their valued practices over their first term as LAs related to their instructional role. The goal of student-centered instructional practice is often framed as becoming a better cognitive coach; however, this orientation does not foreground ideas around teaching practice that aim to foster engagement, belonging, and student agency. Implications for both the LA model and, more generally, for postsecondary STEM instructors are discussed.
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21967822
|
EDUCATION
|
10.3389/fonc.2024.1475860
|
Preoperative subjective impairments in language and memory in brain tumor patients
|
Background: Subjective reports can reveal relevant information regarding the nature of the impairment of brain tumor patients, unveiling potential gaps in current assessment practices. The co-occurrence of language and memory impairments has been previously reported, albeit scarcely. The aim of this study is therefore to understand the co-occurrence of subjective language and memory complaints in the preoperative state of brain tumor patients and its impact on Quality of Life (QoL).Methods: 31 brain tumor patients (12 LGG, 19 HGG) underwent a semi-structured interview to assess subjective complaints of language deficits, co-occurrences between language and memory dysfunction, and changes in QoL. Group and subgroup analyses were conducted to provide general and tumor grade specific data.Results: 48.4% of patients mentioned co-occurrence of language and memory impairments in reading, writing, and conversation. The HGG group reported co-occurrences in all three of these (reading: 31.6%; writing: 21.1%; conversation: 26.3%), while the LGG only described co-occurrences in reading (25%) and conversation (8.3%), although these were not statistically significant. All patients with co-occurring language and memory deficits reported these to be linked to reduced QoL (48.4%). In patients with an HGG, this number was slightly higher (52.6%) than in patients with an LGG (41.7%).Conclusion: Language impairments co-occur with memory dysfunction as perceived in patients’ daily life. Patients see these impairments as affecting their quality of life. Further attention to dedicated language and memory tasks seems necessary.
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2234943X
|
ONCOLOGY
|
10.3389/feduc.2024.1379755
|
How schoolchildren use digital media in class and outside of school over several weeks: a quantitative case study with media diaries
|
Introduction: Digital media play a central role in the lives of today’s schoolchildren, immersed in an increasingly digital world. Modern technologies blur the lines between formal school settings and informal settings outside of school. Although formats like bring-your-own-device align the use in the formal setting with informal usage, a disjunction exists between children’s interactions with digital technologies in their home environments and those within the educational setting. For bridging the gap between school learning and children’s lives outside of school, it is essential to explore the differences and similarities in media usage in both settings.Methods: In our case study, we examined schoolchildren’s motives and evaluations of digital media usage in both settings, addressing individual needs. Additionally, we explored several dimensions of digital literacy through self-assessment, identified associated learning opportunities within and outside the school environment, and captured self-reported learning gains. We collected this data over the course of several weeks in a longitudinal design with media diaries, aiming to estimate the extent of the fluctuation.Results: Eighty-four German schoolchildren aged between 10 and 16 years participated over a six-week period. We found differences but also similarities between media usage outside of school and in class. Digital media were less frequently used in class for entertainment, communication, and learning compared to outside of school, but no differences were reported regarding information search. Schoolchildren expressed above-average satisfaction with their media usage in both settings, but they perceived the usage of digital media outside of school as significantly more important than in class. Regarding their digital competencies, the schoolchildren displayed high self-confidence in most areas. Only in the areas of algorithms and programming, schoolchildren rated themselves as below average. While learning opportunities were identified in class and outside of school, the frequency of these opportunities varied across different digital skills. The self-reported learning gain in digital media usage remained consistently low in both settings. Across all analyses, there was no substantial temporal fluctuation in media usage over the study period.Discussion: The findings raise crucial considerations regarding the integration of digital media in the classroom, fostering a discussion on their implications for both research and educational practices.
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2504284X
|
EDUCATION
|
10.1186/s40359-024-02120-x
|
Relative victimization scale: initial development and retrospective reports of the impact on mental health
|
Bullying and victimization have been studied in many contexts and classified as peer victimization in school settings and parental or sibling victimization within family settings. Yet, current research is scarce on whether victimization occurring within family settings is specific to parental or sibling victimization. Thus, the current study aims to develop a scale assessing victimization conducted by relatives and provide support for its psychometric properties. Cross-sectional and longitudinal data were collected from university students (1622 and 1045 students, respectively) and participants responded to questionnaires via an online survey. EFA and CFA results demonstrated the unidimensionality of the Relative Victimization Scale (RVS) consisting of eight items. In terms of convergent validity, RVS scores were correlated with the scores on parental, sibling, and peer victimization scales and several psychological health outcomes including depression, anxiety, social anxiety, perceived stress, loneliness, negative and positive affect, life satisfaction, and resilience. Moreover, RVS explained a significant amount of variance beyond the contribution of parental, sibling, and peer victimization in those psychological health outcomes for the support of incremental validity. The findings of the study indicated the potential utility of the RVS in assessing the experience of relative victimization through offering support for internal consistency reliability and construct, longitudinal predictive, and incremental validity.
|
20507283
|
PSYCHOLOGY
|
10.3390/ai5040106
|
OTM-HC: Enhanced Skeleton-Based Action Representation via One-to-Many Hierarchical Contrastive Learning
|
Human action recognition has become crucial in computer vision, with growing applications in surveillance, human–computer interaction, and healthcare. Traditional approaches often use broad feature representations, which may miss subtle variations in timing and movement within action sequences. Our proposed One-to-Many Hierarchical Contrastive Learning (OTM-HC) framework maps the input into multi-layered feature vectors, creating a hierarchical contrast representation that captures various granularities within a human skeleton sequence temporal and spatial domains. Using sequence-to-sequence (Seq2Seq) transformer encoders and downsampling modules, OTM-HC can distinguish between multiple levels of action representations, such as instance, domain, clip, and part levels. Each level contributes significantly to a comprehensive understanding of action representations. The OTM-HC model design is adaptable, ensuring smooth integration with advanced Seq2Seq encoders. We tested the OTM-HC framework across four datasets, demonstrating improved performance over state-of-the-art models. Specifically, OTM-HC achieved improvements of 0.9% and 0.6% on NTU60, 0.4% and 0.7% on NTU120, and 0.7% and 0.3% on PKU-MMD I and II, respectively, surpassing previous leading approaches across these datasets. These results showcase the robustness and adaptability of our model for various skeleton-based action recognition tasks.
|
26732688
|
AI
|
10.3390/ai5040107
|
Dynamic Multiobjective Optimization Based on Multi-Environment Knowledge Selection and Transfer
|
Background: Dynamic multiobjective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, and dynamic multiobjective algorithms (DMOAs) aim to find Pareto optima that are closer to the real one in the new environment as soon as possible. In particular, the introduction of transfer learning in DMOAs has led to good results in solving DMOPs. However, the selection of valuable historical knowledge and the mitigation of negative transfer remain important problems in existing transfer learning-based DMOAs. Method: A DMOA based on multi-environment knowledge selection and transfer (MST-DMOA) is proposed in this article. First, by clustering historical Pareto optima, some representative solutions that can reflect the main evolutionary information are selected as knowledge of the environment. Second, the similarity between the historical and current environments is evaluated, and then the knowledge of multiple similar environments is selected as valuable historical knowledge to construct the source domain. Third, solutions with high quality in the new environment are obtained to form the target domain, which can better help historical knowledge to adapt to the current environment, thus effectively alleviating negative transfer. Conclusions: We compare the proposed MST-DMOA with five state-of-the-art DMOAs on fourteen benchmark test problems, and the experimental results verify the excellent performance of MST-DMOA in solving DMOPs.
|
26732688
|
AI
|
10.3389/fonc.2024.1437140
|
The role of systemic immune-inflammation index in predicting pathological complete response of breast cancer after neoadjuvant therapy and the establishment of related predictive model
|
Objective: To investigate the role of systemic immune-inflammation index (SII) in complete pathological response (pCR) of breast cancer patients after neoadjuvant chemotherapy, and to establish and validate a nomogram for predicting pCR.Methods: Breast cancer patients were selected from the First Affiliated Hospital of Xi’an Jiaotong University from January 2020 to December 2023. The optimal cut-off value of SII was calculated via ROC curve. The correlation between SII and clinicopathological characteristics was analyzed by Chi-square test. Logistic regression analysis was performed to evaluate the factors that might affect pCR. Based on the results of Logistic regression analysis, a nomogram for predicting pCR was established and validated.Results: A total of 112 breast cancer patients were included in this study. 33.04% of the patients achieved pCR after neoadjuvant therapy. Chi-square test showed that SII was significantly correlated with pCR (P=0.001). Logistic regression analysis suggested that Ki-67 (P=0.039), therapy cycle (P<0.001), CEA (P=0.025) and SII (P=0.019) were independent predictors of pCR after neoadjuvant chemotherapy. A nomogram based on Ki-67, therapy cycle, CEA and SII showed a good predictive ability.Conclusion: Ki-67, therapy cycle, CEA and SII were independent predictors of pCR of breast cancer after neoadjuvant chemotherapy. The nomogram based on the above positive factors showed a good predictive ability.
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2234943X
|
ONCOLOGY
|
10.1186/s40594-024-00513-3
|
Leveraging professional learning communities in linking digital professional development and instructional integration: evidence from 16,072 STEM teachers
|
Background: Integration of digital tools and resources in STEM instruction has garnered significant attention due to its high potential. Digital professional development is identified as a pivotal factor for equipping teachers with necessary digital skills to effectively orchestrate digital resources. Notably, the role of professional learning communities is considered critical. However, the intricate relationships among digital professional development, professional learning communities, and digital instructional integration among STEM teachers remain underexplored. Utilizing partial least-squares–structural equation models (PLS–SEM), the present study examined links in digital professional development, professional learning communities, and digital instructional integration among STEM teachers (N = 16,072) who participated in the Programme for International Student Assessment (PISA) 2022. Results: Findings from the PLS–SEM analysis indicate that digital professional development exhibits a direct positive relationship with professional learning communities and digital instructional integration. Relatedly, professional learning communities is positively correlated with digital instructional integration. In terms of indirect effect, findings show that professional learning communities play a significant positive mediating role in linking digital professional development and digital instructional integration. Conclusions: This study reports new evidence on the influence of digital professional development on digital instructional integration through professional learning communities among 16,072 STEM teachers and concludes that, when STEM teachers regularly immerse themselves in professional learning communities, they are more likely to benefit from their digital professional development by integrating digital technologies in classroom instruction. Policymakers and educational leaders should consider promoting digital professional development and professional learning communities among STEM teachers, along with efforts to encourage digital instructional integration.
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21967822
|
EDUCATION
|
10.1186/s40359-024-02116-7
|
Adaptation and validation of the Parents’ Self-stigma Scale into Turkish and its association with parenting stress and parental self-efficacy
|
In the present era, parents frequently stigmatize themselves for their children’s negative behaviors and inadequate social skills. Parents’ self-stigma (PSS) may lead to a decrease in parental self-efficacy and quality of marital and family life. In light of these reasons, the principal objective of this study to assess the validity and reliability of the Turkish version of the PSS Scale (PSSS) as developed by Eaton et al. (2019) and to investigate the indirect effect that parenting stress has on the relationship between PSS and parental self-efficacy. We collected data from a total of 1,118 parents via random sampling, with the first part of the study involving 645 participants (Mage = 32.64 ± 7.28) and the second part of the study involving 473 participants (Mage = 27.43 ± 9.87). In the first part of the study, we employed structural equation modeling for the confirmatory factor analysis and Pearson’s correlation coefficient for the criterion-related validity, average variance extracted, and composite reliability analyses. Moreover, we calculated Cronbach’s alpha, McDonald’s omega, and Guttman split-half coefficients for the reliability analyses. In the second part of the study, we utilized Hayes’ bootstrapping method to assess the indirect effect of parenting stress on the relationship between PSS and parental self-efficacy. The first part of the study confirms the PSSS’s 11-item, 3-factor structure, showing the Turkish form to have acceptable goodness-of-fit indices, and found Cronbach’s alpha for the PSSS to be 0.89. Furthermore, the first part of the study demonstrates a significant negative correlation between marital life satisfaction and PSS. Meanwhile, the second part of the study has determined PSS to be positively related to parenting stress and negatively related to parental self-efficacy. The second part of the study also indicates parenting stress to have an indirect effect on the association between PSS and parental self-efficacy. The study indicates the Turkish version of the PSSS to be a valid and reliable instrument in Turkish culture for measuring parents’ PSS levels regarding their children, with higher scores indicating greater PSS. The scale can be effectively used in both research and clinical settings. The study also suggests parental stress to have a possible impact on the association between PSS and parental self-efficacy. Furthermore, addressing the variables of PSS and parenting stress in family-focused interviews and therapeutic interventions may contribute to increasing parental self-efficacy.
|
20507283
|
PSYCHOLOGY
|
10.1007/s44196-024-00672-9
|
Enhancing Expert Decision-Making for Wastewater Treatment Plants with Seidel Laplacian Energy and Cosine Similarity Measure in Intuitionistic Fuzzy Graphs
|
Wastewater treatment facilities’ main goal is to protect the public and environment from the hazardous and poisonous materials found in wastewater. Water treatment facilities were developed to speed up the natural process of cleansing water. A novel cosine similarity measure across intuitionistic fuzzy graphs has been proven to be more effective than certain present ones in group decision-making issues using example verification. This paper provides a unique approach for calculating expert-certified, well-known scores by finding the ambiguous information of intuitionistic fuzzy preference relations as well as the regular cosine similarity grades from one separable intuitionistic fuzzy preference relation to another. The new technique considers both "objective" and "subjective" information provided by experts. Using intuitionistic fuzzy preference relations, we provide workable techniques for judging experts’ eligible reputational ratings. This can be used to raise or decrease the relevance of the stated criteria in an evaluation that takes into account several competing elements. We give a solution to a decisional problem by using two effective methods: the newly constructed cosine similarity measure and the Seidel Laplacian energy (SLe+) of an intuitionistic fuzzy graph. Finally, two working procedures and circumstances are offered to show the effectiveness and superiority of the proposed techniques.
|
18756883
|
AI
|
10.1007/s44196-024-00678-3
|
Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms
|
Diabetes mellitus is considered one of the main causes of death worldwide. If diabetes fails to be treated and diagnosed earlier, it can cause several other health problems, such as kidney disease, nerve disease, vision problems, and brain issues. Early detection of diabetes reduces healthcare costs and minimizes the chance of serious complications. In this work, we propose an e-diagnostic model for diabetes classification via a machine learning algorithm that can be executed on the Internet of Medical Things (IoMT). The study uses and analyses two benchmarking datasets, the PIMA Indian Diabetes Dataset (PIDD) and the Behavioral Risk Factor Surveillance System (BRFSS) diabetes dataset, to classify diabetes. The proposed model consists of the random oversampling method to balance the range of classes, the interquartile range technique-based outlier detection to eliminate outlier data, and the Boruta algorithm for selecting the optimal features from the datasets. The proposed approach considers ML algorithms such as random forest, gradient boosting models, light gradient boosting classifiers, and decision trees, as they are widely used classification algorithms for diabetes prediction. We evaluated all four ML algorithms via performance indicators such as accuracy, F1 score, recall, precision, and AUC-ROC. Comparative analysis of this model suggests that the random forest algorithm outperforms all the remaining classifiers, with the greatest accuracy of 92% on the BRFSS diabetes dataset and 94% accuracy on the PIDD dataset, which is greater than the 3% accuracy reported in existing research. This research is helpful for assisting diabetologists in developing accurate treatment regimens for patients who are diabetic.
|
18756883
|
AI
|
10.3389/feduc.2024.1378493
|
Using an app-based screening tool to predict deficits in written word spelling at school entry
|
Introduction: The first year of schooling is crucial for the further development of spelling abilities in children, which makes early assessment and intervention essential. The aim of this study was to develop and validate an efficient and cost-free screening tool for identifying spelling problems in community school settings around the time of school entry.Methods: A broad range of precursors of spelling (vocabulary, grammar, letter knowledge, phonological awareness, phonological working memory, rapid automatized naming) were assessed in 522 Austrian first graders (6–7 years of age) in the first weeks of schooling. At the end of first grade, spelling abilities were assessed by newly developed spelling tasks based on the trochaic foot. By applying logistic regression with the least absolute shrinkage and selection operator (LASSO), we aimed to select a set of important predictors of spelling problems at the end of grade 1 (i.e., scoring below the 16th percentile in the spelling test).Results: Our analysis identified letter knowledge (i.e., an aspect of phonological information processing) and sentence repetition (i.e., a measure of grammatical knowledge) as important predictors of spelling problems. The screening tool has acceptable diagnostic accuracy [area under the curve (AUC) = 0.0.725 and DeLong 95% CI (0.666, 0.784)]. Further analyses indicated that the AUC differs neither between boys and girls nor between children with and without German as their first language.Discussion: These results suggest that administering the screening tool during the first weeks of schooling is a valid approach to identifying spelling deficits, which in turn enables early targeted pedagogical interventions. Practical implications for spelling instructions are discussed.
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2504284X
|
EDUCATION
|
10.3390/ai5040109
|
Adaptive Exploration Artificial Bee Colony for Mathematical Optimization
|
The artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landscapes. This article introduces the adaptive exploration artificial bee colony (AEABC), a novel variant that reinspires the ABC algorithm based on real-world phenomena. AEABC incorporates new distance-based parameters and mechanisms to correct the original design, enhancing its robustness. The performance of AEABC was evaluated against 33 state-of-the-art metaheuristics across twenty-five benchmark functions and an engineering application. AEABC consistently outperformed its counterparts, demonstrating superior efficiency and accuracy. In a variable-sized problem (n = 10), the traditional ABC algorithm converged to 3.086 × 106, while AEABC achieved a convergence of 2.0596 × 10−255, highlighting its robust performance. By addressing the shortcomings of the traditional ABC algorithm, AEABC significantly advances mathematical optimization, especially in engineering applications. This work underscores the significance of the inspiration of the traditional ABC algorithm in enhancing the capabilities of swarm intelligence.
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26732688
|
AI
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10.3390/ai5040111
|
Enhancing Medical Image Classification with Unified Model Agnostic Computation and Explainable AI
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Background: Advances in medical image classification have recently benefited from general augmentation techniques. However, these methods often fall short in performance and interpretability. Objective: This paper applies the Unified Model Agnostic Computation (UMAC) framework specifically to the medical domain to demonstrate its utility in this critical area. Methods: UMAC is a model-agnostic methodology designed to develop machine learning approaches that integrate seamlessly with various paradigms, including self-supervised, semi-supervised, and supervised learning. By unifying and standardizing computational models and algorithms, UMAC ensures adaptability across different data types and computational environments while incorporating state-of-the-art methodologies. In this study, we integrate UMAC as a plug-and-play module within convolutional neural networks (CNNs) and Transformer architectures, enabling the generation of high-quality representations even with minimal data. Results: Our experiments across nine diverse 2D medical image datasets show that UMAC consistently outperforms traditional data augmentation methods, achieving a 1.89% improvement in classification accuracy. Conclusions: Additionally, by incorporating explainable AI (XAI) techniques, we enhance model transparency and reliability in decision-making. This study highlights UMAC’s potential as a powerful tool for improving both the performance and interpretability of medical image classification models.
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26732688
|
AI
|
10.3389/frai.2024.1477447
|
Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor
|
Background: The algorithmic patient preference predictor (PPP) has been proposed to aid in decision making for incapacitated patients in the absence of advanced directives. Ethical and legal challenges aside, multiple practical barriers exist for building a personalized PPP. Here, we examine previous work using machine learning to predict patient reported outcome measures (PROMs) for capacitated patients undergoing diverse procedures, therapies, and life events. Demonstrating robust performance in predicting PROMs for capacitated patients could suggest opportunities for developing a model tailored to incapacitated ones.Methods: We performed a scoping review of PubMed, Embase, and Scopus using the PRISMA-ScR guidelines to capture studies using machine learning to predict PROMs following a medical event alongside qualitative studies exploring a theoretical PPP.Results: Sixty-eight studies used machine learning to evaluate PROMs; an additional 20 studies focused on a theoretical PPP. For PROMs, orthopedic surgeries (n = 33) and spinal surgeries (n = 12) were the most common medical event. Studies used demographic (n = 30), pre-event PROMs (n = 52), comorbidities (n = 29), social determinants of health (n = 30), and intraoperative variables (n = 124) as predictors. Thirty-four different PROMs were used as the target outcome. Evaluation metrics varied by task, but performance was overall poor to moderate for the best reported scores. In models that used feature importance, pre-event PROMs were the most predictive of post-event PROMs. Fairness assessments were rare (n = 6). These findings reinforce the necessity of the integrating patient values and preferences, beyond demographic factors, to improve the development of personalized PPP models for incapacitated patients.Conclusion: The primary objective of a PPP is to estimate patient-reported quality of life following an intervention. Use of machine learning to predict PROMs for capacitated patients introduces challenges and opportunities for building a personalized PPP for incapacitated patients without advanced directives.
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26248212
|
AI
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10.1007/s44196-024-00670-x
|
ExAq-MSPP: An Energy-Efficient Mobile Sink Path Planning Using Extended Aquila Optimization Algorithm
|
Wireless sensor networks play a crucial role in gathering data from remote or hard-to-reach locations, enabling real-time monitoring and decision-making in a wide range of industries and applications. The mobile sink path planning (MSPP) enables mobile sinks (e.g., drones or rovers) to navigate through the environment, collecting data from different sensor nodes, ensuring comprehensive coverage, and adaptively addressing changing conditions. Still, the energy-efficient routing with minimal delay is the challenging aspect. This research focuses on improving data gathering in wireless sensor networks by introducing an efficient routing protocol. In this proposed protocol, sensor nodes are initially deployed using Voronoi diagrams to ensure uniform network coverage. The network is then divided into clusters using the low-energy adaptive clustering hierarchy (LEACH) algorithm for energy-efficient routing. To optimize the path planning of a mobile sink for data collection, we introduce the extended Aquila (ExAq) optimization algorithm, which uses a multi-objective fitness function considering factors such as delay, residual energy, link quality, priority, and distance. Simulation results demonstrate the effectiveness of the proposed ExAq-MSPP protocol in terms of reduced delay, improved network lifetime, higher packet delivery ratio, enhanced residual energy, and increased throughput compared to existing protocols with the values of 1.169, 99.857, 99.920, 0.997, and 255.306, respectively. Thus, the energy-efficient routing and optimizing path planning for mobile sinks, the proposed ExAq-MSPP protocol can extend network lifetime, increase data accuracy, and provide more robust performance under changing environmental conditions.
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18756883
|
AI
|
10.3389/fpsyg.2024.1485278
|
The sorrow comes when I’m having moments of joy—experiences of parenting a live baby following a previous stillbirth: an interpretative phenomenological analysis
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Stillbirth can lead to complex and varied psychological outcomes for parents. Many choose to have another pregnancy following a stillbirth; however, little is known about the experience of parenting and bonding with the subsequent baby. Couples, who were the biological parents of a stillborn baby and at least one subsequent live baby aged under five, were recruited and interviewed individually. Data were analysed using interpretative phrenomenological analysis. Twelve individual interviews (of six couples) were conducted and four themes with nine subthemes were developed. Theme 1 “Back to the starting line: pregnancy as a means to an end” captured parents’ desire to bring a live baby home with pregnancy being experienced alongside fear, trauma, and grief. Theme 2 “Reality hits” encapsulated the experience of arriving home and feeling overwhelmed by the demands of a new-born baby. Theme 3 “Being a living and loss parent” captured the experience of being a parent to both a living and non-living baby with conflicting emotions. Theme 4 “Protection: ‘I need him there next to me, so I know he’s alive’” represented the fear some parents felt when parenting their live baby and included parents’ strategies to manage this anxiety. This study presents novel insight into the complexities of being a parent to a stillborn baby in tandem with a live baby, with difficulties arising in bonding, and managing emotional distress linked to trauma and grief. Potential implications for care includes a need for increased training for professionals providing postnatal care.
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16641078
|
PSYCHOLOGY
|
10.3390/cancers16223773
|
The Efficacy of a Lower Dose of Everolimus in Patients with Advanced Neuroendocrine Tumors
|
Background: Everolimus at 10 mg daily is approved to treat patients with advanced grade 1/2 neuroendocrine tumors (NETs), although it may lead to significant toxicity. Grade 3 or higher drug-related adverse events and drug discontinuation occur in approximately one-fourth of cases. However, phase I trials have demonstrated that doses from 5 mg daily efficiently inhibit NET cell signaling. Objectives and Methods: This multicenter retrospective study compared the time to treatment failure (TTF) in patients with NETs who received a mean daily dose of 7–10 mg (higher dose [HD]) or ≤6 mg (lower dose [LD]) of everolimus. Results: Ninety-two patients were included: 74 (80%) in the HD group and 18 (20%) in the LD group. At a median follow-up of 4.2 years, the median time to treatment failure (TTF) was 9.2 months for the HD and 7.2 months for the LD groups (p = 0.85). The TTF did not significantly differ between the LD and the HD groups (HR: 1.24; 95% CI: 0.68–2.25; p = 0.47), even after adjusting for age at treatment initiation, the NET grade, and the treatment line. Conclusion: Everolimus doses from 5 to 6 mg/day seem to be equally as effective as higher doses, but lower doses are potentially associated with less toxicity and lower costs. These findings support validation through a randomized clinical trial.
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20726694
|
ONCOLOGY
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10.3389/frai.2024.1453847
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Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN
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Mural paintings are vital cultural expressions, enriching our lives by beautifying spaces, conveying messages, telling stories, and evoking emotions. Ancient temple murals degrade over time due to natural aging, physical damage, etc. Preserving these cultural treasures is challenging. Image inpainting is often used for digital restoration, but existing methods typically overlook naturally degraded areas, using randomly generated binary masks or small, narrow regions for repair. This study proposes a novel architecture to reconstruct large areas of naturally degraded murals, maintaining intrinsic details, avoiding color bias, and preserving artistic excellence. The architecture integrates generative adversarial networks (GANs) and the diffusion model, including a whole structure formation network (WSFN), a semantic color network (SCN), and a diffusion mixture distribution (DIMD) discriminator. The WSFN uses the original image, a line drawing, and an edge map to capture mural details, which are then texturally inpainted in the SCN using gated convolution for enhanced results. Special attention is given to globally extending the receptive field for large-area inpainting. The model is evaluated using custom-degraded mural images collected from Tamil Nadu temples. Quantitative analysis showed superior results than state-of-the-art methods, with SSIM, MSE, PSNR, and LPIPS values of 0.8853, 0.0021, 29.8826, and 0.0426, respectively.
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26248212
|
AI
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10.1186/s40594-024-00516-0
|
Employing automatic analysis tools aligned to learning progressions to assess knowledge application and support learning in STEM
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We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed. However, these tasks are time-consuming and expensive to score and provide feedback for. Artificial intelligence (AI) allows to validate the LPs and evaluate performance assessments for many students quickly and efficiently. The evaluation provides a report describing student progress along LP and the supports needed to attain a higher LP level. We suggest using unsupervised, semi-supervised ML and generative AI (GAI) at early LP validation stages to identify relevant proficiency patterns and start building an LP. We further suggest employing supervised ML and GAI for developing targeted LP-aligned performance assessment for more accurate performance diagnosis at advanced LP validation stages. Finally, we discuss employing AI for designing automatic feedback systems for providing personalized feedback to students and helping teachers implement LP-based learning. We discuss the challenges of realizing these tasks and propose future research avenues.
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21967822
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EDUCATION
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10.3390/educsci14111233
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Assessing Student Teachers’ Motivation and Learning Strategies in Digital Inquiry-Based Learning
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Over the past two decades, teachers have adopted several teaching and learning strategies for motivating students to learn chemistry. Learning chemistry in context enables students to develop richer crosscutting learning experiences relevant to contributing to solving problems. A qualitative case study method was adopted to examine student teachers’ experiences in digital inquiry-based learning. Questionnaires with closed-ended and open-ended questions were used to evaluate student teachers’ motivational orientations and learning strategies during a general chemistry course for one month. The results show that student teachers utilized varied perspectives such as self-efficacy, task value, and intrinsic goals to elaborate their learning for knowledge construction and application when performing collaborative tasks. The approach enables students to receive maximum support and feedback from instructors who use pedagogical styles to self-direct them during class discussions, which enhances their active participation in learning with the learning materials. The findings provide a practical insight into instructional strategies in delivering chemistry concepts when students are motivated to use and adopt varied learning strategies.
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22277102
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EDUCATION
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10.1186/s40359-024-02139-0
|
The effectiveness of multimedia mental health self-care program based on cyber space on the mental health of infertile women: a randomized controlled trial
|
So far, some training interventions have been carried out to improve the mental health in women with infertility, but designing the need and evidence-based, as well as multimedia mental health self-care interventions based on cyber space has received less attention. Due to the spread of the internet and the role of self-care in improving mental disorders, this study was conducted to evaluate the effectiveness of the multimedia mental health self-care program on mental health and to assess the users' satisfaction. This study is a randomized controlled trial with pretest–posttest follow-up design. The sample was selected using a convenience sampling method (n = 90). The random number function was used to assign random numbers. The research instruments include a demographic, psychological Well-being, depression, anxiety, perceived stress, fertility problems and satisfaction with training questionnaire. Six weeks of intervention was conducted following the pre-test and the link of each session's content was sent to the participants, based on the training schedule, through Eitaa Messenger. The post-test and follow-up were conducted 1 week and 1 month post intervention. The data were analyzed using independent t-test and repeated measures ANOVA. A statistically significant difference was observed between the intervention and control group in the mean score of psychological well-being, perceived stress and infertility stress 1 week and 1 month post intervention and in the mean score of depression and anxiety 1 month post intervention. The intervention group scored higher than the control in psychological well-being but lower in perceived stress, depression, anxiety and infertility stress. The intervention had a positive effect and reduced the score of perceived stress, depression, anxiety and infertility in the intervention group over time. The score reduction continued until the follow-up stage. No significant time-interaction effect was observed on psychological well-being and on the control group. Satisfaction with the program and subscales was desirable. This program could significantly reduce the depression, anxiety, perceived stress and infertility stress and desirable satisfaction with the program was observed among users. This program can be used in designing the experimental and therapeutic interventions to improve mental-health self-care behaviors. RCT Registry: Iranian Registry of Clinical Trials; RCT registration number: IRCT20210526051410N1; Registration date: 2022–11-06. Last update: 2023–01-28
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20507283
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PSYCHOLOGY
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10.3390/ai5040116
|
SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms
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As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environments, focusing on the interpretation of ML models. SIBILA simplifies model development by allowing users to set objectives and preferences before automating the search for optimal ML pipelines. Unlike traditional AutoML frameworks, SIBILA is specifically designed to exploit the computational capabilities of HPC platforms, thereby accelerating the model search and evaluation phases. The emphasis on interpretability is particularly crucial when model transparency is mandated by regulations or desired for stakeholder understanding. SIBILA has been validated in different tasks with public datasets. The results demonstrate that SIBILA consistently produces models with competitive accuracy while significantly reducing computational overhead. This makes it an ideal choice for practitioners seeking efficient and transparent ML solutions on HPC infrastructures. SIBILA is a major advancement in AutoML, addressing the rising demand for explainable ML models on HPC platforms. Its integration of interpretability constraints alongside automated model development processes marks a substantial step forward in bridging the gap between computational efficiency and model transparency in ML applications. The tool is available as a web service at no charge.
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26732688
|
AI
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10.3390/ai5040117
|
Evaluating Anomaly Explanations Using Ground Truth
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The widespread use of machine and deep learning algorithms for anomaly detection has created a critical need for robust explanations that can identify the features contributing to anomalies. However, effective evaluation methodologies for anomaly explanations are currently lacking, especially those that compare the explanations against the true underlying causes, or ground truth. This paper aims to address this gap by introducing a rigorous, ground-truth-based framework for evaluating anomaly explanation methods, which enables the assessment of explanation correctness and robustness—key factors for actionable insights in anomaly detection. To achieve this, we present an innovative benchmark dataset of digital circuit truth tables with model-based anomalies, accompanied by local ground truth explanations. These explanations were generated using a novel algorithm designed to accurately identify influential features within each anomaly. Additionally, we propose an evaluation methodology based on correctness and robustness metrics, specifically tailored to quantify the reliability of anomaly explanations. This dataset and evaluation framework are publicly available to facilitate further research and standardize evaluation practices. Our experiments demonstrate the utility of this dataset and methodology by evaluating common model-agnostic explanation methods in an anomaly detection context. The results highlight the importance of ground-truth-based evaluation for reliable and interpretable anomaly explanations, advancing both theory and practical applications in explainable AI. This work establishes a foundation for rigorous, evidence-based assessments of anomaly explanations, fostering greater transparency and trust in AI-driven anomaly detection systems.
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26732688
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AI
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10.3389/fpsyg.2024.1408929
|
Speech characteristics that differentiate stuttering and cluttering in Japanese speakers
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Background: Cluttering is a speech disorder distinct from stuttering. Despite this distinction, there is no established method to clearly differentiate the two disorders. This study aimed to use objective criteria to differentiate cluttering from stuttering in Japanese speakers.Methods: Participants were 32 consecutive native-Japanese speakers who visited the Keio University Hospital between July 2020 and January 2023 with a chief complaint of speech disfluency. One physician and two speech-language-hearing therapists concurred on a stuttering or cluttering diagnosis of the 32 patients based on recordings of the Kitsuon kensa-ho test. The frequencies of stuttering-like disfluencies (SDF) and normal disfluencies (NDF) were calculated from the Kitsuon kensa-ho, and the ratio of disfluencies (RDF) was calculated as the ratio of SDF to NDF. Differences between the cluttering and stuttering groups in the RDF and the mean articulatory rate (MAR) for oral reading and a monologue task were tested using the Mann–Whitney U test. ROC curves were used to determine the sensitivity and specificity that well-distinguished subjects with cluttering from those with stuttering; the experts’ diagnosis was the gold standard.Results: Of the 32 participants, 12 (38%) were diagnosed with cluttering and 20 (62%) with stuttering. The cluttering and stuttering groups were comparable in demographic characteristics. The RDF on monologue task had the highest sensitivity in diagnosing cluttering, and the MAR on monologue task had the highest specificity. Adopting provisional criteria of a monologue RDF greater than 1.2 and a monologue MAR greater than 7.5 produced a sensitivity of 0.92 and a specificity of 0.95.Conclusion: We conclude that combining monologue RDF and monologue MAR well-distinguished cluttering from stuttering. This method provides new objective diagnostic criteria, which can aid clinicians, therapists, and basic researchers.
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16641078
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PSYCHOLOGY
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10.3390/ai5040118
|
Learning and Evolution: Factors Influencing an Effective Combination
|
(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2) Methods: In this paper, the author investigates whether combining learning and evolution permits finding better solutions than those discovered by evolution alone. In further detail, the author presents a series of empirical studies that highlight some specific conditions determining the success of such combination. Results are obtained in five qualitatively different domains: (i) the 5-bit parity task, (ii) the double-pole balancing problem, (iii) the Rastrigin, Rosenbrock and Sphere optimization functions, (iv) a robot foraging task and (v) a social foraging problem. Moreover, the first three tasks represent benchmark problems in the field of evolutionary computation. (3) Results and discussion: The outcomes indicate that the effect of learning on evolution depends on the nature of the problem. Specifically, when the problem implies limited or absent agent–environment conditions, learning is beneficial for evolution, especially with the introduction of noise during the learning and selection processes. Conversely, when agents are embodied and actively interact with the environment, learning does not provide advantages, and the addition of noise is detrimental. Finally, the absence of stochasticity in the experienced conditions is paramount for the effectiveness of the combination. Furthermore, the length of the learning process must be fine-tuned based on the considered task.
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26732688
|
AI
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10.3390/ai5040119
|
Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions
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Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems.
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26732688
|
AI
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10.3389/frai.2024.1457299
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The impact of AI on education and careers: What do students think?
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Introduction: Providing one-on-one support to large cohorts is challenging, yet emerging AI technologies show promise in bridging the gap between the support students want and what educators can provide. They offer students a way to engage with their course material in a way that feels fluent and instinctive. Whilst educators may have views on the appropriates for AI, the tools themselves, as well as the novel ways in which they can be used, are continually changing.Methods: The aim of this study was to probe students' familiarity with AI tools, their views on its current uses, their understanding of universities' AI policies, and finally their impressions of its importance, both to their degree and their future careers. We surveyed 453 psychology and sport science students across two institutions in the UK, predominantly those in the first and second year of undergraduate study, and conducted a series of five focus groups to explore the emerging themes of the survey in more detail.Results: Our results showed a wide range of responses in terms of students' familiarity with the tools and what they believe AI tools could and should not be used for. Most students emphasized the importance of understanding how AI tools function and their potential applications in both their academic studies and future careers. The results indicated a strong desire among students to learn more about AI technologies. Furthermore, there was a significant interest in receiving dedicated support for integrating these tools into their coursework, driven by the belief that such skills will be sought after by future employers. However, most students were not familiar with their university's published AI policies.Discussion: This research on pedagogical methods supports a broader long-term ambition to better understand and improve our teaching, learning, and student engagement through the adoption of AI and the effective use of technology and suggests a need for a more comprehensive approach to communicating these important guidelines on an on-going basis, especially as the tools and guidelines evolve.
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26248212
|
AI
|
10.1007/s00432-024-06009-5
|
CT-defined muscle density as a prognostic factor in multiple myeloma undergoing autologous stem cell therapy: a retrospective single center study
|
Purpose: Skeletal muscle quality assessment can be performed by cross-sectional imaging. Skeletal muscle density (SMD) identified to be of prognostic relevance of several clinically outcomes in patients with hematological diseases. The purpose of the present study was to establish the effect of SMD on overall survival (OS) and progression-free survival (PFS) in patients with multiple myeloma (MM). Methods: All patients with MM were retrospectively analyzed between 2009 and 2019. 127 patients were included into the analysis. Whole-body computed tomography (CT) was used to calculate skeletal muscle index (SMI), SMD, albumin-gauge score and intramuscular adipose tissue content (IMAC). Results: Overall, 28 patients (22.0%) of the patient sample died. In the discrimination analysis muscle density was higher in non-survivors compared to survivors (mean 30.8 ± 12.5 versus 24.1 ± 15.8, p = 0.03) and IMAC was lower in non-survivors (− 0.66 ± 1.8 versus − 0.25 ± 0.21, p = 0.01). These differences, however, were not demonstrated in the logistic regression analysis, which could not show prognostic relevance for the investigated muscle density parameters on PFS or OS. Conclusion: CT-defined muscle density parameters have no prognostic relevance on survival in patients with MM undergoing autologous stem cell therapy, which was demonstrated in a comprehensive analysis. These results corroborate previous smaller studies that body composition might have a limited role in this tumor entity.
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14321335
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ONCOLOGY
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10.1186/s40359-024-02190-x
|
The effect of questioning gender stereotype threat on girl’s standing long jump performance
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Over the past few years, the sport psychology literature has established that gender stereotype threat (ST) is one of the factors that can impair girls’ performance. However, few studies have attempted to annihilate these negative effects. The purpose of the current study was to investigate whether questioning gender ST can mitigate the classical decline in girls’ standing long jump (SLJ) performance. The participants were 120 girls (Mage = 10.74 ± 0.85 years), selected through convenience sampling and randomly assigned to three groups: the gender ST group (n = 40), the questioning group (n = 40), and the control group (n = 40). For all groups, baseline performance (i.e., SLJ) was measured by a female researcher following a warm-up period. In the experimental phase, the control group repeated the baseline conditions; the gender ST group completed the same test but was evaluated by a male examiner (i.e., implicit stereotype induction), while participants in the questioning group were assessed after receiving questioning statements while performing the task in front of a male examiner. The results of the present study showed that the induction of a gender ST leads to a decrease in SLJ in girls. Additionally, if these inducing conditions of gender ST are accompanied by a questioning condition, the negative effects of gender ST can be reduced, and SLJ in girls does not decline. Based on our findings, this intervention is recommended as a simple, inexpensive, and quick solution for mitigating the negative effects of gender ST on girl’s motor performance.
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20507283
|
PSYCHOLOGY
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10.1186/s40359-024-02193-8
|
The effects of digital CBT intervention on attentional bias and sleep quality of poor sleepers with insomnia symptoms
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Attentional bias is a salient manifestation of insomnia. Digital cognitive therapy for insomnia (dCBT-I) has been validated as effective in alleviating this cognitive dysfunction. However, the effect of dCBT-I on attentional bias among Chinese individuals with insomnia remains undiscussed. This research sought to investigate this effect via a pictorial dot-probe task. In Study 1, the pattern of attentional bias among poor sleepers (N = 52) and normal sleepers (N = 56) was assessed by the dot-probe task. In study 2, dCBT-I and conventional education were received by the experimental group (N = 42) and control group (N = 25), respectively. The dot-probe tasks and sleep quality assessments were completed at baseline and post-test. The results of Study 1 indicated that poor sleepers exhibited significant attentional bias, characterized by increased attentional vigilance. Compared to normal sleepers, they showed heightened attentional vigilance toward sleep-related cues. The results of Study 2 showed that both dCBT-I and conventional education led to improvements in PSQI scores. However, only dCBT-I training alleviated attentional vigilance toward sleep-related cues. Additionally, dCBT-I was uniquely effective in reducing feelings of fatigue. Poor sleepers had a significant attentional bias, marked by heightened vigilance toward sleep-related cues. Digital CBT-I effectively reduced attentional vigilance and fatigue, suggesting that dCBT-I targets the cognitive distortions associated with insomnia. ChiCTR2100053172 (registered 13/11/2021).
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20507283
|
PSYCHOLOGY
|
10.3390/ejihpe14110192
|
Examining the Factor Structure and Validity of the Depression Anxiety Stress Scale-21
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Background: The prevalence of mental health disorders calls for valid and reliable instruments that are easy to administer and assess for clinicians and researchers. The Depression Anxiety Stress Scale-21 (DASS-21) is a commonly used instrument to assess psychological distress; however, model fit and internal reliability issues have been reported. Our objective was to assess the factorial and structural validity of the DASS-21. Methods: A confirmatory factor analysis (CFA) was conducted on the full sample (n = 1036) to assess the proposed three-factor DASS-21 using a priori cut-off values. Because model fit indices were not met, an exploratory factor analysis (EFA) was conducted to identify a parsimonious model. The resulting three-factor structure (i.e., DASS-9) was then assessed using CFA and multigroup invariance testing procedures. Results: The proposed three-factor DASS-21 did not meet model fit criteria. The DASS-9 did meet recommended model fit criteria and was invariant between sex, injury status, mental health diagnosis, and activity level groups. Statistically different group means were found between mental health diagnosis and activity level groups, while no differences between sex or injury status groups were found. Conclusions: The current study provides support to use a condensed DASS-21 instrument, such as the DASS-9. Future research is necessary to establish the DASS-9 prior to its adoption in research and clinical practice. Additionally, there is a need to identify and review all condensed versions of the DASS-21, so individuals know which instrument can be used for clinical or research purposes.
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22549625
|
PSYCHOLOGY
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10.1186/s40359-024-02200-y
|
Page: investigating the predictors of general psychological help seeking intention among people who attempted suicide by using structural equation modeling
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The aim of this study was to determine the role of suicide literacy, suicide stigma, perceived social support, and attitudes toward seeking professional psychological help (ATSPPH) in predicting general help-seeking intention among individuals who have attempted suicide by structural equation modeling. This cross-sectional study was conducted among 462 people who were referred to the hospital due to suicide attempt in one of the cities of eastern Iran in 2023. The sampling method in this study was consecutive sampling. The Pearson correlation, One-way ANOVA, and Independent-samples t-test were used to analyze data by SPSS software. Also, AMOS software was used for conducting structural equation modeling and checking the standardized direct effects and standardized indirect effects between variables. Only 0.9% (n = 4) of participants answered correctly more than 17 questions regarding suicide literacy. The structural equation modeling (SEM) results showed that suicide literacy, suicide stigma, perceived social support, and ATSPPH predicted 51% variance in the general help-seeking intention (R2 = 0.51, PGFI = 0.607, PCFI = 0.568, RMSEA = 0.064). The variables of suicide literacy (estimate total effect = 0.615), ATSPPH (estimate total effect = 0.368), perceived social support (estimate total effect = 0.123), and suicide stigma (estimate total effect = 0.033) had the greatest impact in predicting the general help-seeking intention. The SEM results highlight the importance of paying more attention to suicide literacy, reducing suicide stigma, promoting social support, improving the positive attitude toward mental health services, encouraging people who have attempted suicide to seek psychological help, and finally preventing suicide attempt.
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20507283
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PSYCHOLOGY
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10.3390/educsci14121292
|
Essential Elements for Implementing AI Tools in Elementary School: A Systematic Literature Review
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The global use of Artificial Intelligence (AI) has attracted considerable attention, and its integration into educational systems is a priority that warrants further exploration. In collaboration with UNESCO, numerous organizations have proposed parameters advocating for the inclusion of AI in basic education systems. A systematic literature review (SLR) was conducted to identify these parameters from the existing research. Although these parameters have been mentioned in some studies, they are generally not prioritized in the research landscape. AI tools are primarily used to support students, while teachers typically employ a pedagogical approach centered on in-class activities. Additionally, essential conditions related to research requirements and involvement from the private and third sectors showed consistent adherence across the examined studies. However, it was found that only 52% of the studies included an ethical declaration regarding the data collected by AI during research development, especially regarding studies involving children. This review provides a guide for educational communities looking to enhance pedagogical practices through AI integration into their educational environments, but who may be uncertain about where to begin. Questions related to AI modality selection, pedagogical relevance, ethical considerations, and procedural guidelines for integrating AI into curricula are addressed through the insights provided in this review.
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22277102
|
EDUCATION
|
10.3390/ai5040121
|
Advancing Healthcare: Intelligent Speech Technology for Transcription, Disease Diagnosis, and Interactive Control of Medical Equipment in Smart Hospitals
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Intelligent Speech Technology (IST) is revolutionizing healthcare by enhancing transcription accuracy, disease diagnosis, and medical equipment control in smart hospital environments. This study introduces an innovative approach employing federated learning with Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) neural networks to improve IST performance. Leveraging the “Medical Speech, Transcription, and Intent” dataset from Kaggle, comprising a variety of speech recordings and corresponding medical symptom labels, noise reduction was applied using a Wiener filter to improve audio quality. Feature extraction through MLP and sequence classification with GRU highlighted the model’s robustness and capacity for detailed medical understanding. The federated learning framework enabled collaborative model training across multiple hospital sites, preserving patient privacy by avoiding raw data exchange. This distributed approach allowed the model to learn from diverse, real-world data while ensuring compliance with strict data protection standards. Through rigorous five-fold cross-validation, the proposed Fed MLP-GRU model demonstrated an accuracy of 98.6%, with consistently high sensitivity and specificity, highlighting its reliable generalization across multiple test conditions. In real-time applications, the model effectively performed medical transcription, provided symptom-based diagnostic insights, and facilitated hands-free control of healthcare equipment, reducing contamination risks and enhancing workflow efficiency. These findings indicate that IST, powered by federated neural networks, can significantly improve healthcare delivery, accuracy in patient diagnosis, and operational efficiency in clinical settings. This research underscores the transformative potential of federated learning and advanced neural networks for addressing pressing challenges in modern healthcare and setting the stage for future innovations in intelligent medical technology.
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26732688
|
AI
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10.3390/ai5040122
|
Empirical Evaluation and Analysis of YOLO Models in Smart Transportation
|
You Only Look Once (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models, selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (i.e., COCO and PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications. Third, multiple Intersection over Union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. It was found that YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.
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26732688
|
AI
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10.3390/ejihpe14120194
|
Negative Association Between Schizophrenia and Subsequent Cancer Diagnoses—A Retrospective Cohort Study from Germany
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Background: Since previous studies have reported contradictory findings regarding the relationship between schizophrenia and cancer, we evaluated the association between schizophrenia and cancer diagnoses. Methods: In this retrospective cohort study, the IQVIA Disease Analyzer database was utilized to examine the incidence of cancer among patients aged over 18 years diagnosed with schizophrenia in German general practices from 2005 to 2022. Patients with schizophrenia were compared with those without the condition, with adjustments made for age, sex, index year of diagnosis, average annual practitioners visit frequency, and comorbidity. Kaplan–Meier curves were used to analyze the 10-year cumulative incidence of schizophrenia and cancer in total amongst patients with and without schizophrenia. Univariate Cox regression analysis was performed to calculate Hazard Ratios (HR) of cancer risk and their 95% confidence intervals (CI) of cancer in total and of specific cancer types. Results: Patients with schizophrenia (N = 13.711) had a lower incidence of cancer diagnosis compared to those without (N = 68.555). Specifically, 10.4% of patients with schizophrenia and 12.5% of patients without the condition were diagnosed with cancer (p < 0.001). Cox regression analysis showed a significant association between schizophrenia and subsequent cancer in the total population (HR: 0.82; 95% CI: 0.76–0.90), and among men (HR: 0.70; 95% CI: 0.61–0.80), but not among women (HR: 0.94, 95% CI: 0.84–1.04). Analyses stratified by cancer type and sex revealed a strong and significant association between schizophrenia and a decreased risk of prostate cancer in men (HR: 0.38; 95% CI: 0.24–0.61). Furthermore, there was also a negative association between schizophrenia and colorectal cancer risk in men, but statistical significance was not reached (HR: 0.58; 95% CI: 0.37–0.93). Conclusions: This study demonstrates negative associations between schizophrenia and subsequent cancer, and more specifically in men for prostate and colorectal cancer. However, further research is required to explore the underlying reasons for these associations.
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22549625
|
PSYCHOLOGY
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10.3389/frai.2024.1493566
|
Exploring the utilization and deficiencies of Generative Artificial Intelligence in students’ cognitive and emotional needs: a systematic mini-review
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Despite advances in educational technology, the specific ways in which Generative Artificial Intelligence (GAI) and Large Language Models cater to learners’ nuanced cognitive and emotional needs are not fully understood. This mini-review methodically describes GAI’s practical implementations and limitations in meeting these needs. It included journal and conference papers from 2019 to 2024, focusing on empirical studies that employ GAI tools in educational contexts while addressing their practical utility and ethical considerations. The selection criteria excluded non-English studies, non-empirical research, and works published before 2019. From the dataset obtained from Scopus and Web of Science as of June 18, 2024, four significant studies were reviewed. These studies involved tools like ChatGPT and emphasized their effectiveness in boosting student engagement and emotional regulation through interactive learning environments with instant feedback. Nonetheless, the review reveals substantial deficiencies in GAI’s capacity to promote critical thinking and maintain response accuracy, potentially leading to learner confusion. Moreover, the ability of these tools to tailor learning experiences and offer emotional support remains limited, often not satisfying individual learner requirements. The findings from the included studies suggest limited generalizability beyond specific GAI versions, with studies being cross-sectional and involving small participant pools. Practical implications underscore the need to develop teaching strategies leveraging GAI to enhance critical thinking. There is also a need to improve the accuracy of GAI tools’ responses. Lastly, deep analysis of intervention approval is needed in cases where GAI does not meet acceptable error margins to mitigate potential negative impacts on learning experiences.
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26248212
|
AI
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10.1186/s40359-024-02197-4
|
Clinical decision making and moral distress among intensive care units nurses in Iran
|
Intensive care units are often presented as environments where ethical issues are common and decisions can determine the life or death of patients, and these units have unique challenges due to critical health care. In these units, the relationship between the medical team and the patient’s relatives, their refusal of treatment, informed consent causes the nurses to have conflict in their decision making, therefore, this study aims to determine the level of clinical decision-making and moral distress and the relationship between them in intensive care units nurses. This cross-sectional study was conducted with a descriptive-analytical approach in 2023 in Gorgan city. The number of 198 nurses in the intensive care units of Gonbad Kavos hospitals in the north of Iran were investigated and evaluated using the Corley Moral Distress questionnaire (2002), and Laurie’s Clinical Decision questionnaire (2001) in 2023. Independent T-test and anova analysis of variance were used for bivariate analysis. The significance level in this study was considered 0.05. The results of the study showed that the mean and standard deviation of clinical decision making was 60.98 ± 10.25 (analytical-systematic level) and moral distress was 92.2 ± 23.61 (moderate level). There was a statistically significant relationship between clinical decision-making and nurses’ moral distress (P < 0.001 and r = 0.370). The moral distress score had a statistically significant relationship with marriage, employment status, education level, age, work experience and duration of employment in the special department. Also, the clinical decision score had a statistically significant relationship with employment status, education level, age and work experience. According to these results, it seems that more attention should be paid to the moral distress of nurses in intensive care units, and it is necessary to improve their decision-making towards intuitive interpretation, which is done by designing and compiling training programs and workshops for nurses can be done, so that we can provide optimal nursing services to the patients of this department.
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20507283
|
PSYCHOLOGY
|
10.3390/cancers16233998
|
Prevalence of Abnormalities at Tandem Endoscopy in Patients Referred for Colorectal Cancer Screening/Surveillance Colonoscopy
|
Introduction: Performing a tandem endoscopy and colonoscopy in selected individuals has advantages, such as the early detection of benign and/or precancerous foregut diseases; it is efficient, and it may allow added therapies. It may also have disadvantages, such as generating anxiety from false-positive screening, possible harm from further testing, and unproven cost-effectiveness. Aims: We aimed to examine the prevalence of foregut endoscopic and histologic abnormalities in subjects referred for screening/surveillance colonoscopy who also underwent a tandem endoscopy. We wanted to (1) assess implications for cancer detection, intervention, and surveillance of precancerous foregut abnormalities, (2) identify benign foregut lesions, and (3) generate data on the utilities of this tandem approach. Patients and Methods: A retrospective cohort study of consecutive subjects referred for screening or surveillance colonoscopy who also underwent an endoscopy. Based on national screening guidelines, responses to prompting questions, personal or family history, or other risk factors, subjects were assigned to tandem endoscopy with biopsies (modified Seattle and Sydney protocols), under one anesthesia. Results: Of the 1004 patients referred for colonoscopy, 317 (32%) underwent tandem endoscopy. There were 214 women and 103 men. There were 237 Whites, 16 Asians, 40 Blacks, and 24 Hispanics. Median age was 59 (range 19–85). At endoscopy, we identified actionable benign (45%) peptic, inflammatory, and H. pylori-related abnormalities, and premalignant findings (i.e., intestinal metaplasia, 27%, dysplasia, 2%, and cancer 0.9%), comparable to the premalignant (40.3%) and malignant (0.6%) colonoscopy yield. Conclusions: When implemented based on national screening guidelines, tandem EGD and colonoscopy combines Barrett’s esophagus and gastric cancer screening in one examination, and it has a high yield in a diverse US population.
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20726694
|
ONCOLOGY
|
10.3390/ai5040124
|
Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability
|
Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively model intricate relationships between numerical and categorical variables, resulting in a notable increase in prediction accuracy. Methods: A comparative analysis was performed with other Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Darknet, and Extreme Inception (Xception), showing superior performance of the proposed Resnet. Key factors influencing accident severity were identified, with Shapley Additive Explanations (SHAP) values helping to address the need for transparent and explainable Artificial Intelligence (AI) in critical decision-making areas. Results: The generalizability of the ResNet model was assessed by training it, initially, on a UK road accidents dataset and validating it on a distinct dataset from India. The model consistently demonstrated high predictive accuracy, underscoring its robustness across diverse contexts, despite regional differences. Conclusions: These results suggest that the adapted ResNet model could significantly enhance traffic safety evaluations and contribute to the formulation of more effective traffic management strategies.
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26732688
|
AI
|
10.3389/fpsyg.2024.1390968
|
The risks of unconcern: low sensitivity to threat can have unfortunate consequences
|
Each one of us is confronted with warnings of danger or threats to wellbeing in our everyday life, whether in the form of certain road signs, Public Service Announcements, ominous changes in bodily functioning, or cautionary tales heard from family or friends. There is great inter-individual variation in how people respond to such threats, with some people habitually tending to ignore or dismiss them, often to their peril. The first purpose of the present paper is to review several studies showing that individuals—most often men—who score very low on measures of trait anxiety are more likely to engage in behaviors that could jeopardize their physical wellbeing. The general hypothesis that is derived from that review is that when attention to everyday threats is chronically muted by way of a dispositional trait, the likelihood of proceeding down some dangerous path is increased. Those findings are then discussed within the broader context of personality theory to highlight the importance of recognizing the bipolarity of common traits. Here the case is made for replacing the term trait anxiety with the term threat sensitivity in order to capture the full breadth of this basic personality variable. A discussion of the neurobiological underpinnings of threat sensitivity is then presented with an emphasis on individual and sex differences in the workings of the defensive survival circuitry. Taken together, this paper has implications for two subfields within psychology. For the area of personality theory, this paper provides support for the adaptationist view with the argument that low threat sensitivity has both adaptive and maladaptive potential. For the area of health psychology, it is argued that some individuals who demonstrate a habitual tendency to neglect their physical wellbeing may be acting—at least in part—in accordance with their innate neurobiological constitution.
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16641078
|
PSYCHOLOGY
|
10.3389/fpsyg.2024.1502199
|
Effect of match location on the playing style of teams coached by ‘Pep’ Guardiola
|
Introduction: Analysis in football seeks to find the performance factors that bring teams closer to success.Methods: This study aims to analyze the playing styles of two teams managed by Pep Guardiola (F.C. Barcelona and Manchester City) based on match location (home or away). Two methods of analysis were used: descriptive statistics through chi-square tests to evaluate game characteristics and the polar coordinates technique to analyze the relationships between the different lines of each team (goalkeeper, defenders, midfielders, and forwards).Results: The results showed that F.C. Barcelona maintained a consistent playing style regardless of location, exhibiting significant differences only in actions that involved shots or header (p = 0.035), with better performance at home. In contrast, Manchester City displayed significantly different performance in action success (p < 0.001), level of play elaboration (p = 0.004), density (p = 0.033), duration (p = 0.036), and actions that included a shot (p = 0.001) depending on the location. Additionally, qualitative analyses revealed differences in the relationships among the team lines according to match location, with Manchester City displaying more variability in these interactions than F.C. Barcelona.Discussion: The study concludes that although Guardiola applies a consistent set of strategies, match location has a greater influence on Manchester City’s performance, suggesting that this team adjusts its playing style on the basis of contextual conditions. These findings highlight the importance of considering factors such as location when preparing tactics to increase the probability of success in elite football.
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16641078
|
PSYCHOLOGY
|
10.3389/frai.2024.1427534
|
SkyMap: a generative graph model for GNN benchmarking
|
Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 benchmark datasets for validation. To address this limitation, several generative graph models like ALBTER or GenCAT have emerged, aiming to fix this problem with synthetic graph datasets. However, these models often struggle to mirror the GNN performance of the original graphs. In this work, we present SkyMap, a generative model for labeled attributed graphs with a fine-grained control over graph topology and feature distribution parameters. We show that our model is able to consistently replicate the learnability of graphs on graph convolutional, attention, and isomorphism networks better (64% lower Wasserstein distance) than ALBTER and GenCAT. Further, we prove that by randomly sampling the input parameters of SkyMap, graph dataset constellations can be created that cover a large parametric space, hence making a significant stride in crafting synthetic datasets tailored for GNN evaluation and benchmarking, as we illustrate through a performance comparison between a GNN and a multilayer perceptron.
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26248212
|
AI
|
10.3389/frai.2024.1463164
|
Sequence labeling via reinforcement learning with aggregate labels
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Sequence labeling is pervasive in natural language processing, encompassing tasks such as Named Entity Recognition, Question Answering, and Information Extraction. Traditionally, these tasks are addressed via supervised machine learning approaches. However, despite their success, these approaches are constrained by two key limitations: a common mismatch between the training and evaluation objective, and the resource-intensive acquisition of ground-truth token-level annotations. In this work, we introduce a novel reinforcement learning approach to sequence labeling that leverages aggregate annotations by counting entity mentions to generate feedback for training, thereby addressing the aforementioned limitations. We conduct experiments using various combinations of aggregate feedback and reward functions for comparison, focusing on Named Entity Recognition to validate our approach. The results suggest that sequence labeling can be learned from purely count-based labels, even at the sequence-level. Overall, this count-based method has the potential to significantly reduce annotation costs and variances, as counting entity mentions is more straightforward than determining exact boundaries.
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26248212
|
AI
|
10.1186/s40359-024-02229-z
|
The relationship between perceived social support and social anxiety in Chongqing rural secondary school students: the chain mediating effect of core self-evaluation and shyness
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Adolescents in less economically developed areas are susceptible to social anxiety, so finding ways to effectively prevent and intervene in social anxiety could be a major step forward for poverty alleviation. However, little is known about the inner workings of social anxiety in this group. Exploring the risk and protective factors of social anxiety among adolescents in less developed rural areas is crucial for maintaining their mental health and improving their social adaptability. The purpose of this study is to explore the relationships among perceived social support, core self-evaluation, shyness and social anxiety among rural secondary school students and analyze the risk and protective factors of social anxiety. A total of 626 rural secondary school students are investigated with the Perceived Social Support Scale (PSSS), Core Self-Evaluation Scale (CSES), Shyness Scale (SS) and Social Avoidance and Distress Scale (SADS). Structural equation modeling is used to analyze the mediating effects of core self-evaluation and shyness. The results reveal that (1) the perceived social support and core self-evaluation of rural secondary school students are significantly negatively correlated with social anxiety, whereas their shyness is significantly positively correlated with social anxiety. There are significant gender differences in perceived social support, core self-evaluation, shyness and social anxiety. (2) There is a significant chain mediating effect of core self-evaluation and shyness between perceived social support and social anxiety, and the mediation model is cross-gender consistent. These results confirm that perceived social support and core self-evaluation are protective factors against social anxiety in rural secondary school students and that shyness is a risk factor for social anxiety. Moreover, perceived social support can indirectly affect social anxiety through core self-evaluation and shyness. Prevention and intervention of social anxiety can be carried out in three ways: improving the perceived ability of social support, enhancing positive self-evaluation, and reducing shyness and avoidance behaviors.
|
20507283
|
PSYCHOLOGY
|
10.3389/fpsyg.2024.1489997
|
Controlling the narrative: the relationship between narrative ability and executive functioning in children with developmental language disorder
|
Children with developmental language disorder (DLD) experience problems in language comprehension and/or production. In particular, storytelling or narrative ability is often impaired, as this type of discourse involves all domains of language. These problems may lead to a lower quality of social interaction and mental health. Moreover, problems in oral narrative ability during early development have a negative effect on later literacy. However, telling a story involves more than language alone. Executive functioning is thought to play an important part in stimulating narrative ability, as linguistic utterances need to be planned in a temporal and causal order, and switching is needed between multiple characters and events in the story. Research has shown that children with DLD experience problems with executive functioning, independent of their language ability. Thus, the difficulties in storytelling may be caused by both impaired language and executive functioning, as both domains follow hierarchical developmental paths during the early childhood years. In this review, we discuss three components of narrative ability (comprehension, production of macrostructure and production of microstructure) and how they may be interconnected to the three core components of executive functioning (working memory, switching and inhibition) and attention. This review shows that updating and monitoring information in working memory plays an important part in all three components of narrative ability, across multiple studies. This result may give direction in the development of narrative assessment and intervention, and urge further research to disentangle the interplay between language and executive control in DLD.
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16641078
|
PSYCHOLOGY
|
10.3390/ai5040128
|
From Language Models to Medical Diagnoses: Assessing the Potential of GPT-4 and GPT-3.5-Turbo in Digital Health
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Background: Large language models (LLMs) like GPT-3.5-Turbo and GPT-4 show potential to transform medical diagnostics through their linguistic and analytical capabilities. This study evaluates their diagnostic proficiency using English and German medical examination datasets. Methods: We analyzed 452 English and 637 German medical examination questions using GPT models. Performance metrics included broad and exact accuracy rates for primary and three-model generated guesses, with an analysis of performance against varying question difficulties based on student accuracy rates. Results: GPT-4 demonstrated superior performance, achieving up to 95.4% accuracy when considering approximate similarity in English datasets. While GPT-3.5-Turbo showed better results in English, GPT-4 maintained consistent performance across both languages. Question difficulty was correlated with diagnostic accuracy, particularly in German datasets. Conclusions: The study demonstrates GPT-4’s significant diagnostic capabilities and cross-linguistic flexibility, suggesting potential for clinical applications. However, further validation and ethical consideration are necessary before widespread implementation.
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26732688
|
AI
|
10.1007/s44196-024-00696-1
|
Evaluation of Motorcycle Brands Using Multi-attribute Decision-Making Method Under Single-Valued Neutrosophic Cubic Hypersoft Set Environment
| null |
18756883
|
AI
|
10.1007/s44196-024-00695-2
|
Efficient Prediction of Judicial Case Decisions Based on State Space Modeling
|
With the rapid advancement of information technology and artificial intelligence, the digitization of legal texts has caused a swift increase in the volume of legal materials. Judges now face increased professional demands, larger information loads, and more complex case structures, which heightens their workload and demands. To enhance the quality and efficiency of judicial work and drive the modernization of the judicial system, the application of intelligent prediction models has become essential. This paper presents the MambaEffNet model, which integrates multiple modules such as Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP). The core convolutional structure is improved using a state space model, and a multi-directional feature fusion structure is designed to enhance the performance of sequence feature extraction. Generative Adversarial Networks (GAN) are employed for data augmentation, to address the issue of missing features in judicial case predictions. The EfficientNetV2 architecture is used to optimize the kernel size and the expansion ratio of input and output channels. Experimental results demonstrate that the MambaEffNet model achieves a prediction accuracy of 92.05% on the Nigerian Supreme Court judgment dataset and performs excellently on other judicial datasets, significantly improving prediction accuracy and efficiency. Specifically, the MambaEffNet model increases the prediction accuracy for criminal and civil case judgments by 9.53% and 11.57%, respectively. Additionally, the model excels in handling long sequence data, effectively capturing key features and providing comprehensive decision support.
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18756883
|
AI
|
10.1007/s44196-024-00630-5
|
Channel2DTransformer: A Multi-level Features Self-attention Fusion Module for Semantic Segmentation
|
Semantic segmentation is a crucial technology for intelligent vehicles, enabling scene understanding in complex driving environments. However, complex real-world scenarios often contain diverse multi-scale objects, which bring challenges to the accurate semantic segmentation. To address this challenge, we propose a multi-level features self-attention fusion module called Channel2DTransformer. The module utilizes self-attention mechanisms to dynamically fuse multi-level features by computing self-attention weights between their channels, resulting in a consistent and comprehensive representation of scene features. We perform the module on the Cityscapes and NYUDepthV2 datasets, which contain a large number of multi-scale objects. The experimental results validate the positive contributions of the module in enhancing the semantic segmentation accuracy of multi-scale objects and improving the performance of semantic segmentation in complex scenes.
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18756883
|
AI
|
10.1007/s44196-024-00694-3
|
Studying the Impact of Changing Consumer Behavior During Crisis Periods Through Store Classification
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Since customer behavior changes unpredictably during crisis periods such as pandemics, many sectors have been affected differently. The retail sector in particular has been one of the most affected sectors. Retail companies that could not determine the right strategies against customer behavior change were in a difficult situation, and some even had to close down. The inability of consumers to do physical shopping for reasons such as socializing, experiencing products and interacting during the pandemic process required an understanding of changing consumer needs. In this study, to determine the changes in customer purchasing behaviors during the pandemic period, using the sales data of a company operating in the women’s clothing sector and whose sales loss approached 50% during the pandemic period, separate stores were divided into clusters using machine learning methods for the pre-pandemic and pandemic period. The clusters formed were examined and the stores in different clusters were determined depending on customer purchasing behavior. The aim of the study is to ensure that the company segments its stores correctly to gain competitive advantage. Firms will be able to determine the right strategies against changing consumer behavior through a correct store segmentation. First, stores that do not belong to any classification group are clustered using unsupervised machine learning methods. No significant change was observed in the clusters formed before and during the pandemic. This indicated that the pandemic had a similar effect on all stores. Then, pre-pandemic, pandemic period and both periods data were analyzed using 7 different machine learning classification algorithms. The results obtained were compared. For all three analyses, the random forest algorithm gave the highest accuracy rate. The random forest algorithm with the highest accuracy was hybridized with 3 different classification algorithms. The hybrid model consisting of random forest and support vector machine gave the highest accuracy rate (90%) for the period including all data for store classification. Thanks to the hybrid model created with random forest and support vector machines, companies can be advantageous against other companies in the competitive environment by creating separate strategies for each store class.
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18756883
|
AI
|
10.1007/s44196-024-00707-1
|
Correction: Artificial Intelligence in Aviation Safety: Systematic Review and Biometric Analysis
| null |
18756883
|
AI
|
10.1007/s44196-024-00692-5
|
Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance
|
Environmental perception is one of the key technologies to realize autonomous vehicles. The fault diagnosis process involves identifying the fault that occurred or the cause of the out-of-control condition. Here, the major objective is to locate problems in detection by analysing previous data or sequential patterns of data that cause failure. This study evaluates the use of deep learning for improved sensor data fusion in fault identification and tolerance using the KITTI dataset. The input video from the dataset has been transformed to frames through median filtering. Next, feature extraction is applied to a preprocessed image, resulting in the fusion of sensor data. Data fusion is then carried out utilizing an enhanced RPN (region proposal network). The enhanced RPN also has a loss function (object detection loss, bounding box loss and target classification loss), an estimate of ROI and feature extraction network (FEN). Through the use of the COOT connected blue monkey optimization (CCBMO) model, the weight of the optimally enhanced RPN is established. Next, using global non-maximum suppression with both global and local confidence, fault identification and tolerance are carried out. From the analysis, it clearly shows that proposed method accomplished better results in terms of accuracy, precision and specificity of 97.78%, 93.76% and 93.43%, respectively, when compared with various conventional models with respect to diverse performance measures.
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18756883
|
AI
|
10.1186/s40594-024-00520-4
|
A decade of advancing development, diversity, engagement, and excellence in STEM education
|
From August 2014 to July 2024, the International Journal of STEM Education has completed ten full publication cycle years. In this editorial, I offer a brief reflection of the journal’s successful growth over the past decade. I also celebrate the collective achievements of all those involved and highlight the journal’s continued commitment to excellence, driven by the engagement of diverse researchers and readers from around the world.
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21967822
|
EDUCATION
|
10.3389/feduc.2024.1376087
|
Extending access for all chemistry students with extended reality
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Equal access to instructor’s time and attention in chemistry classes and laboratories can be a barrier experienced by students from historically excluded groups. An instructor’s own biases will determine the nature of their interaction with students, and even well-meaning instructors can interact with students in slightly different ways, which might prevent certain students from having access to all the available instructional resources for the class. This is an additive problem, which may or may not be recognized in peer and student evaluations, and an issue that might escape self-reflection even in educators that are committed to diversity, inclusion, and justice. This issue conflates both actual and perceived biases, introducing a complex dynamic between instructor and student. Extended reality (XR) provides an avenue to generate materials that can be used to enhance or replace classroom instruction with a great degree of realism. In this paper we will discuss the implementation of a set of virtual reality (VR) organic chemistry labs. We will show that XR learning tools are by their very nature accessible and inclusive of a wide variety of students and will provide evidence from student reflections that shows that students from historically excluded groups find the XR content offered in our virtual reality labs more personal than in-person activities covering the same material.
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2504284X
|
EDUCATION
|
10.3389/frai.2024.1502580
|
The effect of AI on pink marketing: the case of women’s purchasing behavior using mobile applications
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This research looks in detail at the dynamics of pink marketing and its effect on the purchase behavior of Saudi women through mobile applications, with an emphasis on Artificial Intelligence (AI) as a moderator. Furthermore, this study assesses the effects of customized pink marketing strategies – product, price, promotion, and place – on buying intentions and behaviors. A closed-ended questionnaire was adopted to measure constructs associated with women’s mobile app purchase behavior influenced by pink marketing and AI elements. Structural Equation Modeling (SEM) was the study tool used to examine how AI affects women’s consumer behavior and how it influences pink marketing. The results suggest that each component of the pink marketing mix significantly influences buying behavior, especially price and promotion. Additionally, AI has a significant moderating effect, improving the personalization and effectiveness of marketing activities. The results of this study highlight the essential role of AI in forming consumer engagement in the digital market, providing useful input for marketers who intend to target women in Saudi Arabia. This study complements the understanding of gender marketing in the digital era and provides a vision for the possibility of AI fundamentally changing traditional approaches.
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26248212
|
AI
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10.3389/feduc.2024.1465207
|
Examining the effect of AI-powered virtual-human training on STEM majors’ self-regulated learning behavior
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Introduction: Students pursuing science, technology, engineering, and math (STEM) majors often struggle with essential skills critical to their academic success and future careers. Traditional self-regulated learning (SRL) training programs, while effective, require significant time investments from both students and instructors, limiting their feasibility in large lecture-based STEM courses.Methods: This study investigates whether completion of three AI-powered virtual-human training modules—focused on planning, self-monitoring, and reflection—leads to increased use of corresponding MS Planner tools among STEM majors compared to a control group.Results: Results indicate that students who did not complete the first two training modules were less likely to use MS Planner features for planning and self-monitoring; however, the reflection module did not yield comparable results.Discussion: These findings highlight the potential of AI-powered virtual-human training as a scalable solution to enhance desirable learning behaviors among STEM majors, particularly in large and diverse classrooms. This research contributes to the understanding of effective interventions for fostering SRL behaviors in STEM education and suggests avenues for future refinement and implementation of digital training tools.
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2504284X
|
EDUCATION
|
10.3389/frai.2024.1374323
|
Accuracy improvement in financial sanction screening: is natural language processing the solution?
|
Sanction screening is a crucial banking compliance process that protects financial institutions from inadvertently engaging with internationally sanctioned individuals or organizations. Given the severe consequences, including financial crime risks and potential loss of banking licenses, effective execution is essential. One of the major challenges in this process is balancing the high rate of false positives, which exceed 90% and lead to inefficiencies due to increased human oversight, with the more critical issue of false negatives, which pose severe regulatory and financial risks by allowing sanctioned entities to go undetected. This study explores the use of Natural Language Processing (NLP) to enhance the accuracy of sanction screening, with a particular focus on reducing false negatives. Using an experimental approach, we evaluated a prototype NLP program on a dataset of sanctioned entities and transactions, assessing its performance in minimising false negatives and understanding its effect on false positives. Our findings demonstrate that while NLP significantly improves sensitivity by detecting more true positives, it also increases false positives, resulting in a trade-off between improved detection and reduced overall accuracy. Given the heightened risks associated with false negatives, this research emphasizes the importance of prioritizing their reduction. The study provides practical insights into how NLP can enhance sanction screening, while recognizing the need for ongoing adaptation to the dynamic nature of the field.
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26248212
|
AI
|
10.3389/frai.2024.1482141
|
Toward explainable deep learning in healthcare through transition matrix and user-friendly features
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Modern artificial intelligence (AI) solutions often face challenges due to the “black box” nature of deep learning (DL) models, which limits their transparency and trustworthiness in critical medical applications. In this study, we propose and evaluate a scalable approach based on a transition matrix to enhance the interpretability of DL models in medical signal and image processing by translating complex model decisions into user-friendly and justifiable features for healthcare professionals. The criteria for choosing interpretable features were clearly defined, incorporating clinical guidelines and expert rules to align model outputs with established medical standards. The proposed approach was tested on two medical datasets: electrocardiography (ECG) for arrhythmia detection and magnetic resonance imaging (MRI) for heart disease classification. The performance of the DL models was compared with expert annotations using Cohen’s Kappa coefficient to assess agreement, achieving coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset. These results demonstrate strong agreement, underscoring the reliability of the approach in providing accurate, understandable, and justifiable explanations of DL model decisions. The scalability of the approach suggests its potential applicability across various medical domains, enhancing the generalizability and utility of DL models in healthcare while addressing practical challenges and ethical considerations.
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26248212
|
AI
|
10.3389/fpsyg.2024.1451431
|
Loss of empathy in stroke
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Background: Loss of empathy (LoE) is common among stroke survivors, yet often undiagnosed and thus untreated. LoE is related to the loss of a caring marital relationship, higher care burden and poorer quality of life in carers. The present study will evaluate the clinical and MRI correlates of LoE in a cohort of stroke survivors. The secondary objective is to describe the 12-month course of LoE.Methods: The current study is a prospective cohort study. We will recruit 246 subjects. Subjects and carers will receive a detailed assessment at a research clinic at 3, 9, and 15 months after stroke onset (T1/T2/T3). The Chinese version of the Interpersonal Reactivity Index (IRI), a 28-item personality assessment tool, will be completed by a carer for each subject. LoE is defined as an IRI total score of 39 or less. Patients will be examined by MRI including diffusion weighted imaging (DWI) within 1 week after the onset of stroke. A stepwise logistic regression will be performed to assess the importance of lesions in the regions of interest. To examine the predictors of LoE remission, the demographic, clinical and MRI variables of remitters and non-remitters at T2/T3 will be examined by logistic regression.Discussion: This project will be the first longitudinal study on LoE in stroke survivors. The results will shed light on the association between prefrontal cortex and subcortical lesions and LoE risk, symptom severity and outcome. The findings will provide data to advance our understanding of the pathogenesis and clinical course of LoE in stroke as well as other neurological conditions. They are thus likely to be applicable to the large population of neurological patients at risk of LoE and should also stimulate further research in this field.
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16641078
|
PSYCHOLOGY
|
10.3389/frai.2024.1466321
|
Predicting financial distress in TSX-listed firms using machine learning algorithms
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Introduction: This study investigates the application of machine learning (ML) algorithms, a subset of artificial intelligence (AI), to predict financial distress in companies. Given the critical need for reliable financial health indicators, this research evaluates the predictive capabilities of various ML techniques on firm-level financial data.Methods: The dataset comprises financial ratios and firm-specific variables from 464 firms listed on the TSX. Multiple ML models were tested, including decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN). Recursive feature elimination with cross-validation (RFECV) and bootstrapped CART were also employed to enhance model stability and feature selection.Results: The findings highlight key predictors of financial distress, such as revenue growth, dividend growth, cash-to-current liabilities, and gross profit margins. Among the models tested, the ANN classifier achieved the highest accuracy at 98%, outperforming other algorithms.Discussion: The results suggest that ANN provides a robust and reliable method for financial distress prediction. The use of RFECV and bootstrapped CART contributes to the model’s stability, underscoring the potential of ML tools in financial health monitoring. These insights carry valuable implications for auditors, regulators, and company management in enhancing practices around financial oversight and fraud detection.
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26248212
|
AI
|
10.3389/feduc.2024.1484999
|
“Be yourself, rediscover yourself, and find new aspects of yourself”: newcomer youth integration in a francophone school system
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Negative school integration experiences can compromise the healthy development of newcomer youth. Little research has explored what affects their experiences; even less has engaged youth in the research process. This study investigated the school integration experiences of French-speaking newcomer youth in a predominantly anglophone Canadian province using the Arts-Based Engagement Ethnography research method. We explored: (1) How do newcomer youth experience a public francophone school? and (2) How do these experiences influence their positive integration into the francophone school system in a predominantly anglophone province? Data from artifacts, interviews, and planned group discussions were organized into four structures: (a) navigating school integration challenges, (b) negotiating identity, (c) confronting biases, and (d) helping other newcomer youth. Underlying patterns painted a rich portrait of newcomer youth school integration experiences, which informed their emerging identity. Findings point to needed changes to supports and services offered to newcomer youth integrating into the francophone school system in a predominantly anglophone province.
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2504284X
|
EDUCATION
|
10.3389/fonc.2024.1483435
|
METTL3 as a potential therapeutic target in gastric cancer
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Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. N6-methyladenosine (m6A) modification is the most prominent epigenetic modification of eukaryotic mRNAs, and methyltransferase-like 3 (METTL3), a core component of the methyltransferase complex, catalyzes m6A modification. The results of previous studies indicate that the expression level of METTL3 is significantly elevated in gastric cancer tissues and cells. In addition, fluctuations in m6A levels induced by METTL3 are closely associated with the malignant progression of tumors as well as the poor prognosis of patients with gastric cancer. In this review, we focus on the potential mechanism of METTL3 in gastric cancer, and through our analysis, we suggest that targeting METTL3 could be a new therapeutic tool for treating GC.
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2234943X
|
ONCOLOGY
|
10.3389/feduc.2024.1510416
|
Coping and well-being in university students: sex and cultural differences
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For the psychological and personal well-being of university students, it is considered essential to study the coping strategies they use when faced with conflictive situations in the academic context and the resources that the institution offers to help them overcome these challenges. The objective of this work is to evaluate the effect of sex and culture on the different coping strategies that higher education students use in the face of the difficulties they face in the academic environment. For this purpose, the questionnaire “Coping Strategies Inventory (CSI)” was applied to a sample of 1,281 university students. The results indicate that there are significant differences in the problem-solving strategies used depending on gender and culture, finding interaction between these variables, with European women being the ones who use active strategies the most. On the contrary, men of Berber origin, are the ones who use less coping strategies, both active (emotional expression and social support) and passive (desiderative thinking), to resolve conflicts.
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2504284X
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EDUCATION
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10.3389/fonc.2024.1453246
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Mendelian randomization study of the relationship between blood and urine biomarkers and lung cancer
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Introduction: Identifying suitable biomarkers is crucial for exploring the pathogenesis, early screening, and therapeutic monitoring of lung cancer. This study aims to analyze comprehensively the associations between lung cancer and biomarkers in blood and urine.Methods: Bidirectional two-sample Mendelian randomization (MR) was used to evaluate the potential causal relationships between blood and urine biomarkers and lung cancer. We obtained Single nucleotide polymorphisms (SNPs) related to lung cancer from the 2021 Finnish database of genome-wide association studies, including small cell lung cancer (SCLC), total non-small cell lung cancer (NSCLC), lung adenocarcinoma (LAC), and lung squamous cell carcinoma (LSCC).Data on blood and urine biomarkers were derived from the UK Biobank cohort, comprising 376,807 participants.Results: We found a potential inverse causal relationship between total bilirubin and SCLC (β=-0.285, P=0.015, FDR=0.12). Urate was inversely associated with NSCLC (β=-0.158, P=0.004, FDR=0.036*). Serum calcium showed a possible inverse relationship with lung squamous cell carcinoma (β=-0.256, P=0.046, FDR=0.138), while urinary creatinine was positively associated (β=1.233, P=0.024, FDR=0.216). Non-albumin proteins (β=-0.272, P=0.020, FDR=0.180) and total protein (β=-0.402, P=0.009, FDR=0.072) were inversely related to lung squamous cell carcinoma. The AST/ALT ratio was positively associated with lung adenocarcinoma (β=0.293, P=0.009, FDR=0.072). Our reverse Mendelian randomization study found a positive causal association between small cell lung cancer and serum creatinine (β=0.022, P=0.002, FDR=0.018*), while it was inversely associated with the estimated glomerular filtration rate(eGFR)(β=-0.022, P=0.003, FDR=0.027*). A positive causal relationship was also observed with cystatin C (β=0.026, P=0.005, FDR=0.045*) and glycated hemoglobin HbA1c (β=0.013, P=0.014, FDR=0.028*). A negative causal relationship was observed with Gamma_glutamyltransferase (β=-0.013, P=0.019, FDR=0.152). For non-small cell lung cancer, a negative causal relationship was found with albumin (β=-0.024, P=0.002, FDR=0.016*), while a potentially positive causal relationship was observed with cystatin C (β=0.022, P=0.006, FDR=0.054). Possible negative causal relationships were also observed with phosphate (β=-0.013, P=0.008, FDR=0.072) and urinary potassium (β=-0.011, P=0.012, FDR=0.108), while a potential positive causal relationship was observed with C-reactive protein (β=0.013, P=0.040, FDR=0.280).Regarding lung squamous cell carcinoma, an inverse causal relationship was found with eGFR (β=-0.022, P=9.58e-06, FDR=8.62×10-5*), while a positive causal relationship was observed with serum creatinine (β=0.021, P=1.16e−4, FDR=1.05×10-3*). Potential positive causal relationships were observed with Urate (β=0.012, P=0.020, FDR=0.180), urea (β=0.010, P=0.046, FDR=0.141), and glycated hemoglobin HbA1c (β=0.020, P=0.049, FDR P=0.098), whereas a potential negative causal relationship was observed with sex hormone-binding globulin(SHBG) (β=-0.020, P=0.036, FDR=0.108).Lastly, adenocarcinoma was found to have a positive causal association with alkaline phosphatase (β=0.015, P=0.006, FDR=0.033*).Conclusion: Our study provides a robust theoretical basis for the early screening and therapeutic monitoring of lung cancer and contributes to understanding the pathogenesis of the disease.
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2234943X
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ONCOLOGY
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10.3389/fonc.2024.1241221
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Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study
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Background: Cardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric features such as dose-volume indicators. Hence, using the spatial information contained in the dosiomics features with machine learning methods may improve the prediction of CD.Methods: We considered the 7670 5-year survivors of the French Childhood Cancer Survivors Study (FCCSS). Dose-volume and dosiomics features are extracted from the radiation dose distribution of 3943 patients treated with RT. Survival analysis is performed considering several groups of features and several models [Cox Proportional Hazard with Lasso penalty, Cox with Bootstrap Lasso selection, Random Survival Forests (RSF)]. We establish the performance of dosiomics compared to baseline models by estimating C-index and Integrated Brier Score (IBS) metrics with 5-fold stratified cross-validation and compare their time-dependent error curves.Results: An RSF model adjusted on the first-order dosiomics predictors extracted from the whole heart performed best regarding the C-index (0.792 ± 0.049), and an RSF model adjusted on the first-order dosiomics predictors extracted from the heart’s subparts performed best regarding the IBS (0.069 ± 0.05). However, the difference is not statistically significant with the standard models (C-index of Cox PH adjusted on dose-volume indicators: 0.791 ± 0.044; IBS of Cox PH adjusted on the mean dose to the heart: 0.074 ± 0.056).Conclusion: In this study, dosiomics models have slightly better performance metrics but they do not outperform the standard models significantly. Quantiles of the dose distribution may contain enough information to estimate the risk of late radio-induced high-grade CD in childhood cancer survivors.
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2234943X
|
ONCOLOGY
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10.1186/s40359-024-02132-7
|
A preparation program for psychological safety of hospitalized adolescents
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The study aimed to investigate the impact of an information-based preparation program on the psychological safety of adolescents admitted to pediatric wards, emphasizing the importance of enhancing patient safety. This quasi-experimental study was conducted among 98 adolescents admitted to pediatric wards at Namazi Hospital, managed by Shiraz University of Medical Sciences, in 2021. The participants were randomly assigned to either an intervention group or a control group using an electronic randomization table. The intervention group received an information-based preparation program, while the control group followed routine care. Adolescents completed the Psychological Safety Questionnaire after admission and at discharge. Data were analyzed using SPSS (Version 22), with a significance level of 0.05. The mean psychological safety scores before the intervention were 136.73 ± 17.30 in the control group and 141.03 ± 16.34 in the intervention group, with no significant difference between the two groups (p = 0.20). After the intervention, the mean scores were 136.65 ± 19.01 in the control group and 145.50 ± 14.05 in the intervention group. A comparison of the mean psychological safety scores showed a significant difference between the two groups after the intervention (p = 0.01). The findings of this study indicate that the information-based preparation program positively affected the psychological safety of hospitalized adolescents. Therefore, it is recommended that nurses incorporate this method into therapeutic programs for hospitalized adolescents to enhance their psychological safety effectively.
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20507283
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PSYCHOLOGY
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10.3389/feduc.2024.1501899
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Company-university intersections through service-learning (SL): a systematic review
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The most relevant intersections in society include the relationship between universities and companies for a projection toward the sustainable employability of future graduates. Among the possible intersections, Service-learning (SL) is an educational proposition that may help university students to develop their personal skills, offering them opportunities to learn and practice civic commitment, improving their sense of social and citizen responsibility, and combining academic and community-service learning in a constructed programme where participants train by working on real needs of the environment to optimize and transform the latter. The development of SL programmes in university departments related to technical areas is posing a challenge to faculty members and students, thus it is important to explore this lack of programmes. The main aim of the present study was to identify SL projects and their topics through a systematic review, following the guidelines of the «Preferred Reporting Items for Systematic Reviews and Meta-Analyses» (PRISMA) declaration in the knowledge areas of Architecture, Computer Science, Environmental Engineering, Software Engineering, Computer Engineering, Artificial Intelligence, and Computer Languages and Systems, from the year 2008 to the year 2023. This review includes 128 articles, which were analyzed with ATLAS. Ti 22. The categorical system employed in this work emerged from the topics of the programmes identified in the selected articles, which were verified by experts in the mentioned fields of knowledge. The agreed categories were: accessibility, learning, social groups, courses, devices, infrastructure, games, environment, landscaping, heritage, software and web. The most relevant conclusions highlight that most of the articles refer to theoretical aspects of SL, showing a lack of data on the practical development of SL programmes and their impact on employability. The largest number of SL programmes are developed in the areas of Architecture, Computer Science and Software Engineering. Regarding the topics that are addressed in research, most of the articles refer to social groups, software, learning and accessibility.
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2504284X
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EDUCATION
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10.1186/s40594-024-00518-y
|
Creating better internships by understanding mentor challenges: findings from a series of focus groups
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Background: Despite demands to make higher education more relevant beyond academia, and a growing body of work testifying to the benefits of work-relevance programs (e.g., work-placements, or internships) for both students and the companies that host them, there is limited information available for those aiming to optimize these programs. For example, few have explored the challenges and needs of internship supervisors. Here, we focus on the experiences of supervisors in biology and geology programs across three Norwegian institutions. Specifically, through a series of focus groups, we asked internship supervisors about their motivations for serving as student mentors, any challenges they had faced, and what higher-education institutions could do to better prepare them for hosting students at their workplaces. Results: Key challenges faced by supervisors include the need to tailor placements to individual student needs and capabilities, navigating the constraints imposed by academic structures, and addressing communication gaps between students, institutions, and workplace supervisors. Internship supervisors suggest enhancing communication strategies to better define roles and expectations, increasing support and training for supervisors, and establishing clearer, more collaborative frameworks for setting learning objectives with students. Conclusions: The supervisors’ suggestions aim to ensure that internships are mutually beneficial, supporting both students' educational outcomes and the workplace needs. By focusing on the supervisor's perspective, we provide valuable insights into one aspect of implementing effective and rewarding internships (i.e., supervisor preparation), thereby suggesting pathways for future improvements in these high-impact educational practices.
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21967822
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EDUCATION
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10.3389/feduc.2024.1401388
|
Navigating an uncertain interregnum
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This article seeks to identify trends in Steiner Waldorf education through the lens of Clarence Beeby’s work on educational myths. Beeby calls myths a form of communication between contemporaries or between generations, ways of conceptualizing education that can be understood quickly yet are flexible enough to accommodate a range of interpretations. A myth holds for a period and then transitions into a new myth that best suits changed times and changed circumstances. I reflect on what the myths of Waldorf education might be and take up Gramsci’s well-known quotation on change, “The crisis consists precisely of the fact that the old is dying and the new cannot be born; in this interregnum a great variety of morbid symptoms appear,” In writing this, Gramsci extended the interregnum beyond its usual papal connotation to include the socio-cultural condition as well. I use the notion to consider if Waldorf education is currently in an interregnum period and is displaying both “morbid symptoms” and promising signs of fresh development. In addition, I contemplate if these promising signs point toward a new myth that will allow Waldorf education to step beyond its century-old, colonial heritage.
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2504284X
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EDUCATION
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10.3389/fpsyg.2024.1471658
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How watching sports events empowers people’s sense of wellbeing? The role of chain mediation in social interaction and emotional experience
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Background: While engaging in sports is widely recognized for enhancing wellbeing, limited research has examined the effects of watching sports events on individuals’ subjective wellbeing. The mechanisms and pathways underlying this relationship remain unclear.Objectives: This study explores the correlation between watching sports events and the wellbeing of Chinese individuals, based on the theoretical framework of “spectator behavior → social interaction → emotional experience → happiness.” The aim is to investigate the mediating effects of social interaction and emotional experience, providing insights for promoting greater participation in sports events and supporting the healthy development of the sports industry.Methods: The study involved 885 participants from five representative provinces and cities in China. Assessment tools included the Physical Activity Rating Scale, Social Interaction Questionnaire, Emotional Experience Questionnaire, and Subjective Wellbeing Scale. Data were analyzed using Stata and the PROCESS plug-in of SPSS for comprehensive multivariate statistical analysis.Results: Watching sports events significantly and positively affects subjective wellbeing, social interaction, and emotional experience (p < 0.001). Three mediating pathways were identified: (1) watching sports events → social interaction → subjective wellbeing (effect size: 0.024), (2) watching sports events → emotional experience → subjective wellbeing (effect size: 0.011), and (3) watching sports events → social interaction → emotional experience → subjective wellbeing (effect size: 0.003).Conclusion: The direct impact of watching sports events on subjective wellbeing was positive. Indirect effects were facilitated by the mediating roles of social interaction and emotional experience, with the effect of social interaction being more substantial than that of emotional experience.Implications: These findings suggest that watching sports events can serve as a catalyst for enhancing wellbeing, primarily through fostering social connections and enriching emotional experiences. Practically, this indicates the potential value of encouraging viewership of sports events as a means of promoting community engagement and mental health, thus contributing to the holistic growth of the sports sector and public health initiatives.
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16641078
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PSYCHOLOGY
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10.3389/fpsyg.2024.1463641
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Predicting PTSD and complex PTSD from interpersonal violence in Japanese school-based extracurricular sports activities: using the International Trauma Questionnaire (ITQ)
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Introduction: Victims of interpersonal violence in sports show various mental health concerns. However, no studies have quantitatively examined their primary complaints, considering psychological symptoms such as denial of self-concept and interpersonal challenges not captured by conventional post-traumatic stress disorder (PTSD). Recently, an association between interpersonal violence victimization and complex PTSD (CPTSD) has been noted in Japanese sports coaching situations, specifically for extracurricular sports activities. This study aimed to examine the applicability of the International Trauma Questionnaire (ITQ) and determine whether interpersonal violence victimization and related risk factors predicted PTSD and CPTSD in extracurricular sports activities in Japan.Methods: This study included 651 adults aged 18–25 who had previously participated in extracurricular sports activities in junior high and high school. The ITQ was examined using confirmatory factor analysis with maximum likelihood with robust standard errors, fit indices comparisons, a graded response model, differential item functioning, and rank correlation designs. A binomial logistic regression model with robust standard errors examined the association of PTSD and CPTSD with interpersonal violence victimization and related risk factors.Results: The optimal factor structure, measurement precision, and validity of the ITQ were confirmed. Physical and psychological violence victimization and the ITQ were positively correlated with PTSD, difficulties in emotion regulation, self-disgust, and interpersonal problems subscales, respectively. A high frequency of psychological and physical violence victimization experiences and self-identified LGB (lesbian, gay, or bisexual) were associated with PTSD and CPTSD diagnosability. Additionally, being a woman and in school life away from parents were associated solely with PTSD diagnosability.Discussion: This is the first quantitative study to examine CPTSD in a study on interpersonal violence in sports. Our findings can provide insights into desirable victim support and enhanced clinical care in interpersonal violence in a sports context.
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16641078
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PSYCHOLOGY
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10.3389/frai.2024.1532896
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Editorial: Generative AI in education
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In the field of education, there is a growing interest in the use of Generative Artificial Intelligence (Generative AI) to reshape the educational landscape. This Research Topic investigates the transformative potential of Generative AI in various aspects of education. The papers in this edited volume shed light on the latest discoveries, new insights, novel developments, and future challenges in this rapidly advancing field.By leveraging machine learning models, these intelligent systems extract useful insights from vast amounts of data, making them capable of delivering highly individualized content. They can analyze a learner's proficiency level, learning style, and pace, and then tailor the study material accordingly. Generative AI can adapt its content generation strategies to meet distinct preferences and learners’ needs. This can increase student engagement and comprehension, highlighting its potential to transform traditional teaching methodologies.This Research Topic also explores the use of Generative AI as part of AI tutors, capable of tailoring instructions and feedback dynamically based on each learner's progress. Acting as an ever-present mentor, Generative AI can offer learning aids beyond class hours, facilitating continuous learning and immediate doubt clarification. This can be crucial for learners encountering obstacles outside the typical school hours or during self-study periods. Anyway, to use Generative AI as a tutor, further research is needed to examine not only the accuracy of its answers but also their emotional content, as emotions play a crucial role in the learning process.This Research Topic includes 11 papers (Original Research: 6; Perspective: 2; Opinion: 2, and Mini-Review:1). These papers explore areas such as: (a) using Large Language Models (LLMs) to generate feedback, (b) the use and perceived usefulness of a Generative AI chatbot for schoolwork among adolescents, (c) the potential of Generative AI in supporting critical thinking and enhancing human interactions, (d) using ChatGPT to support pre-service mathematics teachers in constructing mathematical proofs, (e) opportunities and challenges of LLMs to model the “whole learner,” (h) exploring Generative AI for personalized educational assessment, (g) the use of AI-mentors in career exploration, (i) the responsible integration of AI in education, (j) the use of LLMs to automatically generate interactive listening tasks, (k) the potential of AI-enhanced robots to generate incorrect information and deceive students, and (l) a mini-review on Generative AI for supporting students' cognitive and emotional needs. The main contributions of these articles are described below.Comparing emotions in ChatGPT answers and human answers to the coding questions on Stack Overflow by Fatahi, Vassileva, and Roy (2024). This paper presents a study aimed to compare the emotional content in human and AI answers. Specifically, it examines the emotional aspects in answers from ChatGPT and humans to 2000 questions sourced from Stack Overflow, finding that ChatGPT's answers tend to be more positive, while human responses often express anger and disgust. Additionally, human emotions exhibit a broader spectrum than ChatGPT. The authors suggest that ChatGPT shows promise as a virtual tutor for students by answering queries and fostering collaboration. However, further research is needed on the emotional aspects of responses.Adolescents’ use and perceived usefulness of generative AI for schoolwork: exploring their relationships with executive functioning and academic achievement by Klarin et al. (2024). The article explores adolescents’ frequency of use and perceived usefulness of generative AI chatbots for schoolwork, focusing on their relationship with executive functioning (EF) and academic achievement. Two studies were conducted with adolescents. Findings indicate that older students use Generative AI tools as more frequently. Also, students facing more EF challenges perceive Generative AI tools as more useful for completing assignments. However, no significant link was found between the use of Generative AI and academic achievement. Future work involves exploring additional Generative AI issues such as potential gender differences, implications for academic equity and the impact on adolescent cognitive development.Using Generative AI in education: the case for critical thinking by Lee and Low (2024). This opinion article makes the case for focusing the use of Generative AI in enhancing students’ critical thinking and human interactions. The authors describe two case studies: (a) teaching communication skills and (b) teaching data structures and algorithms with AI chatbots. The two cases illustrate the potential use of Generative AI to enhance teaching and learning. The authors discuss the benefits of AI-based personalized feedback in improving student engagement and fostering strategic and critical use of AI tools. The article encourages the ethical and responsible use of generative AI in education with potential implications for the workforce.Using large language models to support pre-service teachers' mathematical reasoning—an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry by Herrmann and Dilling (2024). LLMs can be a great source to extract knowledge. It thus appears natural to expect them to generate the texts of classical mathematical proofs. The authors, Marc Herrmann and Frederik Dilling, explore how pre-service teachers employ them to produce proofs. Using the lens of instrumental genesis, their study shows a variety of usage patterns with limited knowledge about the inner workings of the models. It sketches the road to become a teacher support instrument.Large language models for whole-learner support: opportunities and challenges by Mannekote et al. (2024) examines the transformative potential of LLMs in education through the...
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26248212
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AI
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10.3389/fonc.2024.1449401
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A phase I study using bortezomib (Velcade), cladribine, and rituximab in treating patients over 50 years old with mantle cell lymphoma
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Cladribine indirectly downregulates methylation of DNA, RNA, and histones by blocking the transfer of methyl groups from S-adenosyl-methionine. The cladribine and rituximab combination showed a synergetic effect in treating B-cell lymphomas. Bortezomib (Velcade) is a Food and Drug Administration (FDA)-approved proteasome inhibitor for treating mantle cell lymphoma (MCL). In this single-arm, phase I study, the safety, dose-limiting toxicity, and clinical activity of bortezomib, cladribine, and rituximab (VCR) combination treatment were evaluated in elderly MCL patients. Potential DNA methylation biomarkers for VCR treatment were also proposed. A standard 3 + 3 dose-escalation scheme was designed to determine the maximum tolerated dose of cladribine. The therapy consisted of six 28-day cycles. Most patients tolerated this regimen well. The overall response (OR) rate was 84.6%, and the complete remission (CR) rate was 84.6%. In the newly diagnosed subject cohort, the OR and CR were 100%, the 2-year overall survival rate was 84.6%, and the progression-free survival rate was 76.9%. The median age was 64 (54–81). The median time to first response was 3 (2.1–7.4) months. The median follow-up time was 43 (9–60) months. Low-grade hematological toxicity and mild fatigue were observed. No severe systemic toxicity was observed. Five hypermethylated regions located at gene promoters were identified as potential biomarkers for an effective treatment response. In conclusion, the VCR combination is a well-tolerated, low-toxicity, and highly effective regimen for the elderly with untreated MCL.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT01439750.
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2234943X
|
ONCOLOGY
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10.1007/s44196-024-00702-6
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Hyperplane-Assisted Multi-objective Particle Swarm Optimization with Twofold Proportional Assignment Strategy
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In the simultaneous optimization of multiple objectives, how to balance convergence promotion and diversity preservation in the evolutionary process is a key and challenging problem. In this research, a hyperplane-assisted multi-objective particle swarm optimization with a twofold proportional assignment strategy (tpahaMOPSO) is suggested to ameliorate the optimization performance of MOPSO. First, the external archive is maintained in combination with hyperplane-based convergence evaluation and shift-based density estimation to retain high-quality candidate solutions. Second, a twofold proportional assignment scheme is designed to search the surrounding region of candidate solutions with better potential to emphasize convergence and diversity, respectively. Third, the domination relationship and convergence difference are combined to select a more reasonable individual historical best and reduce the risk of particle aggregation. Finally, the proposed tpahaMOPSO was compared with ten representative and advanced multi-objective optimization algorithms on 22 widely used test functions with different characteristics. The simulation results present that the developed tpahaMOPSO got the best result in 11 benchmark functions for both IGD and HV criteria. Concurrently, the Friedman test was applied for ranking analysis and the proposed algorithm also obtained excellent statistical analysis results. The promising performance and strong competitiveness of the proposed tpahaMOPSO have been verified by different experimental studies.
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18756883
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AI
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10.3389/frai.2024.1479905
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Dense Paraphrasing for multimodal dialogue interpretation
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Multimodal dialogue involving multiple participants presents complex computational challenges, primarily due to the rich interplay of diverse communicative modalities including speech, gesture, action, and gaze. These modalities interact in complex ways that traditional dialogue systems often struggle to accurately track and interpret. To address these challenges, we extend the textual enrichment strategy of Dense Paraphrasing (DP), by translating each nonverbal modality into linguistic expressions. By normalizing multimodal information into a language-based form, we hope to both simplify the representation for and enhance the computational understanding of situated dialogues. We show the effectiveness of the dense paraphrased language form by evaluating instruction-tuned Large Language Models (LLMs) against the Common Ground Tracking (CGT) problem using a publicly available collaborative problem-solving dialogue dataset. Instead of using multimodal LLMs, the dense paraphrasing technique represents the dialogue information from multiple modalities in a compact and structured machine-readable text format that can be directly processed by the language-only models. We leverage the capability of LLMs to transform machine-readable paraphrases into human-readable paraphrases, and show that this process can further improve the result on the CGT task. Overall, the results show that augmenting the context with dense paraphrasing effectively facilitates the LLMs' alignment of information from multiple modalities, and in turn largely improves the performance of common ground reasoning over the baselines. Our proposed pipeline with original utterances as input context already achieves comparable results to the baseline that utilized decontextualized utterances which contain rich coreference information. When also using the decontextualized input, our pipeline largely improves the performance of common ground reasoning over the baselines. We discuss the potential of DP to create a robust model that can effectively interpret and integrate the subtleties of multimodal communication, thereby improving dialogue system performance in real-world settings.
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26248212
|
AI
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10.3389/frai.2024.1509179
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A graph neural architecture search approach for identifying bots in social media
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Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.
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26248212
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AI
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10.3389/fonc.2024.1476205
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Penpulimab and Anlotinib in PDL1 high-expression pulmonary giant cell carcinoma with cerebral metastases: case report and review
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Pulmonary giant cell carcinoma (PGCC) is a rare subtype of non-small cell lung cancer (NSCLC) characterized by complex pathology, high rates of misdiagnosis or missed diagnosis, an aggressive clinical course, rapid progression, and poor prognosis. This case report describes a 67-year-old Chinese male with a left upper lobe lung mass, diagnosed via CT-guided lung biopsy as PGCC with symptomatic multiple cerebral metastases. The tumor showed strong PD-L1 positivity, and genetic testing revealed a TP53 exon 4 c.313G mutation. Treatment involved first-line therapy with Penpulimab injection combined with Anlotinib and concurrent cranial radiotherapy. Significant reduction in both the pulmonary and cerebral metastatic lesions was observed, with notable efficacy. As of June 2024, there has been no disease progression for 26 months, with the patient currently maintained on Anlotinib monotherapy. This case demonstrates the favorable efficacy of Penpulimab injection combined with Anlotinib in treating advanced PGCC. These findings indicate that this combination therapy may offer a promising new therapeutic option for this rare type of lung cancer.
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2234943X
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ONCOLOGY
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10.1186/s40594-024-00519-x
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Subtopic-specific heterogeneity in computer-based learning behaviors
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Background: Self-regulated learning (SRL) strategies can be domain specific. However, it remains unclear whether this specificity extends to different subtopics within a single subject domain. In this study, we collected data from 210 college students engaged in a computer-based learning environment to examine the heterogeneous manifestations of learning behaviors across four distinct subtopics in introductory statistics. Further, we explore how the time spent engaging in metacognitive strategies correlated with learning gain in those subtopics. Results: By employing two different analytical approaches that combine data-driven learning analytics (i.e., sequential pattern mining in this case), and theory-informed methods (i.e., coherence analysis), we discovered significant variability in the frequency of learning patterns that are potentially associated with SRL-relevant strategies across four subtopics. In a subtopic related to calculations, engagement in coherent quizzes (i.e., a type of metacognitive strategy) was found to be significantly less related to learning gains compared to other subtopics. Additionally, we found that students with different levels of prior knowledge and learning gains demonstrated varying degrees of engagement in learning patterns in an SRL context. Conclusion: The findings imply that the use—and the effectiveness—of learning patterns that are potentially associated with SRL-relevant strategies varies not only across contexts and domains, but even across different subtopics within a single subject. This underscores the importance of personalized, context-aware SRL training interventions in computer-based learning environments, which could significantly enhance learning outcomes by addressing the heterogeneous relationships between SRL activities and outcomes. Further, we suggest theoretical implications of subtopic-specific heterogeneity within the context of various SRL models. Understanding SRL heterogeneity enhances these theories, offering more nuanced insights into learners’ metacognitive strategies across different subtopics.
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21967822
|
EDUCATION
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10.3389/frai.2024.1477535
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Reader’s digest version of scientific writing: comparative evaluation of summarization capacity between large language models and medical students in analyzing scientific writing in sleep medicine
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Introduction: As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably.Methods: A collection of three AI-generated and human-generated summaries of sleep medicine research article abstracts were shared with 19 study participants (medical students) attending a sleep medicine conference. Participants were blind as to which summary was human or LLM generated. After reading both human and AI-generated research summaries participants completed a 1–5 Likert scale survey on the readability of the extracted writings. Participants also answered article-specific multiple-choice questions evaluating their comprehension of the summaries, as a representation of the quality of content retained by the AI-generated summaries.Results: An independent sample t-test between the AI-generated and human-generated summaries comprehension by study participants revealed no significant difference between the Likert readability ratings (p = 0.702). A chi-squared test of proportions revealed no significant association (χ2 = 1.485, p = 0.223), and a McNemar test revealed no significant association between summary type and the proportion of correct responses to the comprehension multiple choice questions (p = 0.289).Discussion: Some limitations in this study were a small number of participants and user bias. Participants attended at a sleep conference and study summaries were all from sleep medicine journals. Lastly the summaries did not include graphs, numbers, and pictures, and thus were limited in material extraction. While the present analysis did not demonstrate a significant difference among the readability and content quality between the AI and human-generated summaries, limitations in the present study indicate that more research is needed to objectively measure, and further define strengths and weaknesses of AI models in condensing medical literature into efficient and accurate summaries.
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26248212
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AI
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10.1186/s40359-024-02307-2
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Application of the teaching games for understanding model to improve decision-making in sport learning: a systematic review and meta-analysis
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Issues related to sport teaching at different educational stages is a subject of wide interest. Teaching Games for Understanding has been established as the most effective way to teach students the elements related to the field of sport. The objectives of this study were (a) to examine the impact of the Teaching Games for Understanding model on decision-making in sports education and (b) to compare the effect of the interventions analysed according to educational stage. A systematic review and meta-analysis of studies published before August 2024 was conducted. A total of 4937 scientific studies were obtained. The quantitative synthesis consisted of 25 scientific articles (n = 1692). The studies were analyzed using three-level random effects models with variance estimation. Results were calculated as raw mean differences and Hedges’ g effect sizes. This model is suitable for decision-making in sports education (g = 0.82; CI 95% = [0.55; 1.09]). This pedagogical model was also found to be effective for working on decision-making in primary education (g = 0.6108; CI 95% = [0.3587; 0.8628]), secondary education (g = 0.7523; CI 95% = [0.2348; 1.2706]) and higher education (g = 0.8803 [CI 95% = 0.2851 to 1.4855]). Teaching games for understanding effectively addresses decision-making during sports learning. In addition, this pedagogical model is effective for facilitating decision-making according to the role and the moment of the game. The use of this model enables effective technical-tactical learning to solve various problematic actions in real game situations.
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20507283
|
PSYCHOLOGY
|
10.1007/s00432-024-05984-z
|
Anticancer effects of PEP06 (TB01) in combination with Trifluridine/Tipiracil (TAS-102) in a xenograft model of human colorectal cancer
|
Background Colorectal cancer (CRC) is the third most common cancer globally, with advanced stages presenting significant treatment challenges. Recently years, drug combination therapy has become a promising strategy for cancer treatment. Objective To evaluate the therapeutic efficacy of the combination of the anti-angiogenic drug PEP06 (TB01) and the cytotoxic drug Trifluridine/Tipiracil (TAS-102) in human CRC HCT-116 xenograft mouse model. And quantitative assessment of the interaction between TB01 and TAS-102 in the treatment based on pharmacological effects. Methods This study utilized the human CRC HCT-116 xenograft nude mouse model to evaluate the antitumor effects of TAS-102 and TB01, both as mono-therapies and in combination therapies. Results The combination therapy not only demonstrated significantly inhibited tumor growth in a dose-dependent manner, but also seems to reduce the common toxicity associated with such treatments, as shown by the maintenance of body weights in the treated mice. Conclusion The synergistic effect observed from the combined use of TAS-102 and TB01 suggests a promising new treatment avenue for refractory CRC patients, meriting further investigation and potential clinical application.
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14321335
|
ONCOLOGY
|
10.1007/s00432-024-06050-4
|
Occupational adjustments and work ability of young adult cancer survivors: results from the AYA-Leipzig study
|
Purpose: Adolescent and young adult cancer survivors (AYA-CS) face a long working life after treatment, yet factors related to a successful return to work remain largely unexplored. We therefore aimed to investigate the use of occupational adjustments and their impact on work ability upon return to work. Methods: As part of the AYA-LE study, we surveyed AYA-CS (aged 18–39 at diagnosis) who returned to work and assessed work ability (Work Ability Index) as well as use and benefit of occupational adjustments. We analyzed predictors of use and benefit of occupational adjustments on average 4 years post-diagnosis using multivariate linear and logistic regression. Results: Out of 438 AYA-CS, 389 (88.8%) returned to work after cancer diagnosis and were included in analyses. Mean work ability was M = 36.2 (SD = 6.9), 11.4% reported poor, 34.7% moderate, 41.4% good and 12.5% excellent work ability. Following treatment, 82.3% used occupational adjustments, most frequently: flexible working hours, gradual reintegration and reduced working hours. The probability of a reduction in working hours was found to be higher among older AYA-CS (≥ 30), female gender and with a fatigue index ≥ 11 (R2 = 0.073). A fatigue index < 11, elevated levels of pain and the presence of metastases/recurrence were associated with a lower benefit of reduced working hours (R2 = 0.183). Younger age (< 30) and stem cell transplant were associated with a lower benefit of support from colleagues (R2 = 0.077). Conclusion: Our results highlight the need for targeted occupational counselling throughout the treatment and even beyond the return-to-work process, considering individual and social factors.
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14321335
|
ONCOLOGY
|
10.1186/s40359-024-02312-5
|
Assessing ambulance staff attitudes toward mental health conditions: translation and psychometric evaluation of the medical condition regard scale among ambulance staff
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Introduction: Ambulance staff play a crucial role in responding to mental health crises. However, negative regard toward patients with mental health conditions can hinder care. The Medical Condition Regard Scale (MCRS) assesses regards or attitudes but has not previously been validated for educated ambulance staff and has never been translated into Norwegian. This study aims to translate the instrument into Norwegian, test it on a population of ambulance staff, explore the psychometric properties of the Norwegian version, and measure regard for patients with psychosis. Method: The MCRS is an 11-item instrument with a Likert scale of 1–6. Possible sum scores range from 11 to 66 (higher score = more positive regards). We chose “psychosis” as the condition to investigate. Translation followed eight steps: (1) preparation, (2) forward translation, (3) backward translation, (4) first expert panel review, (5) harmonisation, (6) cognitive debriefing, (7) second expert panel review, and (8) writing of the final version. The instrument was tested and re-tested regarding the condition “psychosis” on a representative sample of 114 Norwegian ambulance staff in 2023, with a temporal gap of one month. We explored item scores and distribution, as well as floor and ceiling effects. We tested the internal consistency of the items using Cronbach’s Alpha and consistency in answers over time (test and re-test) using the Paired Sample-T test. We used factor analyses to explore the inter-item relationships of the items. Results: The 114 participants had a mean sum score of 47, which is mid-range. The scale has a ceiling effect on five items, which was not described in detail earlier. Two items regarding the monetary spending on patients with the given condition had the largest ceiling effects. However, the Norwegian translation showed adequate internal consistency (Cronbach’s Alpha = 0.82) and is reliable over time. Test and re-test showed no significant differences in the scale’s total score (Paired sample T-test, p > 0.05). Exploratory and confirmatory factor analyses indicate that the scale should be used as a one-dimensional instrument in a Norwegian setting in ambulance staff populations. Conclusion: The Norwegian translation of the MCRS is a reliable instrument for ambulance staff measuring medical condition regards. However, the ceiling effect limits the ability to discern differences among high-scoring individuals. Ambulance staff’s regard for patients with psychosis is medium positive (mid-range level), but slightly more positive than what is reported in the international literature regarding patients with mental health issues.
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20507283
|
PSYCHOLOGY
|
10.1007/s00432-024-06058-w
|
Metabolomic profile and its association with the diagnosis of prostate cancer: a systematic review
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Objective: To determine the association of a metabolomic profile with the diagnosis of localized prostate cancer. Methods: We conducted a search strategy in MEDLINE (OVID), EMBASE, LILACS, and the Cochrane Central Register of Controlled Trials (CENTRAL) from 2008 to the present. We included Clinical trials and analytical and descriptive observational studies that reported metabolite results and metabolite profiles in serum, tissue, urine, and seminal fluid. All studies used metabolomic techniques such as MS and MRI to identify patients with localized prostate cancer compared with patients without cancer. We used QUADAS 2 to assess the risk of bias. Results: We found 1248 studies with the search strategy. Finally, 14 case–control studies were included. Serum was the primary sample to identify the metabolites. Low concern was found regarding applying the index test and the reference standard in assessing the risk of bias. The metabolites of interest associated with establishing a metabolomic profile in the diagnosis of localized prostate cancer were amino acids, lipids, androgens, estrogens, nucleotides, and histidine metabolism. Conclusion: Disturbances in the metabolism of fatty acids, amino acids, nucleotides, and steroid hormones were identified, suggesting the presence of localized prostate cancer. Importantly, serum samples showed an increase in amino acid levels. Glutamate and aspartic acid stand out among the amino acids that register high levels. In addition, glycine and serine were consistently decreased metabolites in the three kinds of biological samples analyzed.
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14321335
|
ONCOLOGY
|
10.1007/s00432-024-06071-z
|
Case study of a neuroendocrine tumor of uncertain origin: single-cell transcriptomics unravels potential primary location
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Purpose Determining the primary origin of non-organ-confined neuroendocrine tumors (NETs) for accurate diagnosis and management. Neuroendocrine tumors are rare neoplasms with diverse clinical behaviors. Determining their primary origin remains challenging in cases of non-organ-confined NETs. This study explores the histogenesis of a retroperitoneal, non-functional NET localized between the duodenum and pancreatic head, utilizing advanced molecular diagnostics to elucidate its probable primary source. Methods Initial diagnostic methods, including imaging and histopathology, failed to resolve the tumor’s origin. The tumor was subjected to single-cell RNA sequencing (scRNA-seq) and whole exome sequencing (WES). Publicly available transcriptomic datasets from pancreatic and small intestine NETs were used to develop and validate a molecular gene signature for tissue-of-origin identification. Results The gene signature distinguished pancreatic and small intestine NETs with high accuracy. The tumor cells presented a molecular profile consistent with a pancreatic origin, likely derived from ectopic pancreatic tissue. Conclusions This case demonstrates the value of integrating scRNA-seq and WES for the molecular characterization of complex NETs. Identifying the tumor’s pancreatic origin informed a targeted management approach, avoiding unnecessary systemic treatment and underscoring the potential of single-cell approaches in personalized oncology.
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14321335
|
ONCOLOGY
|
10.1186/s40359-024-02322-3
|
Investigating the effect of mindfulness training for stress management in military training: the relationship between the autonomic nervous system and emotional regulation
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Military personnel face an increased risk of developing mental disorders owing to the stressful environments they encounter. Effective stress management strategies are crucial to mitigate this risk. Mindfulness training (MT) is promising as a stress management approach in such demanding settings. This study uses a quantitative approach to investigate the impact of MT on the relationship between the autonomic nervous system (ANS) and emotional regulation. The study evaluated the effectiveness of MT in reducing stress among 86 military personnel. Participants were divided into two groups: MT (n = 42) and non-MT (n = 38). The study compared the two groups using measures of heart rate variability (HRV), a reliable indicator of ANS activity. The MT group exhibited a significant increase in HRV (14.4%, p = 0.001) and alpha asymmetry (AA) in the frontal lobe (45.7%, p < 0.001) compared to the non-MT group. Notably, the MT group achieved significantly higher scores on the parachute landing fall (PLF) training performance (p < 0.001). These improvements in HRV, AA, and PLF performance were strongly correlated. Furthermore, AA fully mediated the relationship between HRV and PLF training performance. The findings suggest that MT has a positive impact on stress resilience, potentially by mitigating anxiety and attention deficits induced by extreme stressors. These positive effects are facilitated by concurrent modulation of the frontal cortex and autonomic nervous system. Our findings provide insight into the neural mechanisms behind MT-induced stress reduction from the perspective of neuromodulation.
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20507283
|
PSYCHOLOGY
|
10.1186/s40359-025-02347-2
|
Psychological impact and coping mechanisms among sudanese medical students: a study on anxiety, depression, behavioral, and cognitive changes post COVID-19 lockdown and ongoing conflict
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Mental health is crucial for overcoming obstacles, completing tasks, and contributing to society. Mental, social, and cognitive healths are included. In demanding fields like medicine, academic pressure can cause exhaustion, poor performance, and behavioral changes. Mental health must be addressed to improve student success and well-being. Medical students’ coping strategies, anxiety, depression, and behavioral changes in uncontrollable situations will be studied. A cross-sectional study involved 393 medical students from various universities in Khartoum. Data was collected using an online questionnaire to assess mental health responses during both controllable and uncontrollable situations across all academic years. Data analysis using SPSS 27 indicated minimal missing data (0.25%) among the 393 participants. PHQ-4 scores assessed psychological distress, anxiety, and depression. The study found that 74.2% of participants experienced behavioral, cognitive, and emotional changes. Significant associations were observed between PHQ-4 scores and these changes (p < .05) using Chi-Square testing. Most participants were females aged 20 to 22, primarily from the Medicine and Pharmacy departments. The study revealed that most individuals utilized pharmacological coping strategies following significant life changes due to uncontrollable situations. The study highlights that women experienced stress, dissatisfaction, concern, and anger more frequently than men during ongoing war and the post-COVID-19 lockdown. Medical students faced substantial challenges in behavior, emotions, and cognition during societal unrest, including fatigue, feelings of failure, and sleep disturbances. Over 74% reported multiple changes in their emotions and behaviors. Coping strategies included nicotine, sleeping aids, socializing, exercise, venting, meditation, and journaling.
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20507283
|
PSYCHOLOGY
|
10.3389/feduc.2024.1502449
|
A culturally relevant, imbued, and sustaining pedagogy framework for culturally connected math curriculum
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This article introduces the CRISP (Culturally Relevant, Imbued, and Sustaining Pedagogy) framework in the context of a three-course sequence, “Indigenous Math I, II, and III,” taught at Turtle Mountain College. These three courses seek to revitalize mathematical ways of knowing embedded within the Turtle Mountain language(s) and culture(s). The Indigenous Math framework and Indigenous Math Education framework guide these three courses, as well as the Secondary Math Education bachelor’s degree program that spurred development of these courses. Discussing the relationship (i.e., connections, similarities, differences) between Western math and Indigenous math is central to these courses. The CRISP framework extends this discussion by describing four significant components of revitalizing and teaching Indigenous math. Multiple Indigenous math examples are shared as evidence for the value of the CRISP framework.
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2504284X
|
EDUCATION
|
10.3389/feduc.2024.1485425
|
Drawing the future: gender and future occupational aspirations of young children in Sweden
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Introduction: Research on young children’s occupational aspirations and the factors shaping them is still limited, especially in early interventions addressing gender disparities in high-status fields like STEM.Methods: This is the first study in Sweden utilizing the Drawing the Future method, surveyed 1,832 children (aged 5–13) from 28 schools in Skåne region of southern, asking them to draw their dream jobs. This exercise was conducted in a classroom setting and facilitated by their class teacher.Results: Significant gender differences emerged, revealing distinct stereotypical patterns in children’s future occupational aspirations and influencing factors. Only three occupations—footballer, doctor, and police officer—were popular among both genders. Girls preferred people- or animal-centered roles, while boys leaned toward jobs involving “things” (p < 0.001). Girls felt they could pursue similar careers as boys, but boys showed more skepticism (p < 0.001). Influence patterns also varied by gender: 25% of girls were inspired by mothers, while 45% of boys were inspired by fathers (p = 0.02). Beyond immediate family, girls often sought career information from acquaintances, while boys turned to media (p < 0.001). STEM interest was limited, with “game developer” being the only STEM job on boys’ lists. Additionally, a larger proportion of boys ranked STEM subjects among their top 10 favorite school subjects, while girls preferred crafts, art, and English (p < 0.001).Discussion: These findings highlight the need for early, unbiased, evidence-based career interventions and policies to broaden children’s awareness of diverse job options and opportunities in the labor market.
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2504284X
|
EDUCATION
|
10.1186/s40594-024-00521-3
|
How gamification boosts learning in STEM higher education: a mixed methods study
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The demand for professionals with expertise in Science, Technology, Engineering, and Mathematics (STEM) continues to grow. To meet this demand, universities are actively seeking strategies to engage more students in STEM disciplines and improve their learning outcomes. One promising approach is gamification, specifically using leaderboards. This study investigates the impact of leaderboard-based gamification on the learning performance of 175 students in a calculus course, with a focus on the mediating roles of autonomous motivation and self-efficacy, as well as potential moderating factors such as gender and gaming experience. A mixed-method research approach was employed, combining a pretest–posttest quasi-experimental design with nine qualitative interviews. A significant improvement in learning performance for students in the gamified condition was observed. However, no significant effects were found related to the mediating variables. Qualitative analysis supported these findings, revealing that students did not perceive an increase in autonomy within the gamified condition, and instead, themes of controlled motivation were prevalent. While the leaderboard provided a sense of achievement for most participants, the quantitative analysis did not show a strong correlation between self-efficacy and learning performance. This study suggests that leaderboard-based gamification can enhance learning performance in calculus courses at the university level. However, the findings highlight the importance of careful gamification design, particularly in how different game elements influence students' motivational aspects.
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21967822
|
EDUCATION
|
10.3389/fonc.2024.1498524
|
Characterizing microbial communities and their correlation with genetic mutations in early-stage lung adenocarcinoma: implications for disease progression and therapeutic targets
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Background: Lung adenocarcinoma (LUAD), the most prevalent form of lung cancer. The transition from adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC) is not fully understood. Intratumoral microbiota may play a role in LUAD progression, but comprehensive stage-wise analysis is lacking.Methods: Tumor and bronchoalveolar lavage fluid (BALF) samples from patients with AIS/MIA or IAC were collected for next-generation sequencing to characterize microbial diversity and composition. DNA extraction involved lysing samples with nuclease and protease, followed by homogenization and elution. Sequencing libraries were prepared and sequenced on the Illumina platform. Whole exome sequencing was performed to identify somatic mutations and genetic variants. Bioinformatics analysis, including taxonomic annotation with Kraken2 and de novo assembly with MEGAHIT, was conducted to process metagenomic data. Correlation analysis was performed to link microbial species with mutated genes using custom R scripts.Results: Metagenomic analysis revealed a distinct microbial profile in IAC compared to AIS/MIA, with increased abundance of Bacteroidetes and Firmicutes in the IAC group. Bosea sp. and Microbacterium paludicola, were less abundant in IAC, suggesting a potential protective role in early-stage disease. Conversely, Mycolicibacterium species were more prevalent in IAC, indicating a possible contribution to disease progression. Genetic sequencing identified PTPRZ1 strongly correlating with microbial composition, suggesting a mechanistic link between microbiota and genetic alterations in LUAD.Conclusion: This study characterizes microbial communities in various stages of LUAD, revealing links between microbiota and genetic mutations. The unique microbiota suggests its role in LUAD progression and as a therapeutic target.
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2234943X
|
ONCOLOGY
|
10.3389/feduc.2024.1502396
|
The sound of science: a sonification learning experience in an Italian secondary school
|
Introduction: The present article reports on a case study aimed at improving STEAM education in secondary schools. It discusses the use of sonification as a teaching strategy to integrate music into science learning, using different approaches from data audification to parameter mapping into aural models and to the rewriting of song lyrics based on STEM topics.Methods: A qualitative research study has been performed in a secondary school in the school district of Bari (South of Italy). More specifically, students’ and experts’ perceptions of experienced sonification activities have been collected through six rounds of focus group interviews.Results: While there was a good improvement in student achievement in science, it is worth noting how musical activities also led to some benefits for students involved in the sonification workshops. The integration of music with STEM disciplines has promoted more cooperation and empathy among the students. Additionally, musical inputs can help students discover and regain interest in music. However, the study also highlighted the differences in teacher training and content knowledge, suggesting the need for future research to consider broader samples and experimental designs.Discussion: Results and implications for educational research and practice are discussed considering the recent literature on STEAM. Finally, this study demonstrates the importance of a robust instructional design for sonification activities, so that they can be more effective, aligned with the school curriculum, and integrated into the classroom teaching-learning process.
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2504284X
|
EDUCATION
|
10.3389/frai.2024.1546421
|
Corrigendum: Person-based design and evaluation of MIA, a digital medical interview assistant for radiology
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Corrigendum on: Denecke K, Reichenpfader D, Willi D, Kennel K, Bonel H, Nairz K, Cihoric N, Papaux D and von Tengg-Kobligk H (2024) Person-based design and evaluation of MIA, a digital medical interview assistant for radiology. Front. Artif. Intell. 7:1431156. doi: 10.3389/frai.2024.1431156 In the published article, there was an error in the Data Availability statement. We were missing to add the links to the repositories mentioned in the paper. The correct Data Availability statement appears below.
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26248212
|
AI
|
10.3389/frai.2024.1499530
|
Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets
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Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.Methods: A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. The framework integrates feature engineering and resampling strategies to enhance predictive accuracy.Results: Rigorous testing was conducted on three datasets—PIMA, Diabetes Dataset 2019, and BIT_2019—demonstrating the robustness and adaptability of the methodology across varying data environments.Discussion: The experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study offers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy.
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26248212
|
AI
|
10.3389/fpsyg.2024.1496140
|
Comprehensibility of gender-fair language in German-language video lectures
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In many languages, it is common to use masculine-only forms when all genders are meant or gender is irrelevant to the actual statement. This practice is criticized for making women and members of other genders, their achievements and interests, less visible. Gender-fair language is intended to represent all genders equally. Recently introduced forms such as the glottal stop and the gender star are intended to also represent people outside the male–female dichotomy on the linguistic surface. However, it is often argued that gender-fair language would make texts less comprehensible and less aesthetically appealing. The critics’ assumptions were tested in an experiment with 272 participants. Subjects watched a screencast on self-regulated learning and were randomly assigned to either a version using masculine-only forms or a version using the glottal stop and the gender star. Subsequently, participants rated the comprehensibility and aesthetic appeal of the video they had watched. Structural equation models show no statistically significant influence of the use of gender-fair language on the comprehensibility (β = −0.13) or the aesthetic appeal (β = −0.16) of the videos. The critics’ assumptions are therefore not supported. But further studies are needed, especially regarding the corresponding singular forms and with non-academic participants.
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16641078
|
PSYCHOLOGY
|
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