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10.3389/feduc.2024.1358620
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The CABANA model 2017–2022: research and training synergy to facilitate bioinformatics applications in Latin America
|
The CABANA project (Capacity Building for Bioinformatics in Latin America) was funded by the UK’s Global Challenges Research Fund in 2017 with the aim to strengthen the bioinformatics capacity and extend its applications in Latin America focused on three challenge areas – communicable diseases, sustainable food production and protection of biodiversity. For 5 years, the project executed activities including data analysis workshops, train-the-trainer workshops, secondments, eLearning development, knowledge exchange meetings, and research projects in 10 countries. The project was successful in accomplishing all its goals with a major impact on the region. It became a model by which the research needs determined the training that was delivered. Multiple publications and over 800 trainees are part of the legacy of the project.
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2504284X
|
EDUCATION
|
10.3390/ejihpe14070130
|
VISCERAL SENSITIVITY INDEX (VSI-IT): Italian Adaptation and Validation
|
The Visceral Sensitivity Index (VSI) represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with irritable bowel syndrome (IBS) and chronic inflammatory bowel diseases (IBD)—such as ulcerative colitis and Crohn’s disease. However, an Italian version of the instrument is not yet available for the Italian-speaking population. This study utilized a national sample of 500 individuals divided into four groups: (a) patients with Crohn’s disease, (b) patients with ulcerative colitis, (c) patients with IBS, and (d) healthy controls (individuals without any diagnoses) to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, this research applied a questionnaire and the VSI through an online format to 500 participants. Confirmatory Factor Analysis (CFA) revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach’s α = 0.949) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with interoceptive awareness, type of disease, and gastrointestinal symptom severity, confirming its role in enhancing the understanding and management of IBD and IBS in Italy.
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22549625
|
PSYCHOLOGY
|
10.1007/s00432-024-05873-5
|
Nasopharyngeal amyloidoma: report of three cases and review of the literature
|
Background: Nasopharyngeal amyloidoma is a rare, locally aggressive tumor that has been reported in the English literature in only 38 cases to date, most of which were in the form of case reports. The present study was aimed to summarize the characteristics of this rare tumor, with the goal of providing new insights for diagnosis and treatment. Materials and methods: We report three cases of nasopharyngeal amyloidoma diagnosed in our hospital following comprehensive medical examination and review the current literature on all cases of nasopharyngeal amyloidoma from PubMed. The journey of nasopharyngeal amyloidoma, including presentation, diagnostics, surgeries, and follow-up was outlined. Results: None of the three patients had systemic amyloidosis. CT and nasal endoscopy showed irregular masses obstructing the nasopharyngeal cavity. Congo red staining confirmed the deposition of amyloid, and immunohistochemical analysis showed that the amyloid deposition was the AL light chain type. Through literature review, we found that nasopharyngeal amyloidoma most commonly occurred in individuals over the age of 40, patients usually had a good prognosis after complete tumor resection; however, there were still cases of recurrence, and unresected patients were at risk of progression to systemic amyloidosis. The efficacy of radiotherapy and chemotherapy was currently uncertain. Conclusion: Early clinical and pathological diagnosis is crucial, and surgical intervention is the primary treatment option for this disease. Although patients usually have a favorable prognosis, long-term monitoring is necessary to detect potential relapses and initiate timely intervention.
|
14321335
|
ONCOLOGY
|
10.3390/cancers16132488
|
A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity
|
The development of cancer involves the accumulation of somatic mutations in several essential biological pathways. Delineating the temporal order of pathway mutations during tumorigenesis is crucial for comprehending the biological mechanisms underlying cancer development and identifying potential targets for therapeutic intervention. Several computational and statistical methods have been introduced for estimating the order of somatic mutations based on mutation profile data from a cohort of patients. However, one major issue of current methods is that they do not take into account intra-tumor heterogeneity (ITH), which limits their ability to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach to estimate the temporal order of mutations at the pathway level by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a maximum likelihood approach to estimate the probability of pathway mutational events occurring in a specific sequence, wherein it focuses on the orders that are consistent with the phylogenetic structure of the tumors. Applications to whole exome sequencing data from The Cancer Genome Atlas (TCGA) illustrate our method’s ability to recover the temporal order of pathway mutations in several cancer types.
|
20726694
|
ONCOLOGY
|
10.3390/educsci11030094
|
Increasing Requests for Information by Preschoolers with and without Language-Based Disabilities
|
We report two experiments on the emission of questions to request the names of unfamiliar stimuli by preschoolers. In the first experiment, 19 preschoolers with and without disabilities served as participants. Experiment 1 was a descriptive analysis of whether or not the 19 participants asked questions about unfamiliar pictures and objects in one-to-one and group settings. These were dependent variables in the second experiment as well. Four participants, who did not ask any questions in the first experiment, served as participants in the second experiment. During the intervention, the participants observed the peer confederates (1) ask questions (e.g., “What is that?”), (2) receive information from the experimenter, and (3) receive praise and tokens contingent on asking a question. A multiple probe design across participants was used. The data showed that the participants increased the number of questions when we returned to baseline conditions. Results are discussed in terms of where the reinforcement exists for asking questions about unfamiliar things in one’s environment, and whether this truly measures the “need to know”.
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22277102
|
EDUCATION
|
10.1186/s40359-024-01870-y
|
Chinese version of the Tendency to Avoid Physical Activity and Sport (TAPAS) scale: testing unidimensionality, measurement invariance, concurrent validity, and known-group validity among Taiwanese youths
|
Background and objectives: Psychosocial factors affect individuals’ desire for physical activity. A newly developed instrument (Tendency to Avoid Physical Activity and Sport; TAPAS) has been designed to assess the avoidance of physical activity. Considering cultural differences could be decisive factors, the present study aimed to translate and validate the TAPAS into Chinese (Mandarin) for Taiwanese youths, and further cultural comparisons are expected. Methods: Standard translation procedure (i.e., forward translation, back translation, and reconciliation) was used to translate the English TAPAS into the Chinese TAPAS. Following translation, 608 youths (mean [SD] age 29.10 [6.36] years; 333 [54.8%] women) participated in the study via a snowballing sampling method with an online survey. All participants completed the Chinese TAPAS and additional instruments assessing weight stigma and psychological distress. Confirmatory factor analysis (CFA) was used to examine the factor structure of the Chinese TAPAS and multigroup CFA to examine measurement invariance across gender (men vs. women) and weight status (overweight vs. non-overweight). Pearson correlations were used to examine the concurrent validity; independent t-tests between gender groups and weight status groups were used to examine the known-group validity. Results: Consistent with the English version, the Chinese TAPAS was found to have a one-factor structure evidenced by CFA results. The structure was invariant across gender and weight status groups evidenced by multigroup CFA results. Concurrent validity was supported by significant associations with the related constructs assessed (r = 0.326 to 0.676; p < 0.001). Known-group validity was supported by the significant differences in TAPAS total scores between gender and weight status groups (p = 0.004 and < 0.001; Cohen’s d = 0.24 and 0.48). Conclusion: The Chinese version of the TAPAS is a valid and reliable instrument assessing individuals’ avoidance of physical activity and sports due to underlying psychosocial issues among Taiwanese youths. It is anticipated to be applied within a large Asian population, as well as cross-cultural comparisons, for further explorations in health, behavioral and epidemiological research and practice.
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20507283
|
PSYCHOLOGY
|
10.3389/fpsyg.2024.1425359
|
A study on the relationship between yoga exercise intervention and the comprehensive well-being of female college students
|
Background: Due to the influence of theories, tools, and methodologies in studying well-being, sports science has predominantly focused on subjective well-being, with less attention given to psychological well-being and even less to the integrated study of comprehensive well-being. This study aims to analyze the relationship between yoga exercise intervention and the comprehensive well-being of college students and to explore the mechanism of a yoga exercise intervention to improve the comprehensive well-being of female college students.Methods: With 92 female college students as subjects, the “Comprehensive Well-being Scale” was used, and research methods such as yoga exercise intervention, questionnaire surveys, qualitative analysis, expert interviews, and statistical analysis were employed to investigate the role of a yoga exercise intervention on the comprehensive well-being of female college students.Results: Among the nine dimensions of comprehensive well-being, the three dimensions of subjective well-being and the two dimensions of psychological well-being (health concern and personality growth) of female college students were significantly improved. Additionally, four other dimensions of psychological well-being also showed significant improvement. Furthermore, the improvement in the life satisfaction of female college students’ subjective well-being was mainly achieved through yoga meditation, while partner yoga posture practice could help individuals gradually form a stable pattern of altruistic behavior.Conclusion: Yoga exercise intervention can improve the comprehensive well-being of female college students and can be an effective counseling method for college students’ mental health education.
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16641078
|
PSYCHOLOGY
|
10.3389/fpsyg.2024.1426450
|
The impact of negative urgency on implicit mobile phone addiction tendency among college freshmen in the context of social exclusion
|
Purpose: The purpose of this study is to investigate the impact of negative urgency on implicit mobile phone addiction tendency among college freshmen, and to observe whether social exclusion situations affect the relationship between negative urgency and implicit mobile phone addiction tendency.Methods: The UPPS-P Impulsive Behavior Scale was used to screen 575 freshmen from a certain university. The experiment utilized a GO/NO-GO paradigm. Experiment 1 employed a 2 (negative urgency group: high negative urgency group, low negative urgency group) × 2 (word type: phone related words, phone non-related words) two-factor mixed experimental design. Experiment 2 employed a 2 (negative urgency group: high negative urgency group, low negative urgency group) × 2 (social exclusion type: priming group, non-priming group) × 2 (word type: phone related words, phone non-related words) three-factor mixed experimental design.Results: Experiment 1 results showed a significant main effect of negative urgency group and a significant interaction effect between negative urgency group and word type. Experiment 2 results demonstrated a significant main effect of negative urgency group and a significant main effect of social exclusion type. There was a significant interaction effect between word type and social exclusion type, as well as between word type and negative urgency group. The three-way interaction effect among negative urgency group, word type, and social exclusion type was significant.Conclusion: College freshmen with high negative urgency exhibit a higher tendency toward implicit mobile phone addiction. In social exclusion situations, college freshmen show a higher tendency toward implicit smartphone addiction. Social exclusion situations and negative urgency jointly influence the implicit mobile phone addiction tendency of college freshmen.
|
16641078
|
PSYCHOLOGY
|
10.1007/s00432-024-05866-4
|
Association of body composition indicators with colorectal cancer: a hospital-based case-control study
|
Purpose: Colorectal cancer (CRC) is a common malignancy that affects adults worldwide, causing a high disease burden. Few studies have examined the relationship between body composition (BC) measures and the prevalence of CRC. Our purpose was to investigate the relationship between pertinent BC indicators and CRC. Methods: Bioelectrical impedance analysis, laboratory test results, face-to-face questionnaire investigation, and nutritional risk assessment (Nutritional Risk Screening 2002 and Patient-Generated Subjective Global Assessment) were used in this case-control study. Bioelectrical impedance analysis in the case group was performed prior to antitumor therapy/surgery. Results: From June 2018 to January 2019, a total of 303 cases and 286 controls were included. The results showed that low body fat percentage (BFP) and high visceral adiposity index (VAI) groups had a higher risk of developing CRC in comparison to the normal BFP and normal VAI groups. The risk of CRC decreased with the increase of BFP. The group with a normal BC had a lower risk of developing CRC compared to those with a greater VAI and a lower BFP, as indicated by the results of the pairwise and total combinations of VAI, fat-free mass index (FFMI), and BFP. Additionally, FFMI and VAI had positive correlations with prealbumin, serum albumin, and nutritional risk scores. Conclusion: Low BFP and high VAI are associated with higher CRC risk. FFMI and VAI are positively correlated with prealbumin, serum albumin, and nutritional risk scores in CRC patients.
|
14321335
|
ONCOLOGY
|
10.1007/s44196-024-00544-2
|
Bipolar Neutrosophic Dombi-Based Heronian Mean Operators and Their Application in Multi-criteria Decision-Making Problems
|
Dombi operations based on the Dombi t-norm (TN) and t-conorm (TCN) have the advantage in terms of operational parameter flexibility in dealing with varying degrees of uncertainty and aggregation requirements. Meanwhile, Heronian mean (HM) operator is an effective technique for capturing the interrelationship between any number of inputs. Bipolar neutrosophic set (BNS) offers the ability to represent both positive and negative information as well as indeterminate information. It is beneficial in cases where there is uncertainty or insufficient information. However, the existing Dombi operator under BNS do not take into account the interrelationship between input arguments. To overcome this limitation, this study incorporates Dombi operator into HM and propose the bipolar neutrosophic Dombi Heronian mean aggregation operator. This paper introduces two type of aggregation operators namely bipolar neutrosophic Dombi-based generalized weighted Heronian mean (BND-GWHM), and bipolar neutrosophic Dombi-based improved generalized weighted Heronian mean (BND-IGWHM). The proposed operators are integrated into MCDM procedure. The influence of different parameter values on decision-making results is discussed. Finally, a comparison analysis with existing methods is also provided.
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18756883
|
AI
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10.1186/s40359-024-01875-7
|
Validation of a German version of the caregiver strain questionnaire-short form 11 (CGSQ-SF11)
|
Objective: Caring for a child, particularly one with special healthcare needs, is a demanding task that can lead to the experience of caregiver strain. This in turn has an effect on the caregiver’s mental health, as well as on the child and his or her treatment. To enable the identification of afflicted parents, this study aims to provide a German version of the Caregiver Strain Questionnaire–Short Form 11 (CGSQ-SF11) and to examine its factor structure and psychometric properties. Methods: Data from 698 caregivers were included in the analyses. Caregivers completed the CGSQ-SF11 along with measures of parenting stress (PSI-SF), stress (PSS-10), anxiety (GAD-7), depression (PHQ-8), family-related quality of life (FLQ), and social desirability (SES-17) as additional instruments for validation. A two-week follow-up questionnaire included only the CGSQ-SF11. Exploratory factor analysis followed by a confirmatory factor analysis was conducted for parents of children with and without special healthcare needs, separately. Further analyses examined the validity and reliability of the instrument. Results: For parents of children with special healthcare needs, a three-factor structure (objective, internalized subjective, externalized subjective strain) with a second-order factor (caregiver strain) was supported. For parents of children without special healthcare needs, a similar three-factor structure was found, although the second-order factor was not supported. Measurement invariance between the two groups was not confirmed. Internal consistency, test-retest reliability, and validity were largely supported in both groups. Conclusions: The results indicate that the German version of the CGSQ SF-11 is a valid and reliable questionnaire for measuring caregiver strain.
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20507283
|
PSYCHOLOGY
|
10.3389/feduc.2024.1252222
|
A latent class analysis on students' beliefs about teachers' practices enhancing their well-being
|
Student well-being and student voice are two interrelated concepts that can play a critical role in education. While Student well-being refers to the overall state of students' physical, mental, and emotional health, student voice represents the active involvement and participation of students in shaping their own educational experiences. Notwithstanding the intimate association, there is a limited body of research that explores how students' distinct perceptions of teachers' practices that promote their well-being influence students' actual well-being levels. To address this research gap, a study was conducted involving 486 students. The participants, with an average age of 13.5 years, completed a questionnaire. Among the participants, 51.1% identified as female, and 13.6% had experienced academic retention. The latent class results classified the 7–9 grade student's beliefs about teacher's practices into “few times,” sometimes' and “often.” The model fitting results were as follows: Akaike Information Criterion (AIC) was 2,555.904, Bayesian Information Criterion (BIC) was 2,610.244, Adjusted Bayesian Information Criterion (aBIC) was 2,568.983, and Entropy was 0.802. Compared with the “few times” and “sometimes” class, the “often” class was more prevalent in 8th grade (p = 0.05) and among male students (p = 0.04). Findings show that class membership is a predictor of student well-being (interpersonal, life satisfaction and perceived competence). Students who feel that their teachers are attentive, supportive, and address their needs more frequently are more likely to experience enhanced well-being.
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2504284X
|
EDUCATION
|
10.3389/feduc.2024.1418398
|
Research approaches in master-based teacher education preparing student teachers for professional work
|
Student teachers have been found to be critical toward the research approaches they learned from their master's-based teacher education programmes. Our aim is to discuss how certain research approaches learnt during a 5-year academic master's level teacher education, may bring student teachers close to practice and provide them with conceptual and practical tools for a thorough understanding of the practice of teaching. The argumentation is based on an elaboration of master's-based teacher education programs in Finland and Norway and the essential characteristics of teachers' work. We elaborate on student teachers' need to understand constative, critical and constructive research approaches. This includes critical approaches such as observations and interviews for understanding and interpretation, and constructive approaches such as action research and lesson studies. Finally, we argue that, through these approaches, student teachers make use of research knowledge in teachers' work with an inquiring orientation as well as develop and change their practice.
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2504284X
|
EDUCATION
|
10.3390/ejihpe14070135
|
Determinants of Inequalities in the Exposure to and Adoption of Multiple Health Risk Behaviors among Brazilian Adolescents, 2009–2019
|
The occurrence of multiple risk behaviors among adolescents imposes challenges in the context of public policies of health, particularly in low- and middle-income countries. Evidence on the conditions leading to the exposure to and adoption of multiple risk behaviors allows the identification of vulnerable groups of adolescents, and may support the proposition of targeted strategies directed to individuals at risk. Therefore, the aim of this study was to perform a quantitative analysis to identify recent trends in the exposure to and adoption of multiple health risk behaviors among Brazilian adolescents, highlighting individual-, household-, and school-level characteristics linked to inequalities among social groups. The analysis was based on cross-sectional data from the National Student Health Survey (PeNSE), conducted by the Brazilian Institute for Geography and Statistics in 2009, 2012, 2015, and 2019. The trends in the occurrence of multiple risk behaviors among adolescents were estimated according to social strata, allowing the calculation of concentration indexes and their disaggregation into major determinants of inequalities in the exposure and adoption of risk behaviors. The analyses were conducted using a complex survey design to allow representativeness at the population level. The results showed a rise in the incidence of multiple risk behaviors among youngsters in Brazil from 2009 to 2019. Factors influencing inequalities in the exposure to multiple risk behaviors were socioeconomic status and the characteristics of the household and school environments, whilst the adoption of multiple risk behaviors was also influenced by early exposure to multiple risk behaviors. Furthermore, trends in inequalities in the exposure to and adoption of multiple risk behaviors showed an intensification from 2009 to 2019, being initially concentrated among wealthier adolescents, followed by a transition to higher incidence in the lower socioeconomic strata in 2012 and 2015, respectively. The findings underscore the role of support systems for adolescents at risk within the familial and school contexts, whereas strategies of public policies of health based on the strengthening of community ties may require improvements to tackle socioeconomic inequalities in the occurrence of risk behaviors among youngsters.
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22549625
|
PSYCHOLOGY
|
10.1186/s40594-024-00490-7
|
Employing technology-enhanced feedback and scaffolding to support the development of deep science understanding using computer simulations
|
Constructivist learning theories consider deep understanding of the content to be the result of engagement in relevant learning activities with appropriate scaffolding that provides the learner with timely and substantive feedback. However, any group of students has a variety of levels of knowledge and cognitive development, which makes providing appropriate individual-level scaffolding and feedback challenging in the classroom. Computer simulations can help meet this challenge by providing technology-enhanced embedded scaffolding and feedback via specific simulation design. The use of computer simulations does not, however, guarantee development of deep science understanding. Careful research-driven design of the simulation and the accompanying teaching structure both play critical roles in achieving the desired learning outcomes. In this paper, we discuss the capabilities of computer simulations and the issues that can impact the learning outcomes when combining technology-enhanced scaffolding and feedback with external teaching structures. We conclude with suggestions of promising research avenues on simulation design and their use in the classroom to help students achieve deep science understanding.
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21967822
|
EDUCATION
|
10.3389/fonc.2024.1403522
|
Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound
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Purpose: To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer.Materials and methods: 149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model’s stability was assessed through AUC, calibration curves, and DCA.Results: Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net.Conclusion: The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.
|
2234943X
|
ONCOLOGY
|
10.3389/fpsyg.2024.1389581
|
The geo domain: a review on the conceptualization of geographical and geopolitical entities
|
Investigating how people represent the natural environment and abstract it into geographical (e.g., mountain) and geopolitical (e.g., city) categories is pivotal to comprehending how they move and interact with the places they inhabit. Yet, the conceptualization of geographical and geopolitical domains has received scant attention so far. To deal with that, we reviewed 50 articles tackling this topic. Most studies have focused on assessing the universality of these concepts—especially geographical ones—mainly using free-listing and ethnophysiographic methods. Current perspectives tend to favor a non-universalistic characterization of these kinds of concepts, emphasizing their high cross-linguistic and cross-cultural variability, especially when compared to other semantic domains. Since geographical and geopolitical features are not pre-segmented by nature, the role of categories imposed by humans is crucial for these concepts. Significantly, their variability does not only depend on “cross” differences: evidence suggests that the cognitive demand requested by the task, idiosyncratic characteristics of individuals such as expertise level, and the typology of inhabited environments are further factors impacting the conceptual flexibility of these domains. Exploring the factors influencing our understanding of geographical and geopolitical categories can provide valuable insights for instructing effective communication policies to enhance sustainable development and address ecological emergencies, taking into consideration diverse cultural backgrounds within different populations.
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16641078
|
PSYCHOLOGY
|
10.1186/s40359-024-01886-4
|
Emotional intelligence impact on academic achievement and psychological well-being among university students: the mediating role of positive psychological characteristics
|
The main objective of this study is to examine the relationship of emotional intelligence with psychological well-being and academic achievement through positive psychological characteristics among university students in China. The study was conducted with postgraduate and undergraduate students. The integration of emotional intelligence theory and positive psychological theory was used in this study. The introduced framework included emotional intelligence as the main independent variable, self-efficacy, motivation, and resilience as three mediators, and psychological well-being and academic achievement as two dependent variables. A survey was conducted among 518 students, and structural equation modelling was used to analyse the data. The study found that emotional intelligence was positively related to positive psychological characteristics, psychological well-being, and academic achievement, and the effects were stronger among postgraduate students. Also, positive psychological characteristics, which include self-efficacy, motivation, and resilience, mediate the relationship between emotional intelligence and psychological well-being and academic achievement, and the relationship was stronger among postgraduate students. Proper coping strategies and mechanisms can be helpful to improve both psychological well-being and academic achievement at the same time among university students.
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20507283
|
PSYCHOLOGY
|
10.1186/s40594-024-00491-6
|
The development of mathematics expectancy-value profiles during the secondary–tertiary transition into STEM fields
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Background: To master the secondary–tertiary transition into fields of science, technology, engineering, and mathematics (STEM), academic self-beliefs play a pivotal role, especially those related to learning mathematics. The framework of expectancy-value theory has been used widely in primary and secondary education and partly in tertiary education to assess the self-beliefs of students in terms of expectancy of success and perceived value of mathematics. Based on this framework, we measured how the intrinsic value, the attainment value, the utility value, and the cost of learning mathematics as well as the expectancy of success when learning mathematics developed during the secondary–tertiary transition of students into STEM fields. Data were collected in a quantitative repeated-measures questionnaire study with two measurement points (measurement point 1: n = 710, measurement point 2: n = 487, listwise: n = 409). We conducted a latent profile analysis to identify the prevalent patterns of mathematics self-beliefs, called profiles, at each of the two measurement points. We studied the relation of these profiles to prior education, achievement at school, and achievement at university. By performing a latent transition analysis, we determined the probabilities of transitioning from the initial profiles to the posterior profiles. Results: Our analysis revealed four distinct prevalent profiles at each measurement point, ranging from highly favorable (i.e., high expectancy, high value, low cost) to highly unfavorable with respect to learning mathematics. The profiles with favorable manifestations remained stable over time, while those with undesirable manifestations deteriorated further. We observed a sharp increase in cost across all profiles. Prior achievement correlated strongly with profile membership. Conclusions: The expenditure of time and energy increased sharply during the secondary–tertiary transition, independently of the students’ initial motivational patterns. The perceived utility of mathematics for potential future careers was shown to be a significant source of motivation. The role of mathematics in future careers should thus be made visible in university teaching. Keeping the detrimental development of initially undesirable motivational profiles in mind, university teachers should create ample opportunities for students to gain a sense of accomplishment.
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21967822
|
EDUCATION
|
10.3389/fpsyg.2024.1436216
|
Music performance anxiety: development and validation of the Portuguese music performance anxiety scale
|
Several studies have developed and validated specific scales to understand, identify and confirm research hypotheses associated with music performance anxiety (MPA). These scales mostly assess behavioral, cognitive, and physiological factors. There is currently no original MPA assessment tool for higher music education in Continental Portuguese, which suggests a research gap. The aim of this study was to determine if the Portuguese Music Performance Anxiety Scale (PoMPAS), developed for this research, is a valid and reliable measure of MPA for the context of higher education in Portugal. The total sample was N = 414 (166 male, 245 female, and three without gender identification). The development of this scale was based on a three-dimensional model (behavioral, cognitive, and physiological), following the theoretical models of Salmon (1990) and Osborne and Kenny (2005). Confirmatory factor analysis of the PoMPAS suggested a good fit in a three-dimensional model with 27 items. The internal consistency values proved appropriate, showing good Cronbach’s alphas (between α = 0.81 and α = 0.90). The McDonald’s Omega also demonstrated good consistency (between ω = 0.81 and ω = 0.90). The PoMPAS is a reliable tool to measure the impact of MPA, with good psychometric qualities, specifically for the Portuguese higher music education context.
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16641078
|
PSYCHOLOGY
|
10.1007/s00432-024-05869-1
|
Targeting CD73 limits tumor progression and enhances anti-tumor activity of anti-PD-1 therapy in intrahepatic cholangiocarcinoma
|
Background & aims: Patients with intrahepatic cholangiocarcinoma (iCCA) respond poorly to immune checkpoint blockades (ICBs). In this study, we aimed to dissect the potential mechanisms underlying poor response to ICBs and explore a rational ICB-based combination therapy in iCCA. Methods: scRNA-seq dataset GSE151530 was analyzed to investigate the differentially expressed genes in malignant cells following ICBs therapy. RNA-seq analysis and western blot assays were performed to examine the upstream and downstream signaling pathways of CD73. Subcutaneous tumor xenograft models were utilized to investigate the impact of CD73 on iCCA growth. Plasmid AKT/NICD-induced spontaneous murine iCCAs were used to explore the therapeutic efficacy of CD73 enzymatic inhibitor AB680 combined with PD-1 blockade. Time-of-flight mass cytometry (CyTOF) was conducted to identify the tumor-infiltrating immune cell populations and their functional changes in murine iCCAs treated with AB680 in combination with PD-1 antibody. Results: scRNA-seq analysis identified elevated CD73 expression in malignant cells in response to ICBs therapy. Mechanistically, ICBs therapy upregulated CD73 expression in malignant cells via TNF-α/NF-κB signaling pathway. In vivo studies revealed that CD73 inhibition suppressed the growth of subcutaneous tumors, and achieved synergistic depression effects with gemcitabine and cisplatin (GC). Adenosine produced by CD73 activates AKT/GSK3β/β-catenin signaling axis in iCCA cells. CD73 inhibitor AB680 potentiates anti-tumor efficacy of PD-1 antibody in murine iCCAs. CyTOF analysis showed that AB680 combined with anti-PD-1 therapy promoted the infiltration of CD8+ T, CD4+ T cells, and NK cells in murine iCCAs, while simultaneously decreased the proportions of macrophages and neutrophils. Moreover, AB680 combined with anti-PD-1 significantly upregulated the expression of Granzyme B, Tbet and co-stimulatory molecule ICOS in infiltrating CD8+ T cells. Conclusions: CD73 inhibitor AB680 limits tumor progression and potentiates therapeutic efficacy of GC chemotherapy or anti-PD-1 treatment in iCCA. AB680 combined with anti-PD-1 therapy effectively elicits anti-tumor immune response.
|
14321335
|
ONCOLOGY
|
10.1186/s40594-024-00492-5
|
Exploring the role of disciplinary knowledge in students’ covariational reasoning during graphical interpretation
|
Background: This study investigates undergraduate STEM students’ interpretation of quantities and quantitative relationships on graphical representations in biology (population growth) and chemistry (titration) contexts. Interviews (n = 15) were conducted to explore the interplay between students’ covariational reasoning skills and their use of disciplinary knowledge to form mental images during graphical interpretation. Results: Our findings suggest that disciplinary knowledge plays an important role in students’ ability to interpret scientific graphs. Interviews revealed that using disciplinary knowledge to form mental images of represented quantities may enhance students’ covariational reasoning abilities, while lacking it may hinder more sophisticated covariational reasoning. Detailed descriptions of four students representing contrasting cases are analyzed, showing how mental imagery supports richer graphic sense-making. Conclusions: In the cases examined here, students who have a deep understanding of the disciplinary concepts behind the graphs are better able to make accurate interpretations and predictions. These findings have implications for science education, as they suggest instructors should focus on helping students to develop a deep understanding of disciplinary knowledge in order to improve their ability to interpret scientific graphs.
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21967822
|
EDUCATION
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10.3389/feduc.2024.1224720
|
Disparities in prevalence of screening/monitoring in children with intellectual and developmental disabilities: culturally sensitive provider can mitigate effects
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Introduction: About one in six children in the US, about 17% of the population, have one or more intellectual or developmental disabilities. Increases in disability due to neurodevelopmental or mental health conditions have increased by 21% in the last decade. Early intervention based on developmental screening and provider-initiated monitoring can significantly improve long-term health and cognitive outcomes. This paper assesses whether differences in receipt of developmental screening or monitoring are associated with access to a high-quality primary care medical home and having a provider who shows sensitivity to a family’s customs and values among neurotypical children and children with intellectual and developmental disabilities (IDD).Methods: We used cross-sectional data from the National Survey of Children’s Health (NSCH) from 2017 to 2019. The NSCH is a nationally representative, parent-completed annual survey of children under 18. Children between 9 months and 5 years with IDD (n = 2,385) and neurotypical children (n = 20,200) were included in the analysis.Results: Uptake of developmental screening/monitoring in neurotypical children and children with IDD conditions was associated with belonging to minority race/ethnic backgrounds, specifically Black, Asian, and AIAN/NHPI, and single-parent households with lower incomes, being publicly insured or uninsured and not having access to a high-quality medical home. Weighted regression models showed that the odds of neurotypical children receiving developmental monitoring/screening were 53% higher when their healthcare provider always or usually demonstrated cultural sensitivity to the family’s values and customs (OR 1.53, 95% CI, 1.08–2.18, p < 0.05). For children with IDD, the odds of receipt of monitoring/screening increased by 2.1 times when the provider always/usually demonstrated an understanding of the family’s cultural norms (95% CI, 0.99–4.43, p = 0.053). Being female was significantly associated with a lack of screening/surveillance (OR 0.73, 95% CI, 0.58–0.91, p < 0.05).Discussion: With the rising prevalence of children with IDD conditions, early identification of developmental delays and subsequent access to interventions are crucial steps in supporting children and children with IDD to receive preventive care, services, and reduce disparities in accessing quality care. Implementing culturally sensitive approaches can be a low-cost and effective intervention in improving rates of provider-initiated monitoring and parent-completed screening.
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2504284X
|
EDUCATION
|
10.3390/cancers16142553
|
Imaging and Metabolic Diagnostic Methods in the Stage Assessment of Rectal Cancer
|
Rectal cancer (RC) is a prevalent malignancy with significant morbidity and mortality rates. The accurate staging of RC is crucial for optimal treatment planning and patient outcomes. This review aims to summarize the current literature on imaging and metabolic diagnostic methods used in the stage assessment of RC. Various imaging modalities play a pivotal role in the initial evaluation and staging of RC. These include magnetic resonance imaging (MRI), computed tomography (CT), and endorectal ultrasound (ERUS). MRI has emerged as the gold standard for local staging due to its superior soft tissue resolution and ability to assess tumor invasion depth, lymph node involvement, and the presence of extramural vascular invasion. CT imaging provides valuable information about distant metastases and helps determine the feasibility of surgical resection. ERUS aids in assessing tumor depth, perirectal lymph nodes, and sphincter involvement. Understanding the strengths and limitations of each diagnostic modality is essential for accurate staging and treatment decisions in RC. Furthermore, the integration of multiple imaging and metabolic methods, such as PET/CT or PET/MRI, can enhance diagnostic accuracy and provide valuable prognostic information. Thus, a literature review was conducted to investigate and assess the effectiveness and accuracy of diagnostic methods, both imaging and metabolic, in the stage assessment of RC.
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20726694
|
ONCOLOGY
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10.1007/s44196-024-00591-9
|
Enhancing the Performance of Vocational Education in the Digital Economy with the Application of Fuzzy Logic Algorithm
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Vocational education improves the skill and efficiency of students/learners in addition to their regular courses. Within a short period of such courses, the performance has to be improved for providing professional development. In this article, the fuzzy-based performance improvement validation method (FPIVM) is introduced. This method excels in analyzing the performance of instructor-centered vocational education improvements for varied learners. In this process, the differential performance between various training and learning sessions is identified for identifying the gap in skill improvement. The fuzzy process operates using continuous intervals for performance measures based on instructor and learner scores. This is synchronized based on the existing learner’s skill and the instructor’s efficiency in meeting the vocational course study level. In particular, the fuzzification over the independent (learner and trainer) skill score is updated for new intervals. Such skill scores are classified as high or low compared to the previous outcomes. This improves the change in instructor or mode of education for successive sessions. Thus, the quality and performance of the sessions are retained unanimously for providing better outcomes. The outcomes are revised after each session for sustaining a high learning score regardless of student density.
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18756883
|
AI
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10.3390/ejihpe14070138
|
Military Values, Military Virtues, and Vulnerable Narcissism among Cadets of the Swiss Armed Forces—Results of a Cross-Sectional Study
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Background: For military leaders, military values and virtues are important psychological prerequisites for successful leadership and for ethical and moral military behavior. However, research on predictors of military values and virtues is scarce. Given this background, we investigated whether Organizational Citizenship Behavior (OCB), resilience, and vulnerable narcissism might be favorably or unfavorably associated with military values and virtues, and whether vulnerable narcissism could moderate the association between the OCB-by-resilience-interaction, and military virtues. Methods: A total of 214 officer cadets (mean age: 20.75 years; 96.8% males) of the Swiss Armed Forces (SAF) volunteered to take part in this cross-sectional study. They completed a booklet of self-rating scales covering dimensions of military values and military virtues, OCB, resilience, and vulnerable narcissism. Results: Higher scores for military virtues were associated with higher scores for military values, OCB, and resilience, and with lower scores for vulnerable narcissism. Multiple regression models showed that higher scores for OCB and resilience were associated with military values and virtues. Vulnerable narcissism moderated the association between military virtues, and the OCB-by-resilience-interaction: the higher the vulnerable narcissism, the more the OCB-by-resilience-interaction was associated with lower scores for military virtues. Conclusions: Among cadets of the SAF, the associations between military values, military virtues, OCB, and resilience were highly intertwined, while vulnerable narcissism appeared to attenuate the association between military virtues, OCB, and resilience.
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22549625
|
PSYCHOLOGY
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10.1007/s44196-024-00599-1
|
Skin Lesion Prediction and Classification Using Innovative Modified Long Short-Term Memory-Based Hybrid Optimization Algorithm
|
Identification of pigmented skin lesions is necessary for the detection of severe diseases associated with the skin organ, notably malignancy. Accurate skin cancer diagnosis can be improved with the use of image detection approaches and computer classification skills. Therefore, this research work plans to perform skin lesion prediction and classification using a novel deep learning methodology. Initially, the data related to the skin lesion are gathered from the ISIC dataset. After collecting the images, the pre-processing is performed using hair removal and filtering hair removed images via median filtering. These pre-processed images undergo segmentation process accomplished using the U-Net method. Next, the features are extracted from these segmented images with the help of color features, and texture features by GLCM and RGB histogram features. These extracted features undergo the prediction phase that is accomplished using the MLSTM model, in which the parameter optimization is done by the nature inspired novel hybrid metaheuristic algorithm referred as SC-STBO algorithm with the consideration of accuracy maximization and RMSE minimization as the major fitness for the objective function. If the predicted output is returned as the presence of skin lesion, the same novel MLSTM model classifies the final skin lesion output into seven types, such as Vascular Lesions, Melanocytic Nevi, Melanoma, Dermatofibroma, Benign Keratosis-like Lesions, BCC, and Actinic Keratoses, respectively. Seven groups of skin diseases can be identified early thanks to the suggested effort, which can then be tested and properly handled by medical professionals. With an accuracy of 0.9931, the recommended methodology clearly outperforms traditional techniques. Similarly, the suggested methodology clearly beats the conventional methods, with a recall of 0.9825.
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18756883
|
AI
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10.1186/s40594-024-00489-0
|
Attending to STEM education in servingness at Hispanic-serving institutions: a systematic review of more than a decade of scholarship
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Background, context, and purpose of the study: Enrolling over 60% of all Latinx undergraduate students, Hispanic-serving institutions (HSIs) are poised to play a critical role in diversifying and strengthening Science, Technology, Engineering, and Mathematics (STEM) education and the STEM workforce. However, how HSIs serve STEM students is not well understood. Accordingly, guided by Garcia et al. (Review of Educational Research 89:5–745, 2019) multidimensional servingness framework, we conducted a systematic review of the research on STEM education within the HSI context. By attending to STEM education in conversations around how HSIs may serve Latinx students and their campus communities, our ultimate aim is to improve STEM education particularly at HSIs and advance STEM servingness more broadly. Results, main findings: Through our systematic review of STEM education research at HSIs, we identified (under)studied components of servingness and gaps within this literature base. Specifically, among the 128 qualifying articles, nearly two-thirds focused on student outcomes but overlooked institutions’ organizational context, raising questions about the effect(iveness) of the studied interventions. Additionally, we identified three thematic gaps in this literature: ghosting the HSI context (i.e., relying on HSIs as research sites without considering the unique HSI context); ghosting Latinx culture (i.e., decentering Latinx students and the Latinx community’s sociocultural aspects and assets), and ghosting people and places (i.e., under-examining certain student populations like Latino men in STEM and places like Hispanic-serving community colleges). Ultimately, our study extends the field’s understanding of servingness by attending to STEM education within the context of HSI institutions. Conclusions, brief summary, and potential implications: By systematically reviewing studies on STEM education at HSIs, we identified (under)studied components of servingness and patterned gaps within this literature. In doing so, we highlight opportunities to advance STEM servingness at HSIs through future research, policy, and practice. Collectively, these avenues hold the promise of improving STEM education and diversifying the STEM workforce.
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21967822
|
EDUCATION
|
10.3390/ai5030058
|
Computer Vision for Safety Management in the Steel Industry
|
The complex nature of the steel manufacturing environment, characterized by different types of hazards from materials and large machinery, makes the need for objective and automated monitoring very critical to replace the traditional methods, which are manual and subjective. This study explores the feasibility of implementing computer vision for safety management in steel manufacturing, with a case study implementation for automated hard hat detection. The research combines hazard characterization, technology assessment, and a pilot case study. First, a comprehensive review of steel manufacturing hazards was conducted, followed by the application of TOPSIS, a multi-criteria decision analysis method, to select a candidate computer vision system from eight commercially available systems. This pilot study evaluated YOLOv5m, YOLOv8m, and YOLOv9c models on 703 grayscale images from a steel mini-mill, assessing performance through precision, recall, F1-score, mAP, specificity, and AUC metrics. Results showed high overall accuracy in hard hat detection, with YOLOv9c slightly outperforming others, particularly in detecting safety violations. Challenges emerged in handling class imbalance and accurately identifying absent hard hats, especially given grayscale imagery limitations. Despite these challenges, this study affirms the feasibility of computer vision-based safety management in steel manufacturing, providing a foundation for future automated safety monitoring systems. Findings underscore the need for larger, diverse datasets and advanced techniques to address industry-specific complexities, paving the way for enhanced workplace safety in challenging industrial environments.
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26732688
|
AI
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10.3389/feduc.2024.1374641
|
Distance education challenges: insight from a nationwide teacher-centric study post- COVID-19 for informed advancements
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Scholars persistently explore the enormous effects of the COVID-19 epidemic on schooling, striving to comprehend its intricacies and derive significant perspectives for forthcoming endeavors. The research-based conclusions and suggestions are deemed potentially effective in closing the gap between theory and practice in literature. This is one of the few studies that connects problems with remedies as proposed by teachers. This national teacher-centric study uses a mixed-method methodology with a random sample of teachers from public and private schools in the State of Qatar to look extensively into the problems faced during the pandemic. In the sample, there were 45 instructors who participated in semi-structured online interviews and 1,553 teachers who answered an online questionnaire. The study points out a number of issues, such as teachers’ deficiency in pedagogical competencies, sophisticated technological proficiency in the classroom, curriculum density, inadequate teaching strategies, challenges with determining students’ needs and obtaining an honest and realistic assessment that accurately represents the students’ level of learning, and the lack of extracurricular activities. According to the findings, the challenges were influenced by a number of factors, including year of experience, gender, age, specialization, education level, and extracurricular activities. We need to leverage the lessons learned to shape the future course that distance education takes to move forward, guided by our observations and insights.
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2504284X
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EDUCATION
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10.3389/frai.2024.1330258
|
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift
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In an increasing number of industrial and technical processes, machine learning-based systems are being entrusted with supervision tasks. While they have been successfully utilized in many application areas, they frequently are not able to generalize to changes in the observed data, which environmental changes or degrading sensors might cause. These changes, commonly referred to as concept drift can trigger malfunctions in the used solutions which are safety-critical in many cases. Thus, detecting and analyzing concept drift is a crucial step when building reliable and robust machine learning-driven solutions. In this work, we consider the setting of unsupervised data streams which is highly relevant for different monitoring and anomaly detection scenarios. In particular, we focus on the tasks of localizing and explaining concept drift which are crucial to enable human operators to take appropriate action. Next to providing precise mathematical definitions of the problem of concept drift localization, we survey the body of literature on this topic. By performing standardized experiments on parametric artificial datasets we provide a direct comparison of different strategies. Thereby, we can systematically analyze the properties of different schemes and suggest first guidelines for practical applications. Finally, we explore the emerging topic of explaining concept drift.
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26248212
|
AI
|
10.1186/s40359-024-01887-3
|
Understanding the public stigma of mental illness: a mixed-methods, multi-level, exploratory triangulation study
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Background: This study examines the role of themata in understanding mental health-related stigma. It is motivated by the need for alternative theoretical-methodological approaches beyond the dominant frameworks in education and contact-based anti-stigma public health efforts, which have shown mixed effects. Specifically, it addresses the need for a more nuanced framework in stigma research, one that is sensitive to the dialogues through which people relate themselves to mental health and stigma in context. Methods: The research employs an exploratory mixed-methods approach, including the analysis of 529 news reports, 20 focus group discussions, and 19 one-to-one interviews, all concerning representations of shared living arrangements with someone perceived to have experiences of mental illness. Thematic analysis and natural language processing are used within a convergent triangulation design to analyze the data. Results: We found that mental health and illness were communicated through an overarching Self/Other thema and five subordinate themata: normal/abnormal, harm/non-harm, bounded/non-bounded, and moral/immoral. Despite familiarity with psychological distress and ‘modern’ explanations of mental illness, concerns about social identity motivated representations of mental illness as a predominantly permanent, negative form of personhood marked by abnormality, harm, distance, and immorality. Additionally, concerns about personal vulnerability, including historically rooted fears of contagion, motivated distancing representations of mental illness, rather than neutral portrayals. Conclusions: Themata have under-developed theoretical and methodological potential for addressing mental health-related stigma, particularly in their ability to describe the dynamic ways in which culture motivates people to both resist and reproduce stigma, partly through ambivalences, absences, tensions, and ambiguities in representation. A critical discussion is provided on how themata may support ecological strategies in mental health campaigns over generic models, emphasizing the need to understand group knowledge and contact dynamics to mitigate adverse effects. Themata Public Health Unintended Consequences Mixed Methods Behaviour Change Natural Language Processing.
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20507283
|
PSYCHOLOGY
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10.3390/educsci14070797
|
Enhancing Technology-Focused Entrepreneurship in Higher Education Institutions Ecosystem: Implementing Innovation Models in International Projects
|
Innovation models are key to fostering technology-focused entrepreneurship in higher education institutions (HEIs). These models create dynamic environments that encourage collaboration, creativity, and problem-solving skills among students and faculty. HEIs face several challenges in fostering entrepreneurship, including allocating sufficient financial and human resources, integrating entrepreneurship education across disciplines, and managing intellectual property. Overcoming these challenges requires HEIs to cultivate an entrepreneurial culture and establish strong partnerships with industry stakeholders. To achieve these goals, HEIs must adopt successful innovation models proven to work. This article presents an international case study highlighting such models and the factors contributing to their success. This study explores the implementation and impact of innovation models, specifically IDEATION and DEETECHTIVE, within HEIs to foster technology-focused entrepreneurship. By implementing numerous actions focusing on online education integration and the Quintuple Helix Innovation Model, these models support shifting engineering students’ mindsets toward entrepreneurship. This research highlights the importance of academia–industry collaboration, international partnerships, and the integration of entrepreneurship education in technology-focused disciplines. This study presents two models. The first, IDEATION, focuses on open innovation and sharing economy aspects. This model underwent rigorous testing and refinement, evolving into the second model, DEETECHTIVE, which is more comprehensive and deep tech-focused. These models have been validated as effective frameworks for fostering entrepreneurship and innovation within HEIs. This study’s findings underscore the potential of these models to enhance innovation capacity, foster an entrepreneurial culture, and create ecosystems rich in creativity and advancement. Practical implications include the establishment of open innovation-oriented structures and mechanisms, the development of specialized curriculum components, and the creation of enhanced collaboration platforms.
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22277102
|
EDUCATION
|
10.3390/ejihpe14070140
|
Well-Being and Dispositional Hope in a Sample of Portuguese Citizens: The Mediating Role of Mental Health
|
In our pursuit of a fulfilling and contented life, the study of well-being has emerged as a fundamental field of research. Higher levels of well-being are associated with better mental health outcomes. Individuals with better mental health might possess the personal resources necessary to set and pursue meaningful goals, maintain positive expectations, and overcome adversities. We aim to explore the positive relationship between well-being (hedonic, psychological, and social) and dispositional hope. We suggest that mental health acts as a mediator in this relationship, since improved mental health can create a conducive environment for the development and maintenance of dispositional hope. Data were collected using an e-survey through social media during the last quarter of 2022. The hypothesis of this study was tested using mediation analysis. The sample was composed of 471 participants (85.4% female) with a mean age of 47.72 ± 11.86 years. Participants were mainly workers (88.6%), followed by pensioners (6.8%), university students (2.5%), and unemployed (2.1%). Results revealed that well-being was positively and significantly associated with dispositional hope. Additionally, well-being presented a significant and positive relationship with mental health, which, in turn, also presented a significant and positive relationship with dispositional hope. Finally, using the Hayes process macro for SPSS, we found that mental health mediates the relationship between well-being and dispositional hope. Our findings reinforce the conceptual frameworks that consider well-being and mental health as key contributors to a resilient and optimistic mindset. Interventions that aim to cultivate positive affect, facilitate personal growth, and foster supportive social environments might help improve mental health outcomes.
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22549625
|
PSYCHOLOGY
|
10.3390/ai5030059
|
Dynamic Programming-Based White Box Adversarial Attack for Deep Neural Networks
|
Recent studies have exposed the vulnerabilities of deep neural networks to some carefully perturbed input data. We propose a novel untargeted white box adversarial attack, the dynamic programming-based sub-pixel score method (SPSM) attack (DPSPSM), which is a variation of the traditional gradient-based white box adversarial approach that is limited by a fixed hamming distance using a dynamic programming-based structure. It is stimulated using a pixel score metric technique, the SPSM, which is introduced in this paper. In contrast to the conventional gradient-based adversarial attacks, which alter entire images almost imperceptibly, the DPSPSM is swift and offers the robustness of manipulating only a small number of input pixels. The presented algorithm quantizes the gradient update with a score generated for each pixel, incorporating contributions from each channel. The results show that the DPSPSM deceives the model with a success rate of 30.45% in the CIFAR-10 test set and 29.30% in the CIFAR-100 test set.
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26732688
|
AI
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10.1007/s44196-024-00601-w
|
A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method
|
This paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named $${K}^{2}$$
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18756883
|
AI
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10.3389/feduc.2024.1376805
|
Unveiling mode effects in grade 1 vocabulary assessment: the intriguing influence of test mode
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Background: Vocabulary knowledge plays a pivotal role in academic development, particularly among Grade 1 students. To support students in their academic development, effective assessment instruments in educational settings are crucial. The GraWo (Graz Vocabulary Test) is introduced as a tool designed to evaluate receptive vocabulary in German-speaking countries in print and in digital mode.Objectives: This study aims to investigate mode effects in the GraWo among Grade 1 students, comparing vocabulary gains in digital and print versions. Additionally, it explores the influence of student characteristics, such as gender and language status, and examines item-level differences between the two modes in order to gain a more comprehensive understanding of test performance.Design: The research design entails a longitudinal approach, following children (n = 421) from the beginning to the end of Grade 1, varying the test modes (digital or print) only at second measurement (40% receiving the print version), while at first measurement all children worked with the digital version.Results: Baseline comparisons of test mode groups indicated almost no significant differences. In terms of growth in vocabulary during Grade 1, an ANOVA with repeated measures revealed a main effect for time, indicating increased performance in both groups at second measurement. Moreover, an interaction effect between time and test mode group showed that the print group exhibited higher gains in the vocabulary test compared to the digital group. Further analysis using MNLFA confirmed that the print mode group outperformed the digital group overall and that four items were also individually affected by differences between the digital and print versions.Conclusion: The study emphasizes the need for nuanced investigations into the impact of test mode on student performance and suggests incorporating observational methods to comprehensively understand student interactions with digital and print modes. In acknowledging potential variations in performance, educators and policymakers need to tailor practices to accommodate the demands of hybrid test procedures and to consider the role of digital competence in shaping testing experiences.
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2504284X
|
EDUCATION
|
10.3389/fpsyg.2024.1356999
|
Exercise motivation, physical exercise, and mental health among college students: examining the predictive power of five different types of exercise motivation
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Introduction: The mental health (MH) of college students has long been a crucial concern for families, educational institutions, and society. Extensive research has demonstrated the influential role of exercise motivation in shaping MH. However, further investigation is warranted to ascertain which types of exercise motivation may have more influence on the MH of college students. The present study examined the direct effects of five distinct types of exercise motivation, namely health motivation (HM), appearance motivation (APM), fun motivation (FM), ability motivation (ABM), and social motivation (SM) on MH. Additionally, the study explored the potential mediating role of physical exercise (PE) in these relationships.Methods: An cross-sectional study design was employed. A total of 433 Chinese college students participated in the study and completed our questionnaires, which included the Exercise motivation scale (EM scale), the Physical exercise scale (PE scale), and the Mental health scale (MH scale).Results: The findings revealed a significant and positive relationship between all five categories of exercise motivation and the MH of college students. Specifically, FM was found to have the most pronounced impact on MH, followed by HM, ABM, SM, and APM, in descending order of influence. Furthermore, the impacts of HM, FM, ABM, and SM on MH were found to be partially mediated by PE. However, the association between APM and MH was entirely mediated by PE.Discussion: The present study contributes to enhancing the comprehension of the underlying mechanisms behind different exercise motivations in relation to PE and MH. Additionally, it offers practical implications for developing intervention strategies for improving the MH of college students.
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16641078
|
PSYCHOLOGY
|
10.3390/ai5030061
|
Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey
|
The development of self-driving or autonomous vehicles has led to significant advancements in 3D object detection technologies, which are critical for the safety and efficiency of autonomous driving. Despite recent advances, several challenges remain in sensor integration, handling sparse and noisy data, and ensuring reliable performance across diverse environmental conditions. This paper comprehensively surveys state-of-the-art 3D object detection techniques for autonomous vehicles, emphasizing the importance of multi-sensor fusion techniques and advanced deep learning models. Furthermore, we present key areas for future research, including enhancing sensor fusion algorithms, improving computational efficiency, and addressing ethical, security, and privacy concerns. The integration of these technologies into real-world applications for autonomous driving is presented by highlighting potential benefits and limitations. We also present a side-by-side comparison of different techniques in a tabular form. Through a comprehensive review, this paper aims to provide insights into the future directions of 3D object detection and its impact on the evolution of autonomous driving.
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26732688
|
AI
|
10.3389/fpsyg.2024.1382614
|
Evaluating the before operational stress program: comparing in-person and virtual delivery
|
Introduction: Public safety personnel (PSP) are at increased risk for posttraumatic stress injuries (PTSI). Before Operational Stress (BOS) is a mental health program for PSP with preliminary support mitigating PTSI. The current study compared the effectiveness of delivering BOS in-person by a registered clinician (i.e., Intensive) to virtually delivery by a trained clinician (i.e., Classroom).Methods: Canadian PSP completed the Intensive (n = 118; 61.9% male) or Classroom (n = 149; 50.3% male) program, with self-report surveys at pre-, post-, 1 month, and 4 months follow-ups.Results: Multilevel modelling evidenced comparable reductions in anxiety (p < 0.05, ES = 0.21) and emotional regulation difficulties (ps < 0.05, ESs = 0.20, 0.25) over time with no significant difference between modalities. Participants discussed benefits of the delivery modality they received.Discussion: The results support virtual delivery of the BOS program (Classroom) as an accessible mental health training option for PSP, producing effects comparable to in-person delivery by clinicians.
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16641078
|
PSYCHOLOGY
|
10.3390/educsci14080818
|
The Design and Impact of Interactive Online Modules for Dental Faculty Calibration
|
The diverse backgrounds of health professions faculty often result in inconsistent teaching, clinical techniques, and feedback for students. Faculty calibration is essential for uniform, high-quality instruction. However, scheduling training sessions is challenging due to faculty availability. This study introduces a self-paced, interactive online approach to dental faculty calibration. Four self-paced online modules were developed using an interactive cloud-based platform. A variety of learning activities were interspersed throughout the module to promote active learning. A survey captured faculty’s perception of the online format. ANOVA analyses examined differences in perceived effectiveness of the online format between full-time, part-time, and adjunct faculty. The platform analytics offered insights into the faculty’s module performance. Anecdotal feedback from faculty provided evidence of behavioral changes. 94% of the faculty expressed high satisfaction with the online format. The majority of faculty agreed or strongly agreed that the online format was effective (89%), engaging (88%), and easy to navigate (84%). They highlighted the modules’ user-friendliness, flexibility, and engaging content. ANOVA analyses revealed no significant differences in perceived effectiveness of the online format between full-time, part-time, and adjunct faculty. Anecdotal feedback demonstrated that faculty were incorporating the knowledge gained from the modules into their teaching practices. This positive online experience also motivated several faculty to integrate similar online approaches into their own courses. This online approach provides a more flexible, sustainable, and interactive approach to faculty development that could be beneficial to other institutions.
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22277102
|
EDUCATION
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10.3389/frai.2024.1408843
|
Multimodal data integration for oncology in the era of deep neural networks: a review
|
Cancer research encompasses data across various scales, modalities, and resolutions, from screening and diagnostic imaging to digitized histopathology slides to various types of molecular data and clinical records. The integration of these diverse data types for personalized cancer care and predictive modeling holds the promise of enhancing the accuracy and reliability of cancer screening, diagnosis, and treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short of capturing the complex and heterogeneous nature of cancer data. The advent of deep neural networks has spurred the development of sophisticated multimodal data fusion techniques capable of extracting and synthesizing information from disparate sources. Among these, Graph Neural Networks (GNNs) and Transformers have emerged as powerful tools for multimodal learning, demonstrating significant success. This review presents the foundational principles of multimodal learning including oncology data modalities, taxonomy of multimodal learning, and fusion strategies. We delve into the recent advancements in GNNs and Transformers for the fusion of multimodal data in oncology, spotlighting key studies and their pivotal findings. We discuss the unique challenges of multimodal learning, such as data heterogeneity and integration complexities, alongside the opportunities it presents for a more nuanced and comprehensive understanding of cancer. Finally, we present some of the latest comprehensive multimodal pan-cancer data sources. By surveying the landscape of multimodal data integration in oncology, our goal is to underline the transformative potential of multimodal GNNs and Transformers. Through technological advancements and the methodological innovations presented in this review, we aim to chart a course for future research in this promising field. This review may be the first that highlights the current state of multimodal modeling applications in cancer using GNNs and transformers, presents comprehensive multimodal oncology data sources, and sets the stage for multimodal evolution, encouraging further exploration and development in personalized cancer care.
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26248212
|
AI
|
10.3389/fonc.2024.1407003
|
Tumor-informed ctDNA assessment as a valuable prognostic and predictive biomarker in diffuse large B-cell lymphoma
|
Background: A novel approach for molecular residual disease (MRD) detection and treatment monitoring is needed in diffuse large B-cell lymphoma (DLBCL) to identify patients with a poor prognosis. We performed a retrospective evaluation of commercial ctDNA testing in patients with stage I-IV DLBCL to evaluate the prognostic and predictive role of tumor-informed ctDNA assessment.Methods: A personalized and tumor-informed multiplex PCR assay (Signatera™ bespoke mPCR NGS assay) was used for ctDNA detection and quantification.Results: In total, 50 patients (median age: 59 years; median follow-up: 12.68 months) were analyzed, of which 41 had pretreatment time points with ctDNA detected in 95% (39/41). Baseline ctDNA levels correlated with R-IPI scores and stage. ctDNA clearance during first-line therapy was predictive of improved therapy responses and outcomes (EFS, HR: 6.5, 95% CI: 1.9-22, p=0.003 and OS, HR: 22, 95% CI: 2.5-191, p=0.005). Furthermore, 48% (13/27) of patients cleared their ctDNA following the first cycle of treatment. Patients who cleared their ctDNA, irrespective of their R-IPI score, had superior outcomes compared to ctDNA-positive patients. ctDNA clearance outperformed other factors associated with EFS in multivariate analysis (HR: 49.76, 95% CI:1.1-2225.6, p=0.044). Finally, ctDNA clearance predicted complete response (CR)/no evidence of disease (NED) on average 97 days (range: 0-14.7 months) ahead of imaging/biopsy.Conclusion: ctDNA testing in patients with DLBCL is predictive of patient outcomes and may enable personalized surveillance, intervention, and/or trial options.
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2234943X
|
ONCOLOGY
|
10.1186/s40594-024-00494-3
|
Scaffolded team-based computational modeling and simulation projects for promoting representational competence and regulatory skills
|
Background: This study posits that scaffolded team-based computational modeling and simulation projects can support model-based learning that can result in evidence of representational competence and regulatory skills. The study involved 116 students from a second-year thermodynamics undergraduate course organized into 24 teams, who worked on three two-week-long team-based computational modeling and simulation projects and reflected upon their experience.Results: Results characterized different levels of engagement with computational model-based learning in the form of problem formulation and model planning, implementation and use of the computational model, evaluation, and interpretation of the outputs of the model, as well as reflection on the process. Results report on students’ levels of representational competence as related to the computational model, meaning-making of the underlying code of the computational model, graphical representations generated by the model, and explanations and interpretations of the output representations. Results also described regulatory skills as challenges and strategies related to programming skills, challenges and strategies related to meaning-making skills for understanding and connecting the science to the code and the results, and challenges and strategies related to process management mainly focused on project management skills.Conclusion: Characterizing dimensions of computational model-based reasoning provides insights that showcase students’ learning, benefits, and challenges when engaging in team-based computational modeling and simulation projects. This study also contributes to evidence-based scaffolding strategies that can support undergraduate students' engagement in the context of computational modeling and simulation.
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21967822
|
EDUCATION
|
10.3390/educsci14080834
|
Foresight Methodologies in Responsible GenAI Education: Insights from the Intermedia-Lab at Complutense University Madrid
|
This study, conducted at Complutense Intermedia-Lab, employs a dual approach to explore university students’ use of Generative AI (GenAI), combining a survey with foresight methodologies (Sci-fi prototyping). The initial survey gathers baseline data on students’ experiences, attitudes, and concerns regarding GenAI, providing a comprehensive understanding of current practices among university students in Spain. This empirical foundation informs subsequent Sci-fi prototyping sessions, where students creatively envision future scenarios, fostering futurist thinking and deeper engagement. By integrating principles of Responsible Research and Innovation (RRI), this approach facilitates a nuanced exploration of GenAI’s potential impacts on education. The incorporation of both quantitative data collection and qualitative foresight methods in this study serves to navigate challenges and level opportunities of promoting the ethical and inclusive incorporation of GenAI in Higher Education, ensuring that future innovations align with societal values and needs.
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22277102
|
EDUCATION
|
10.1007/s44196-024-00606-5
|
A Dynamic Scheduling Method for Logistics Supply Chain Based on Adaptive Ant Colony Algorithm
|
To reduce the dynamic scheduling cost of logistics supply chain and improve customer satisfaction, this paper proposes a dynamic scheduling method for logistics supply chain based on adaptive ant colony algorithm. First, determine the goal of dynamic scheduling in the logistics supply chain. Second, considering supplier satisfaction, transportation costs, and maximum delivery distance constraints, a dynamic scheduling model for logistics supply chain is constructed. Then by smoothing the pheromones and designing a transition function, adjusting factors are introduced to update the pheromone rules. Finally, based on the adaptive ant colony algorithm, the solution of the dynamic scheduling function of the logistics supply chain is solved to achieve the dynamic scheduling of the current logistics supply chain. The experimental results show that after 19 iterations, the method can search for the optimal route A1 group with a length of 33.85 km, with fewer iterations and shorter paths. The total cost is 114,290 yuan, and the degree of cargo loss is low, with a maximum of only 0.14%. The task completion time is short, customer satisfaction is above 0.85, and the scheduling accuracy is 99.9%. It can effectively control costs, improve customer satisfaction, and accurately arrange logistics supply chains.
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18756883
|
AI
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10.3390/ejihpe14080147
|
Social Media Use and Consumption of Prescription-Free Medications for Anxiety, Sleep, and Pain among Norwegian University Students
|
A relationship has been recognized between social media use and health issues. However, no studies have explored the potential link between social media use and consumption of over-the-counter (OTC) medications. We examined social media use, self-reported anxiety, depression, sleep problems, pain, and OTC medications use among Norwegian university students. The goal was to gain insights that would guide preventive health strategies for this target group. A quantitative, cross-sectional study was conducted with an online questionnaire distributed to university student Facebook groups in Norway. A total of 132 completed surveys were analyzed. Among the respondents, 28% experienced anxiety, 35% depression, 64% sleep problems, 71% headaches, and 78% musculoskeletal pain. Moreover, 56% reported using OTC analgesics or sleep aids, mostly purchased from community pharmacies. No statistically significant correlation was found between social media use and headache, musculoskeletal pain, sleep disturbances, or consumption of OTC medications among university students in Norway. The findings, however, demonstrated a positive trend, highlighting the need for further research with larger, more diverse samples, and potentially employing a qualitative or longitudinal design. We propose increased awareness of the potential negative effects of social media among university students, the inclusion of social media and health topics in study curricula, and the more proactive engagement of community pharmacists with young clients concerning the consumption of OTC medications.
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22549625
|
PSYCHOLOGY
|
10.1186/s40359-024-01912-5
|
Correction: Chinese version of the tendency to avoid physical activity and Sport (TAPAS) scale: testing unidimensionality, measurement invariance, concurrent validity, and known-group validity among Taiwanese youths
| null |
20507283
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PSYCHOLOGY
|
10.3390/ejihpe14080148
|
Mother–Child Attachment Relationship in Pregnancy, Postpartum, and Early Childhood: Current Status and New Research Perspectives
|
The mother–child attachment relationship is a cornerstone of human development, with profound implications for the well-being of both the mother and child
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22549625
|
PSYCHOLOGY
|
10.3389/feduc.2024.1414081
|
Computational thinking in primary mathematics classroom activities
|
The integration of computational thinking (CT) into primary education is often facilitated using one or more CT tools, such as block-based programming environments and educational robotics. A major concern is that these CT tools often are used to design mathematics classroom activities that focus on CT at the expense of mathematics. Hence, there is a need to investigate more closely how CT tools can be used in primary mathematics classroom activities in ways that enable a stronger focus on the learning of mathematics. Using information ecology as a theoretical lens, this study aims to understand how and why CT tools are integrated into primary mathematics classrooms, and how teachers value the possible contributions of such tools. We draw on multiple interviews with two primary teachers, recordings of planning sessions where classroom activities that include CT were designed, the classroom implementations themselves, and reflective conversations with the teachers after the CT tools were integrated in their mathematics classrooms. A deductive analytical approach to our data revealed that (1) CT tools, to varying degrees, facilitate the learning of mathematics; (2) some CT tools were valued by teachers as a better ‘fit’ than others; and (3) CT tools are primarily used to support the learning of geometry, excluding other mathematical domains. Based on these findings, we suggest that there is a need for more research on the use of different CT tools and their role in the learning of primary mathematics. Moreover, more research is needed to understand how CT tools can be used in topics other than geometry.
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2504284X
|
EDUCATION
|
10.3389/fpsyg.2024.1445549
|
Exploring social media determinants in fostering pro-environmental behavior: insights from social impact theory and the theory of planned behavior
|
Introduction: This study investigates the impact of social media on pro-environmental behavior (PEB) through the lenses of the Theory of Planned Behavior (TPB) and Social Impact Theory. The research aims to elucidate how social media influences Environmental Attitude (EA) and Subjective Norms (SN), and how these factors contribute to Behavioral Intentions (BI) that ultimately affect PEB. Additionally, it examines the moderating effect of Perceived Behavioral Control (PBC) on the relationship between BI and PEB.Methods: To explore these relationships, the study employs a dual methodological approach using Variance-Based Structural Equation Modeling (VBSEM) and Artificial Neural Networks (ANN). Data were collected from two distinct samples: 1200 participants from Taiwan for the SEM analysis and 602 respondents for the ANN study. SEM was utilized to explore causal relationships, while ANN was employed to enhance predictive accuracy.Results: The SEM analysis reveals that social media significantly affects both EA and SN, except for Social Networking Site Involvement (SNSI), which does not significantly impact EA. Additionally, the findings indicate that BI mediates the relationship between EA and PEB. However, BI does not mediate the SN-PEB relationship, and the link between SN and BI is found to be non-significant. Empirical evidence also suggests that PBC moderates the BI-PEB relationship, with a stronger influence observed under higher levels of PBC and a weaker influence under lower levels.Discussion: These results underscore the complex dynamics between social media factors and pro-environmental behavior. The study concludes that while social media plays a significant role in shaping EA and SN, its impact on EA is not mediated by SNSI. Furthermore, PBC significantly moderates the BI-PEB relationship, highlighting its critical role in PEB. The discussion addresses the implications of these findings, acknowledges the limitations encountered, and suggests potential avenues for future research.
|
16641078
|
PSYCHOLOGY
|
10.1007/s44196-024-00604-7
|
Multi-objective Approach for Dynamic Economic Emission Dispatch Problem Considering Power System Reliability and Transmission Loss Prediction Using Cascaded Forward Neural Network
|
This study addresses the significant problem of Dynamic Economic Emission Dispatch (DEED), a critical consideration in power systems from both economic and environmental protection viewpoints. Reliability stands as another vital facet, impacting maintenance and operation perspectives. The integration of Artificial Neural Network (ANN)-based transmission loss prediction into the DEED model is also essential to address specific limitations and enhance the overall performance of the dispatch process. Traditionally, the DEED model relies on a single B-loss coefficient to estimate transmission losses. While this approach simplifies calculations, it fails to account for the significant variations in demand that occur throughout the dispatch period and it leads to inaccuracies in loss prediction, especially in dynamic environments. Using a single coefficient, the model cannot adequately capture the complex, non-linear relationships between power generation, load, and transmission losses under different operating conditions. To overcome this limitation, this study introduces an ANN-based loss prediction method integrated into the DEED model and uses trained ANN to replace the process of finding B-loss coefficients during each dispatch period. This paper also introduces a strategy leveraging the multi-objective northern goshawk optimizer algorithm, characterized by a non-dominated sorting and crowding distance mechanism, to enhance DEED considerations incorporating reliability (DEEDR). This novel algorithm improves the solution space effectively, maintains high population diversity and enables an even distribution of individuals sharing the same rank in the objective space. The fundamental objective of this study is to balance fuel cost, emission, and system reliability in power system operations. Compared with a few existing multi-objective optimization algorithms, this study demonstrates superior performance in generating a series of non-dominated solutions. The experimental results highlight its competitive and potential as an efficient tool in the DEED and DEEDR problems, promising a synergistic coordination of economy, environmental protection, and system reliability benefits in power system management.
|
18756883
|
AI
|
10.1007/s00432-024-05907-y
|
Impact of response to neoadjuvant chemotherapy on surgical modality in patients with T1-2N0-1M0 triple-negative breast cancer
|
Purpose: Many T1-2N0-1M0 triple-negative breast cancer (TNBC) patients who undergo neoadjuvant chemotherapy (NAC) do not receive breast-conserving therapy (BCT) due to concerns about non-pCR or lymph node metastasis presence. Methods: T1-2N0-1M0 TNBC patients who underwent NAC between 2010 and 2017 were collected from the SEER database. Factors affecting surgical modalities were analyzed by multinomial logistic regression. The overall survival (OS) and breast cancer-specific survival (BCSS) were evaluated by Kaplan-Meier curves and Cox proportional hazards models. Further stratified subgroup analyses were performed based on the response to NAC and N-stage. Adjusted-hazard ratios were also calculated to exclude potential bias. Results: A total of 1112 patients were enrolled (median follow-up: 81 months), 58.5% received BCT, 23.6% received reconstruction and 17.9% received mastectomy. Response to NAC and N-stage not only influenced the choice of surgical modality but also were independent predictors for OS and BCSS. The surgery-induced survival differences mainly affect OS. Survival analyses demonstrated that the 10-year OS of BCT was superior or equal to that of mastectomy even in patients with partial response (PR) (77.4% vs. 64.1%, P = 0.013), no response (NR) (44.9% vs. 64.2%, P = 0.33), or N1 stage (75.7% vs. 57.4%, P = 0.0021). In the N1-PR cohort, mastectomy may lead to worse OS (P = 0.0012). Besides, between reconstruction and BCT, there was no statistical difference in OS or BCSS (P > 0.05). Conclusion: Our study reveals the necessity of breast surgical de-escalation. Besides, physicians should actively recommend reconstruction for individuals who strongly desire mastectomy.
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14321335
|
ONCOLOGY
|
10.3390/ejihpe14080149
|
Teachers’ Heart Rate Variability and Behavioral Reactions in Aggressive Interactions: Teachers Can Downregulate Their Physiological Arousal, and Progesterone Favors Social Integrative Teacher Responses
|
Aggressive student behavior is considered one of the main risk factors for teacher stress. The present study investigated teachers’ physiological and behavioral reactions when facing aggressive student behavior and examined which resources favor adaptive teacher reactions. The sample included 42 teachers. We assessed (a) teacher self-reports (i.e., resources, risk factors, and vital exhaustion) (b) classroom observations, (c) ambulatory assessments of teachers’ heart rate and heart rate variability, and (d) teachers’ progesterone concentrations in the hair. The present study focused on a subsample of ten teachers (9 females, Mage = 34.70, SD = 11.32) managing classes which were potentially very stressful as they had a high density of aggressive behavior. High levels of work satisfaction, hair progesterone, and a low level of work overload fostered social integrative teacher responses. Moreover, in 75% of the cases, teachers succeeded in downregulating their physiological reaction. Our results support the notion that teachers evaluate stressors in light of their resources. When they perceive their resources as insufficient for coping with a challenging situation, stress arises, and subsequently, they react inefficiently to aggressive behavior. Thus, teacher education could benefit from strengthening teacher resources and strategies for coping with aggressive student behavior.
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22549625
|
PSYCHOLOGY
|
10.1186/s40594-024-00493-4
|
Unpacking the role of AI ethics online education for science and engineering students
|
Background: As artificial intelligence (AI) technology rapidly advances, it becomes imperative to equip students with tools to navigate through the many intricate ethical considerations surrounding its development and use. Despite growing recognition of this necessity, the integration of AI ethics into higher education curricula remains limited. This paucity highlights an urgent need for comprehensive ethics education initiatives in AI, particularly for science and engineering students who are at the forefront of these innovations. Hence, this research investigates the role of an online explicit-reflective learning module in fostering science and engineering graduate students' ethical knowledge, awareness, and problem-solving skills. The study’s participants included 90 graduate students specializing in diverse science and engineering research tracks. Employing the embedded mixed-methods approach, data were collected from pre- and post-intervention questionnaires with closed-ended and open-ended questions. Results: The study's results indicate that the online explicit-reflective learning module significantly enhanced students' knowledge of AI ethics. Initially, students exhibited a medium–high level of perceived ethical awareness, which saw a modest but statistically significant enhancement following the participation. Notably, a more distinct increase was observed in students' actual awareness of ethical issues in AI, before and after the intervention. Content analysis of students’ responses to the open-ended questions revealed an increase in their ability to identify and articulate concerns relating to privacy breaches, the utilization of flawed datasets, and issues of biased social representation. Moreover, while students initially displayed limited problem-solving abilities in AI ethics, a considerable enhancement in these competencies was evident post-intervention. Conclusions: The study results highlight the important role of explicit-reflective learning in preparing future professionals in science and engineering with the skills necessary for ethical decision-making. The study highlights the need for placing more emphasis not only on students’ ability to identify AI-related ethical issues but also on their capacity to resolve and perhaps mitigate the impact of such ethical dilemmas.
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21967822
|
EDUCATION
|
10.3390/ai5030065
|
Teaming Up with an AI: Exploring Human–AI Collaboration in a Writing Scenario with ChatGPT
|
Recent advancements in artificial intelligence (AI) technologies, particularly in generative pre-trained transformer large language models, have significantly enhanced the capabilities of text-generative AI tools—a development that opens new avenues for human–AI collaboration across various domains. However, the dynamics of human interaction with AI-based chatbots, such as ChatGPT, remain largely unexplored. We observed and analyzed how people interact with ChatGPT in a collaborative writing setting to address this research gap. A total of 135 participants took part in this exploratory lab study, which consisted of engaging with ChatGPT to compose a text discussing the prohibition of alcohol in public in relation to a given statement on risky alcohol consumption. During the writing task, all screen activity was logged. In addition to the writing task, further insights on user behavior and experience were gained by applying questionnaires and conducting an additional short interview with a randomly selected subset of 18 participants. Our results reveal high satisfaction with ChatGPT regarding quality aspects, mainly cognitive rather than affect-based trust in ChatGPT’s responses, and higher ratings on perceived competence than on warmth. Compared to other types of prompts, mostly content-related prompts for data, facts, and information were sent to ChatGPT. Mixed-method analysis showed that affinity for technology integration and current use of ChatGPT were positively associated with the frequency of complete text requests. Moreover, prompts for complete texts were associated with more copy–paste behavior. These first insights into co-writing with ChatGPT can inform future research on how successful human–AI collaborative writing can be designed.
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26732688
|
AI
|
10.1186/s40594-024-00495-2
|
Enhancing programming learning performance through a Jigsaw collaborative learning method in a metaverse virtual space
|
Computational thinking (CT) is crucial to fostering critical thinking and problem-solving skills. Many elementary schools have been cultivating students’ CT through block-based programming languages such as Scratch using traditional teacher-centered teaching methods. However, the approach excessively relies on teacher lectures, so the teacher’s teaching load is hefty, and most students cannot receive timely assistance from teachers, thus reducing students’ programming learning performance, interest, and confidence. Therefore, this study designs a Jigsaw collaborative learning method implemented in a metaverse virtual space (JCLM-MVS) for peer-to-peer Scratch programming learning to promote learners’ programming learning performance, CT, and learning attitudes. This study used a quasi-experimental research method, with 48 fifth-grade students from two classes in Taiwan’s elementary school as the research participants. One class of 24 students was randomly assigned to the experimental group using JCLM-MVS to conduct Scratch programming learning, whereas the other class of 24 students was assigned to the control group using the traditional teacher-centered teaching method. The study found that the experimental group of learners showed significantly better Scratch programming learning performance and attitude than the control group, and there was no statistically significant difference in CT between both groups, but both learning approaches effectively promoted CT. Additionally, the interview results showed that most interviewees stated that using JCLM-MVS for Scratch programming learning could receive timely assistance from peers, make collaborative learning more efficient and learning more enjoyable, and more intend to use JCLM-MVS for Scratch programming learning than using traditional teacher-centered teaching method due to the effective collaborative interaction mechanisms and realistic learning space provided in the metaverse virtual space. This study presents a novel and engaging learning approach by integrating a metaverse virtual space with the Jigsaw collaborative learning method, referred to as JCLM-MVS, which enhances the effectiveness of the Jigsaw collaborative learning method in promoting Scratch programming learning performance, CT, and attitudes.
|
21967822
|
EDUCATION
|
10.1007/s44196-024-00611-8
|
Online Portfolio Selection of Fuzzy Mean Regression Strategy Considering Investor Sentiment Based on Text Data
|
Investors are often affected by emotion, cognition, and other psychological factors in stock trading when making decisions. At present, people can use machine learning and other technologies to obtain a massive amount of text data from the Internet to mine information related to investor behavior and sentiment. Building intelligent online portfolio trading strategies that consider investor sentiment has become an important topic and key challenge in the financial field. Therefore, this paper explores how to use text data to depict investor sentiment, fuzzifies historical stock price data, designs a new weight transfer equation, and finally obtains a novel fuzzy mean regression strategy that considers investor sentiment based on text data. We conduct empirical tests on this strategy by using the stock price data selected from CSI300 constituent stocks, as well as the text data of investors’ opinions on the internet. The results show that the strategy proposed in this study has a higher Calmar ratio than other mean regression strategies previously studied.
|
18756883
|
AI
|
10.1007/s44196-024-00602-9
|
A Fairness Group Recommendation Algorithm Based On User Activity
|
In the process of group recommendation, due to the different preferences of group members, the recommendation results cannot meet the needs of all users. How to maximize the fairness of group recommendation is still a challenge. Therefore, this paper proposes a group recommendation algorithm based on user activity. Firstly, a group discovery algorithm based on item cluster preference was used to mine potential groups. Secondly, considering the dynamic change of activity, a sliding time window is designed to investigate the recent activity of each member in the group at the time of subgroup division, and the group is divided into active subgroup and inactive subgroup. Finally, the group recommendation list was generated by aggregating the subgroup preferences by average consensus. Experimental results on the public dataset show that compared with the AGREE algorithm, the recommendation accuracy and coverage of the proposed algorithm are improved by 2.1% and 2.9%, respectively. By focusing on the preference needs of inactive users, the proposed algorithm effectively improves the recommendation satisfaction and group fairness.
|
18756883
|
AI
|
10.1007/s44196-024-00618-1
|
EFection: Effectiveness Detection Technique for Clustering Cloud Workload Traces
|
Clustering is widely used in cloud computing studies to extract vital information. These studies have ignored investigating the potential improvements in clustering quality from better selection of its dimensions and methods. Consequently, developing an automated technique to perform such a selection was not addressed thoroughly. Most of the recent attempts either relied on feature reduction or general non-automated techniques, which were deemed unreliable for sufficient selection. Therefore, we first conducted a comprehensive investigation to study the impact of selecting better clustering dimensions and methods. Our results indicate achieving significant improvement by 15–70% points through better selection. Then, we developed a novel technique (EFection) to detect the best selection in advance using a combination of internal validation metrics (Davies–Bouldin) and the Pearson correlation coefficient. We evaluate our technique’s accuracy by comparing the clustering quality of its suggestions with that of the optimal selection. We then compare EFection’s performance with recent attempts to measure its superiority. Finally, we validate its applicability when adopted in cloud clustering-based studies. The results show that EFection offers high accuracy, around 83%, and surpasses prior art by 11%.
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18756883
|
AI
|
10.3389/frai.2024.1433494
|
Enhanced fingerprint classification through modified PCA with SVD and invariant moments
|
This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.
|
26248212
|
AI
|
10.1007/s00432-024-05903-2
|
Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review
|
Triple negative breast cancer (TNBC) is most aggressive type of breast cancer with multiple invasive sub-types and leading cause of women’s death worldwide. Lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) causes it to spread rapidly making its treatment challenging due to unresponsiveness towards anti-HER and endocrine therapy. Hence, needing advanced therapeutic treatments and strategies in order to get better recovery from TNBC. Artificial intelligence (AI) has been emerged by giving its high inputs in the automated diagnosis as well as treatment of several diseases, particularly TNBC. AI based TNBC molecular sub-typing, diagnosis as well as therapeutic treatment has become successful now days. Therefore, present review has reviewed recent advancements in the role and assistance of AI particularly focusing on molecular sub-typing, diagnosis as well as treatment of TNBC. Meanwhile, advantages, certain limitations and future implications of AI assistance in the TNBC diagnosis and treatment are also discussed in order to fully understand readers regarding this issue. Graphical Abstract
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14321335
|
ONCOLOGY
|
10.3389/fpsyg.2024.1438581
|
Stochastic heuristics for decisions under risk and uncertainty
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Models of heuristics are often predicated on the desideratum that they should possess no free parameters. As a result, heuristic implementations are usually deterministic and do not allow for any choice errors, as the latter would require a parameter to regulate the magnitude of errors. We discuss the implications of this in light of research that highlights the evidence supporting stochastic choice and its dependence on preferential strength. We argue that, in principle, the existing models of deterministic heuristics should, and can, be quite easily modified to stochastic counterparts through the addition of an error mechanism. This requires a single free parameter in the error mechanism, whilst otherwise retaining the parameter-free cognitive processes in the deterministic component of existing heuristics. We present various types of error mechanisms applicable to heuristics and discuss their comparative virtues and drawbacks, paying particular attention to their impact on model comparisons between heuristics and parameter-rich models.
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16641078
|
PSYCHOLOGY
|
10.1186/s40359-024-01921-4
|
Rates of, and factors associated with, common mental disorders in homeworking UK Government response employees’ during COVID-19: a cross-sectional survey and secondary data analysis
|
Introduction: Working on the frontline during the COVID-19 pandemic has been associated with increased risk to mental health and wellbeing in multiple occupations and contexts. The current study aimed to provide an insight into the rate of probable mental health problems amongst United Kingdom (UK) Government employees who contributed to the COVID-19 response whilst working from home, and to ascertain what factors and constructs, if any, influence mental health and wellbeing in the sample population.Method: This paper reports on the findings from two studies completed by UK Government employees. Study 1: A cross-sectional online survey, containing standardised and validated measures of common mental health disorders of staff who actively contributed to the COVID-19 response from their own homes. Binary logistic regression was used to assess factors associated with mental health outcomes. Study 2: A secondary data analysis of cross-sectional survey data collected across three timepoints (May, June, and August) in 2020 focusing on the wellbeing of employees who worked from home during the COVID-19 pandemic.Results: Study 1: 17.9% of participants met the threshold criteria for a probable moderate anxiety disorder, moderate depression, or post-traumatic stress disorder. Younger, less resilient, less productive individuals, with lower personal wellbeing and less enjoyment of working from home, were more likely to present with poorer mental health. Study 2: Found lower wellbeing was consistently associated with having less opportunities to look after one’s physical and mental health, and having unsupportive line managers and colleagues.Conclusion: It is important to ensure UK Government employees’ psychological needs are met whilst working from home and responding to enhanced incidents. It is recommended that workplaces should be seeking to continually build and improve employee resilience (e.g., through opportunities to increase social ties and support networks), essentially ensuring employees have necessary resources and skills to support themselves and others.
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20507283
|
PSYCHOLOGY
|
10.1007/s00432-024-05910-3
|
ATG10-dependent autophagy is required for DDX10 to regulate cell proliferation, apoptosis and stemness in colorectal cancer
|
Colorectal cancer (CRC) remains a highly prevalent gastrointestinal neoplasm, presenting significant prevalence and lethality rate. DEAD/H box RNA helicase 10 (DDX10) has been proposed as a potential oncogene in CRC, the specific action mechanism by which DDX10 modulates the aggressive biological cellular events in CRC remains implicitly elucidated, however. During this study, DDX10 expression was detected via RT-qPCR and Western blotting. Cell proliferation was estimated via EDU staining. TUNEL staining and Western blotting appraised cell apoptosis. Cell stemness was evaluated by sphere formation assay, RT-qPCR, Western blotting as well as immunofluorescence staining. Relevant assay kit examined aldehyde dehydrogenase (ALDH) activity. Western blotting and immunofluorescence staining also detected autophagy. DDX10 was hyper-expressed in CRC cells. Down-regulation of DDX10 hampered cell proliferation, aggravated the apoptosis while eliminated the ability to form spheroid cells in CRC. In addition, DDX10 deletion improved ATG10 expression and therefore activated autophagy in CRC cells. Consequently, ATG10 depletion or treatment with autophagy inhibitor 3-Methyladenine (3-MA) partially compensated the influences of DDX10 silencing on the proliferation, apoptosis and stemness of CRC cells. Accordingly, DDX10 deficiency may aggravate autophagy mediated by ATG10 to impede cell proliferation, stemness and facilitate cell apoptosis, hence blocking the progression of CRC.
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14321335
|
ONCOLOGY
|
10.3390/ejihpe14080156
|
Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus
|
This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023–2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The “Artificial Intelligence Awareness Scale”, developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.
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22549625
|
PSYCHOLOGY
|
10.3390/educsci14080886
|
Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept’ of AI’s Ability to Assess the Use of Key Microskills
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Advances in artificial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reflective practice of educators in a time when online teaching has become the norm. Having achieved a ‘proof of concept’, this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices.
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22277102
|
EDUCATION
|
10.3390/cancers16162845
|
Deep Neural Network Integrated into Network-Based Stratification (D3NS): A Method to Uncover Cancer Subtypes from Somatic Mutations
|
(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method based on a deep neural network integrated into a network-based stratification framework (D3NS) is proposed to stratify tumors according to somatic mutations. (2) Methods: This approach leverages the power of deep neural networks to detect hidden information in the data by combining the knowledge contained in a network of gene interactions, as typical of network-based stratification methods. D3NS was applied using real-world data from The Cancer Genome Atlas for bladder, ovarian, and kidney cancers. (3) Results: This technique allows for the identification of tumor subtypes characterized by different survival rates and significant associations with several clinical outcomes (tumor stage, grade or response to therapy). (4) Conclusion: D3NS can provide a base model in cancer research and could be considered as a useful tool for tumor stratification, offering potential support in clinical settings.
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20726694
|
ONCOLOGY
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10.1186/s40359-024-01782-x
|
Psychometric properties of the newly developed self-report environmental determinants of health questionnaire (EDH-Q): development and validation
|
The environmental determinants of health (EDH) have a significant impact on people’s physical, mental, and social wellbeing. Everyone needs access to environmental resources of all types, including food, materials, and energy, to survive. Currently, no valid and reliable instrument exists for evaluating individuals’ perceived levels of EDH. Hence, the purpose of this study was to develop and validate the environmental determinants of health questionnaire (EDH-Q) among undergraduate students in Nigeria. We conducted a cross-sectional survey among undergraduate students in Nigeria to assess the psychometric properties of the newly developed Environmental Determinants of Health Questionnaire (EDH-Q). Respondents were selected using a convenience sampling approach to evaluate their perceptions of environmental determinants of health. The Content Validity Index (CVI) and Face Validity Index (FVI) were calculated to ascertain the scale’s content validity and response process validity, respectively. Additionally, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), composite reliability (CR), average variance extracted (AVE), Cronbach’s alpha, and intraclass correlation coefficient (ICC) were computed to assess the scale’s construct validity. The study involved 300 respondents in the EFA (males 55.7%, females 44.3%) and 430 respondents in the CFA (males 54.0%, females 46.0%). In the EFA, two constructs were identified (the natural environment and the built environment). The EFA model was able to explain 63.57% of the total cumulative variance, and the factor correlation was 0.671. The whole scale Cronbach’s alpha value was 0.947, while the two constructs’ Cronbach’s alpha values were 0.918 (natural environment) and 0.935 (built environment). In the CFA, six pairs of error covariances were included between items within the same construct to improve the fit indices of the initial proposed measurement model. The final re-specified measurement model showed that the EDH-Q, which has two constructs and 18 items, has adequate construct validity (CFI = 0.948, TLI = 0.938, SRMR = 0.046, RMSEA = 0.052, and RMSEA p-value = 0.344). The CRs were 0.845 (natural environment) and 0.854 (built environment). The ICCs were 0.976 (natural environment) and 0.970 (built environment). The results show that the newly created EDH-Q has sufficient construct validity and may be utilized to assess participants’ perceptions of their level of EDH. Researchers should examine this instrument in populations with different age ranges and other demographic characteristics, as the present study only applied it to undergraduate students who may share similar characteristics.
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20507283
|
PSYCHOLOGY
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10.1007/s44196-024-00623-4
|
The Application of Big Data and Fuzzy Decision Support Systems in the Innovation of Personalized Music Teaching in Universities
|
Personalized music teaching in universities improves students’ learning and efficiency through adaptive guidance. This adaptability requires large study data and intelligent decisions based on the learner’s ability. This article introduces a Definitive Teaching Support System (DTSS) exclusive to music learning to augment this concept. This system is designed to increase the adaptability of music learning based on student interest and ability. The system is powered by a fuzzy decision system for identifying maximum teaching adaptability to personalized processes. Low-to-high-sorted personalization provides new endorsements for further music sessions in the fuzzy derivative process. Maximum adaptability is the target for new personalized sessions in the universities. This differs for various students from which a common adaptability level for monotonous recommendations is identified. The identified adaptability is set as a global maximum solution towards music learning personalization. The defuzzification reduces the chances of low adaptability by expelling the stationary adaptability outcomes.
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18756883
|
AI
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10.3389/fonc.2024.1422776
|
Applying enhanced recovery after surgery protocols in a patient with a giant spleen: a case report
|
Although splenomegaly is a common finding in several diseases, massive splenomegaly is rare. Patients with massive splenomegaly often present with a complex clinical picture. This case report describes a 72-year-old female with a complex medical history. Fifteen years ago, she was diagnosed with primary myelofibrosis, which subsequently led to progressive abdominal enlargement and bloating over the past 5 years. Recently, she developed edema in her limbs, accompanied by dizziness, shortness of breath, and fatigue. A massive splenomegaly was discovered during her hospitalization. Additionally, the patient has a history of Crohn’s disease, gout, renal insufficiency, and hypertension. Laboratory results reveal severe anemia and thrombocytopenia. Abdominal CT scans confirm the enlarged spleen and show ascites. She was treated by a multidisciplinary team comprising several departments. Even after a period of comprehensive treatment, the symptoms of massive splenomegaly did not significantly improve. Thus, the patient underwent an open surgical excision of the giant spleen. The weight of the giant spleen was 5.0 kg. During the perioperative period, Enhanced Recovery After Surgery (ERAS) protocols were applied to facilitate recovery. Her recovery was uneventful, and she was able to resume her regular daily routine shortly after the procedure. This report presented a complex and rare case of massive splenomegaly, and underscored that a proper medical and nursing care is the key to better recovery.
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2234943X
|
ONCOLOGY
|
10.3389/fonc.2024.1425545
|
Mechanisms of crosstalk between the oropharyngeal microbiome and human papillomavirus in oropharyngeal carcinogenesis: a mini review
|
Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer globally. Notably, human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) is on the rise, accounting for 70% of all OPSCC cases. Persistent high-risk HPV infection is linked to various cancers, but HPV infection alone is not sufficient to cause cancer. Advances in next-generation sequencing have improved our understanding of changes in the human microbiome of cancerous environments. Yet, there remains a dearth of knowledge on the impact of HPV-microbiome crosstalk in HPV-positive OPSCC. In this review, we examine what is known about the oropharyngeal microbiome and the compositional shifts in this microbiome in HPV-positive OPSCC. We also review potential mechanisms of crosstalk between HPV and specific microorganisms. Additional research is needed to understand these interactions and their roles on cancer development and progression.
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2234943X
|
ONCOLOGY
|
10.3390/cancers16162864
|
Real-Life Management of FLT3-Mutated AML: Single-Centre Experience over 24 Years
|
We analyzed 140 patients with a median age of 51 years; 21% had WBC ≥ 100 × 109/L, and 52% had an NPM1 co-mutation. Until 2018, 101 patients received chemotherapy; thereafter, 39 received 3+7+midostaurin. The overall CR rate was 64%, higher in NPM1 mutant patients (73%). Univariate analysis showed that NPM1 mutation (p = 0.032) and WBC < 100 × 109/L (p = 0.013) positively influenced the response, with a trend for FLT3i administration (p = 0.052). Multivariate analysis confirmed WBC count as an independent prognostic factor (p = 0.017). In CR1, 41/90 patients underwent allogeneic and 18 autologous transplantation. The median EFS was 1.1 vs. 1.6 years in autografted and allografted patients, respectively (p = 0.9). The one-year non-relapse mortality was 0.00% for autologous and 28% for allogeneic transplants (p = 0.007); CIR at 1 and 3 years was higher in autologous transplants (39% vs. 15% and 57% vs. 21%, p = 0.004). The median survival was not reached in the FLT3i group. Overall, 69 patients received stem cell transplantation (18 autologous, 51 allogeneic). Post-transplant FLT3i was resumed in eight patients, all alive after a median of 65 months. Allogeneic transplantation is crucial in FLT3-mutated AML, but the next challenge will be to identify which patients can benefit from transplants in CR1 and in which to intensify post-transplant therapy.
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20726694
|
ONCOLOGY
|
10.3390/educsci14080898
|
Investigating the Impact of the Stratified Cognitive Apprenticeship Model on High School Students’ Math Performance
|
This study assessed the impact of a cognitive apprenticeship model (CAM)-based stratified teaching module on the mathematical proficiency of high school students. The stratified cognitive apprenticeship model teaching module (SCTM) first involves grouping students based on their mathematical abilities. Students with higher performance are placed in one class, while those with lower scores are placed in another. Instruction for each group is then conducted using the cognitive apprenticeship model, tailoring the teaching approach to align with the specific needs and abilities of each group. A quasi-experimental design was adopted and 150 students were recruited. This study compared the outcomes of a control group, which was instructed using conventional teaching methods (CI), with those of two experimental groups—one instructed using a stratified cognitive teaching method (SCTM)-based on the CAM—and another instructed using the CAM alone. Students’ performance was evaluated based on a mathematics test including the following dimensions: knowing and understanding, investigating, communication, and application (of mathematical knowledge to real-life problems). The data were analyzed using an analysis of covariance (ANCOVA). The results indicated that students instructed using the SCTM outperformed their peers in mathematical achievement, thereby validating SCTM’s effectiveness as a comprehensive educational strategy for mathematics education at the senior high school level.
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22277102
|
EDUCATION
|
10.3390/ejihpe14080157
|
Affective Regulation and Trait Anger Personalities: The Buffering Effect of the Companion Animal Bond
|
Emotional dysregulation involving anger can have severe consequences on the individual’s psychosocial and emotional functioning. This study aimed to investigate the role that the companion animal bond and the personality dimension of trait anger play in explaining affective dysregulation. A cross-sectional online survey was administered to 365 participants. Using the PROCESS macro for SPSS, a moderated model was tested to analyze the hypothesis that affective dysregulation depends on trait anger and that the companion animal bond moderates the relationship between trait anger and affective dysregulation. The results showed that the effect of trait anger on affective dysregulation increases especially when the degree of bonding to an animal companion is low, suggesting that a strong bond to a companion animal may protect individuals with trait anger from the likelihood of experiencing affective regulation problems. The psychological, health-related, and educational implications of the current anthrozoological study include the potential of the human–animal bond in acting as a facilitator of adaptive affective regulation processes, which can reduce the levels of uncontrolled anger-related emotions and the subsequent risk of out-of-control behaviors.
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22549625
|
PSYCHOLOGY
|
10.3390/ejihpe14080159
|
Development of Internalizing Mental Health Symptoms from Early Childhood to Late Adolescence
|
Children’s mental health symptoms’ development can be characterized by both continuity and discontinuity. However, existing studies ignore the potential discontinuity in children’s internalizing symptoms’ development. Hence, the current study examines continuous and discontinuous developmental trajectories using representative data from a sample of 2792 children (49.10% females) from the Growing Up in Australia cohort assessed seven times (ages 4, 6, 8, 10, 12, 14, 16). Longitudinal measurement invariance analyses revealed that internalizing symptoms were comparable over time. Linear, quadratic, and piecewise latent growth curve models were deployed to estimate the trajectory of internalizing symptoms from early childhood to late adolescence. The analyses showed that internalizing symptoms were characterized by a quadratic-quadratic piecewise growth curve comprising two distinct phases of upward concave growth. Internalizing scores reduced steadily between ages 4 and 8 years but exhibited a slight upward curvature between ages 8 and 10 years. By age 14 years, the trajectory remained relatively stable but spiked between age 14 and 16 years. The two phases of internalizing symptoms’ development were largely unrelated. Overall, the study adds to the knowledge about the development of internalizing mental health from early childhood to late adolescence and highlights the need for additional support in late adolescence.
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22549625
|
PSYCHOLOGY
|
10.3390/ai5030070
|
H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification
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Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications.
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26732688
|
AI
|
10.3389/frai.2024.1398205
|
Diagnostic performance of AI-based models versus physicians among patients with hepatocellular carcinoma: a systematic review and meta-analysis
|
Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain.Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma.Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. Statistical heterogeneity was evaluated using I-squared (I2) and chi-square statistics.Results: We included seven studies in our meta-analysis;. Both physicians and AI-based models scored an average sensitivity of 93%. Great variation in sensitivity, accuracy, and specificity was observed depending on the model and diagnostic technique used. The region-based convolutional neural network (RCNN) model showed high sensitivity (96%). Physicians had the highest specificity in diagnosing hepatocellular carcinoma(100%); furthermore, models-based convolutional neural networks achieved high sensitivity. Models based on AI-assisted Contrast-enhanced ultrasound (CEUS) showed poor accuracy (69.9%) compared to physicians and other models. The leave-one-out sensitivity revealed high heterogeneity among studies, which represented true differences among the studies.Conclusion: Models based on Faster R-CNN excel in image classification and data extraction, while both CNN-based models and models combining contrast-enhanced ultrasound (CEUS) with artificial intelligence (AI) had good sensitivity. Although AI models outperform physicians in diagnosing HCC, they should be utilized as supportive tools to help make more accurate and timely decisions.
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26248212
|
AI
|
10.3389/frai.2024.1408029
|
Refinement of machine learning arterial waveform models for predicting blood loss in canines
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Introduction: Hemorrhage remains a leading cause of death in civilian and military trauma. Hemorrhages also extend to military working dogs, who can experience injuries similar to those of the humans they work alongside. Unfortunately, current physiological monitoring is often inadequate for early detection of hemorrhage. Here, we evaluate if features extracted from the arterial waveform can allow for early hemorrhage prediction and improved intervention in canines.Methods: In this effort, we extracted more than 1,900 features from an arterial waveform in canine hemorrhage datasets prior to hemorrhage, during hemorrhage, and during a shock hold period. Different features were used as input to decision tree machine learning (ML) model architectures to track three model predictors—total blood loss volume, estimated percent blood loss, and area under the time versus hemorrhaged blood volume curve.Results: ML models were successfully developed for total and estimated percent blood loss, with the total blood loss having a higher correlation coefficient. The area predictors were unsuccessful at being directly predicted by decision tree ML models but could be calculated indirectly from the ML prediction models for blood loss. Overall, the area under the hemorrhage curve had the highest sensitivity for detecting hemorrhage at approximately 4 min after hemorrhage onset, compared to more than 45 min before detection based on mean arterial pressure.Conclusion: ML methods successfully tracked hemorrhage and provided earlier prediction in canines, potentially improving hemorrhage detection and objectifying triage for veterinary medicine. Further, its use can potentially be extended to human use with proper training datasets.
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26248212
|
AI
|
10.3389/feduc.2024.1442318
|
An inclusive Research and Education Community (iREC) model to facilitate undergraduate science education reform
|
Over the last two decades, there have been numerous initiatives to improve undergraduate student outcomes in STEM. One model for scalable reform is the inclusive Research Education Community (iREC). In an iREC, STEM faculty from colleges and universities across the nation are supported to adopt and sustainably implement course-based research – a form of science pedagogy that enhances student learning and persistence in science. In this study, we used pathway modeling to develop a qualitative description that explicates the HHMI Science Education Alliance (SEA) iREC as a model for facilitating the successful adoption and continued advancement of new curricular content and pedagogy. In particular, outcomes that faculty realize through their participation in the SEA iREC were identified, organized by time, and functionally linked. The resulting pathway model was then revised and refined based on several rounds of feedback from over 100 faculty members in the SEA iREC who participated in the study. Our results show that in an iREC, STEM faculty organized as a long-standing community of practice leverage one another, outside expertise, and data to adopt, implement, and iteratively advance their pedagogy. The opportunity to collaborate in this manner and, additionally, to be recognized for pedagogical contributions sustainably engages STEM faculty in the advancement of their pedagogy. Here, we present a detailed pathway model of SEA that, together with underpinning features of an iREC identified in this study, offers a framework to facilitate transformations in undergraduate science education.
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2504284X
|
EDUCATION
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10.3389/feduc.2024.1333697
|
How can multiculturalism be celebrated through teacher training in Israel?
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Can we celebrate multiculturalism through teachers’ training in a heterogeneous and diverse setting such as Israeli society? The current study examines teachers’ processes through an online teachers’ professional development program and an interactive activity, where 68 Israeli teachers shared their cultural stories with teachers from other cultures. Findings indicate that the teachers who met with teachers from other cultures, whom they usually do not meet, wanted to learn about each other’s culture, including their religious values, practices, and traditions while looking for commonalities. Furthermore, such intercultural meetings can occur online if the activities are designed to foster meaningful meetings and discussions between different cultures despite the social rifts and the separation within the education system.
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2504284X
|
EDUCATION
|
10.3390/educsci14090930
|
Mobile Smartphones as Tools for ICT Integration in Geography Teaching
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This article seeks to reflect on the opportunities that mobile smartphones (MSPs) present as ICT integration tools in teaching geography. The more extensive study, underpinned by the Professional Development Framework for Digital Learning (PDFDL) in ICT integration, employed a qualitative research approach. Lensed by the Professional Development Framework for Digital Learning (PDFDL), the article used the qualitative approach to garner insights from the participants regarding using MSPs as tools to integrate ICT in geography teaching. Data collection tools included interviews, observations, and document reviews. Researchers sampled (n = 4) schools, interviewed and observed (n = 13) teachers, and interviewed (n = 10) learners and (n = 8) parents in the province of KwaZulu-Natal. Furthermore, they used a purposive sampling technique to access the participants, basing the research on the premise that MSPs promote virtual reality for an array of learners. As the findings revealed, although some participants viewed the use of MSPs as a distractor in the learning space, teachers felt compelled to heed the call to modify their teaching pedagogies, such that they integrated mobile phones fruitfully in their teaching. The findings further revealed that such a paradigm shift would benefit homeschooling and facilitate a dual teaching mode at learning institutions. Curriculum planners are responsible for helping teachers accept that uncertainty is the only certainty about the future, considering the volatility, uncertainty, complexity, and augmentation (VUCA) challenges brought on by the COVID-19 pandemic. Extended lockdown periods accelerated the use of MSPs in teaching, requiring every stakeholder in the educational space to become a life-long learner by using a range of technologies and platforms.
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22277102
|
EDUCATION
|
10.1186/s40594-024-00499-y
|
Unlocking STEM pathways: A person-centred approach exploring a teacher recruitment intervention
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This research employed a person-centred approach to evaluate the effectiveness of a recruitment intervention aimed at attracting STEM undergraduate students to the teaching profession. The study aimed to identify participant profiles based on changes of interest in teaching, examine the demographic factors associated with these profiles, and explore the outcomes associated with the identified profiles. A total of 267 participants from 18 universities in England were recruited for the study. The intervention involved presenting 12 vignettes that depicted different motivations for choosing teaching as a career. Participants rated their change of interest in teaching after reading each vignette. The latent profile analysis revealed four distinct profiles: dissuaded participants, unpersuaded participants, moderately persuaded participants, and highly persuaded participants. The highly persuaded profile reported the highest levels of self-efficacy, interest, perceived fit, and enjoyment in teaching. Participants from higher socioeconomic backgrounds were more likely to be persuaded by the recruitment intervention, but gender, ethnicity, or program levels did not significantly affect profile membership. The findings demonstrate the potential of recruitment interventions to influence the interest of STEM undergraduate students in teaching and underscore the importance of considering individual characteristics and motivations when attracting prospective teachers to the profession.
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21967822
|
EDUCATION
|
10.3390/educsci14090938
|
Application of Student-Centered Learning in Improving Teaching English as a Foreign Language Students’ 21st-Century Skills Performance
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A student-centered learning (SCL) method has been applied to improve Teaching English as a Foreign Language (TEFL) students’ 21st-century skills at the English department of a university. Therefore, this study aimed to investigate the effect of SCL application on TEFL students’ 21st-century skills performance. To achieve this objective, a total of 220 questionnaires were distributed to TEFL students, and ten course designs were obtained from the department. Content analysis on course designs showed that hard skills were more prioritized than soft skills, while character slightly ebbed in learning design. Furthermore, SCL application through Group, Independent, and Online learning methods significantly increased TEFL students’ 21st-century skills. Hard and soft skills were most and slightly associated with cumulative grade point average (CGPA), respectively. These results showed that SCL should be properly applied to deliver course content and improve 21st-century skills performance.
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22277102
|
EDUCATION
|
10.3389/frai.2024.1402098
|
Bayesian model of tilling wheat confronting climatic and sustainability challenges
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Conventional farming poses threats to sustainable agriculture in growing food demands and increasing flooding risks. This research introduces a Bayesian Belief Network (BBN) to address these concerns. The model explores tillage adaptation for flood management in soils with varying organic carbon (OC) contents for winter wheat production. Three real soils, emphasizing texture and soil water properties, were sourced from the NETMAP soilscape of the Pang catchment area in Berkshire, United Kingdom. Modified with OC content at four levels (1, 3, 5, 7%), they were modeled alongside relevant variables in a BBN. The Decision Support System for Agrotechnology Transfer (DSSAT) simulated datasets across 48 cropping seasons to parameterize the BBN. The study compared tillage effects on wheat yield, surface runoff, and GHG-CO2 emissions, categorizing model parameters (from lower to higher bands) based on statistical data distribution. Results revealed that NT outperformed CT in the highest parametric category, comparing probabilistic estimates with reduced GHG-CO2 emissions from “7.34 to 7.31%” and cumulative runoff from “8.52 to 8.50%,” while yield increased from “7.46 to 7.56%.” Conversely, CT exhibited increased emissions from “7.34 to 7.36%” and cumulative runoff from “8.52 to 8.55%,” along with reduced yield from “7.46 to 7.35%.” The BBN model effectively captured uncertainties, offering posterior probability distributions reflecting conditional relationships across variables and offered decision choice for NT favoring soil carbon stocks in winter wheat (highest among soils “NT.OC-7%PDPG8,” e.g., 286,634 kg/ha) over CT (lowest in “CT.OC-3.9%PDPG8,” e.g., 5,894 kg/ha). On average, NT released minimum GHG- CO2 emissions to “3,985 kgCO2eqv/ha,” while CT emitted “7,415 kgCO2eqv/ha.” Conversely, NT emitted “8,747 kgCO2eqv/ha” for maximum emissions, while CT emitted “15,356 kgCO2eqv/ha.” NT resulted in lower surface runoff against CT in all soils and limits runoff generations naturally for flood alleviation with the potential for customized improvement. The study recommends the model for extensive assessments of various spatiotemporal conditions. The research findings align with sustainable development goals, e.g., SDG12 and SDG13 for responsible production and climate actions, respectively, as defined by the Agriculture and Food Organization of the United Nations.
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26248212
|
AI
|
10.3390/educsci14090953
|
Transforming Education Leadership through Multiple Approaches to Develop and Support School Leadership
|
This article elaborates on the multiple approaches to develop and support school leadership. In a 5-year quasi-experimental longitudinal mixed-methods study based on a sample of 122 schools in three regions in a German state, 75 school leaders and their teams participated in a 3-year program using multiple approaches; the rest served as the control group. The multiple approaches covered the school leaders’ (a) professional development, comprising (i) a professional development program, (ii) individual coaching series, and (b) support for them, including (iii) school consultancy and (iv) additional financial resources. The quality of the interventions (regarding both the process and didactic qualities, as well as outcome qualities) and how the quality of both the school leadership and the schools changes over time as a consequence of these interventions are analyzed. The study’s results show a highly positive assessment of the quality and advantages of the multiple approaches and their benefits for the quality of school leadership and further aspects of the school. The regression analyses demonstrate that positively perceived outcome qualities of the interventions are associated with improvements in numerous dimensions of school quality.
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22277102
|
EDUCATION
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10.1186/s40594-024-00502-6
|
One size doesn’t fit all: how different types of learning motivations influence engineering undergraduate students’ success outcomes
|
Motivation is the inherent belief to guide students learning goals and behaviors to make continuous efforts and strengthen learning outcomes. Previous research reported the positive impacts of learning motivation on student success, but there have been limited efforts in systematically and structurally studying different types of motivations and their impacts on students’ success in engineering education. The current study contributes to the literature by systematically examining two important types of motivations and their influences on undergraduate engineering students in a theoretically grounded manner while using an advanced analytical approach. The current study conducted a cross-sectional survey with undergraduate engineering students (n = 514) from 18 different schools across nine U.S. states. The survey assessed students’ self-report scores on six types of motivations to study developed based on formative research and the current literature and then collected students’ self-reported learning outcomes, current GPA, university satisfaction, engineering program satisfaction, and individual demographic factors. The data were then analyzed using structural equation modeling. The results showed that motivations related to family, personality, and academic expectations were consistently positively associated with all measured students’ success outcomes; motivations related to educators were associated with all four outcomes but student GPA; motivations related to course contents were associated with learning outcomes and student GPA; and motivations related to peers did not predict any of the four measured students’ success outcomes. We explain some of the unexpected results with further literature that examines engineering culture and ecology. We also make recommendations related to cognitive training, tailored engineering education, peer culture interventions, and family orientation programs.
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21967822
|
EDUCATION
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10.3389/frai.2024.1425713
|
Fall risk prediction using temporal gait features and machine learning approaches
|
Introduction: Falls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible.Methods: This study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers.Results: Two experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model’s ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task.Discussion: The findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model’s generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.
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26248212
|
AI
|
10.3390/educsci14090957
|
Where Are the Costs? Using an Economic Analysis of Educational Interventions Approach to Improve the Evaluation of a Regional School Improvement Programme
|
Education systems are moving to a more evidence-informed paradigm to improve outcomes for learners. To help this journey to evidence, robust qualitative and quantitative research can help decisionmakers identify more promising approaches that provide value for money. In the context of the utilisation of scarce resources, an important source of evidence commonly used in health and social care research is an understanding of the economic impact of intervention choices. However, there are currently very few examples where these methodologies have been used to improve the evaluation of education interventions. In this paper we describe the novel use of an economic analysis of educational interventions (EAEI) approach to understand both the impact and the cost of activities in the evaluation of a formative assessment implementation project (FAIP) designed to improve teachers’ understanding and use of formative assessment strategies. In addition to utilising a mixed method quasi-experimental design to explore the impact on learner wellbeing, health utility and attainment, we describe the use of cost-consequence analysis (CCA) to help decisionmakers understand the outcomes in the context of the resource costs that are a crucial element of robust evaluations. We also discuss the challenges of evaluating large-scale, universal educational interventions, including consideration of the economic tools needed to improve the quality and robustness of these evaluations. Finally, we discuss the importance of triangulating economic findings alongside other quantitative and qualitative information to help decisionmakers identify more promising approaches based on a wider range of useful information. We conclude with recommendations for more routinely including economic costs in education research, including the need for further work to improve the utility of economic methods.
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22277102
|
EDUCATION
|
10.3390/ai5030075
|
Effective Hybrid Structure Health Monitoring through Parametric Study of GoogLeNet
|
This paper presents an innovative approach that utilizes infused images from vibration signals and visual inspections to enhance the efficiency and accuracy of structure health monitoring through GoogLeNet. Scrutiny of the structure of GoogLeNet identified four key parameters, and thus, the optimization of GoogLeNet was completed through manipulation of the four key parameters. First, the impact of the number of inception modules on the performance of GoogLeNet revealed that employing eight inception layers achieves remarkable 100% accuracy while requiring less computational time compared to nine layers. Second, the choice of activation function was studied, with the Rectified Linear Unit (ReLU) emerging as the most effective option. Types of optimizers were then researched, which identified Stochastic Gradient Descent with Momentum (SGDM) as the most efficient optimizer. Finally, the influence of learning rate was compared, which found that a rate of 0.001 produces the best outcomes. By amalgamating these findings, a comprehensive optimized GoogLeNet model was found to identify damage cases effectively and accurately through infused images from vibrations and visual inspections.
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26732688
|
AI
|
10.3390/cancers16173032
|
Predictors of Clinical Hematological Toxicities under Radiotherapy in Patients with Cervical Cancer—A Risk Analysis
|
Background: Cervical cancer ranks third in frequency among female cancers globally and causes high mortality worldwide. Concurrent chemoradiotherapy improves the overall survival in cervical cancer patients by 6% but it can cause significant acute and late toxicities affecting patient quality of life. Whole pelvis radiotherapy doses of 10–20 Gy can lead to myelosuppression and to subsequent hematological toxicities since pelvic bones contain half of bone marrow tissue. Methods: A total of 69 patients with IB-IVB-staged cervical cancer have been included in this retrospective cohort study. We analyzed clinical adverse events and changes in blood cell counts (hemoglobin, neutrophils, leukocytes, and platelets) during radiation or chemoradiotherapy received at the Oncological Institute of Bucharest from 2018 to 2021. Results: Decreases in hemoglobin levels of over 2.30 g/dL during treatment were associated with BMI > 23.2 kg/m2 (OR = 8.68, 95%CI = [1.01, 75.01]), age over 53 years (OR = 4.60 95%CI = [1.10, 19.22]), with conformational 3D irradiation (OR = 4.78, 95%CI = [1.31, 17.40]) and with total EQD2 of over 66.1 Gy (OR = 3.67, 95%CI = [1.02, 13.14]). The hemoglobin decrease rate of 0.07 g/dL/day was related to 95% isodose volume (OR = 18.00). Neutropenia is associated frequently with gastrointestinal side effects and with the bowel and rectal V45 isodoses (OR = 16.5 and OR = 18.0, respectively). Associations of total external and internal radiation dose with the time durations calculated from the initiation of treatment to the onset of hematological adverse reactions were also obtained. The maximum drop in leukocytes was observed before day 35 from the RT initiation in patients who underwent treatment with 3D conformal radiotherapy (OR = 4.44, 95%CI = [1.25, 15.82]). Neutrophil levels under 2.2 × 103/μL and thrombocyte levels under 131 × 103/μL during the follow-up period were associated with a total planned dose of 54 Gy to the pelvic region volume (OR = 6.82 and OR = 6.67, respectively). Conclusions: This study shows the existence of clinical and blood predictors of hematological adverse reactions in cervical cancer patients. Thus, patients who are in a precarious clinical situation, with low hematological values (but not yet abnormal), should be monitored during days 29–35 after the initiation of RT, especially if they are obese or over 53 years of age.
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20726694
|
ONCOLOGY
|
10.1186/s40594-024-00501-7
|
Academic social comparison: a promising new target to reduce fear of negative evaluation in large-enrollment college science courses
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Fear of negative evaluation, defined as a sense of dread associated with being unfavorably evaluated in a social situation, is the primary factor underlying student anxiety in college science courses and is disproportionately experienced by students who are underserved in science. Yet, it is unknown why fear of negative evaluation disproportionately affects these students and what can be done to reduce student fear of negative evaluation. Academic social comparison describes how students perceive themselves compared to their peers with regard to desirability as a groupmate, the extent they fit in among others in their major, and academic talent. We hypothesize that academic social comparison mediates the relationship between student identities and fear of negative evaluation, where individuals with underserved identities in science may perceive themselves as “less than” their peers, contributing to their fear of negative evaluation. We surveyed 909 undergraduate science majors across 15 research-intensive institutions in the United States (U.S.) to assess: (1) To what extent do student identities predict fear of negative evaluation among science undergraduates? and (2) For identities that significantly predict fear of negative evaluation, to what extent does academic social comparison mediate the relationship? We used regression, single-mediator models, and multi-mediator models to address our research questions. Women/non-binary and LGBTQ + science majors reported disproportionately high fear of negative evaluation compared to men and non-LGBTQ + science majors. Women/non-binary and LGBTQ + students also expressed lower academic social comparison relative to their respective counterparts, meaning they perceive themselves as less than their peers with regard to their desirability as a groupmate, the extent to which they fit in among others in their major, and their academic talent. Academic social comparison partially mediated the relationship between fear of negative evaluation and both gender and LGBTQ + status. Major fit, defined as the extent to which students perceive they fit in among others in their major, was found to be the primary mediating subconstruct of academic social comparison for both gender and LGBTQ + identities. Women/non-binary and LGBTQ + science majors perceive themselves as less than their peers to a greater extent than men and non-LGBTQ + science majors, contributing to their higher fear of negative evaluation in college science course. Major fit, defined as the extent to which students feel they fit in with others in their major, is the subconstruct of academic social comparison that had the strongest influence on fear of negative evaluation in our sample. Academic social comparison is a promising target for future efforts aimed at decreasing fear of negative evaluation in active learning college science courses.
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21967822
|
EDUCATION
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10.3389/feduc.2024.1392104
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MAICC model: development of complex thinking through citizen science project evaluation
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As traditional education systems struggle to keep up with technological advances, incorporating open science into Education 5.0 is critical to addressing student skills gaps. In this study, the MAICC model is introduced, a tool designed to foster complex thinking in higher education students through the evaluation of citizen science projects. It integrates research-based learning and service learning, and helps develop critical and reflective skills by applying them to real-life settings. To assess student engagement and skills development, a mixed methods approach combining qualitative and quantitative analysis was used. Findings indicate that the MAICC model promotes complex thinking, enhances critical thinking through citizen science project evaluation, and features an emphasis on citizen science and educational technology. Discussion highlights citizen science’s important role in education and suggests future research exploring its wider application across disciplines and contexts to enhance 21st century skills.
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2504284X
|
EDUCATION
|
10.3390/educsci14090962
|
A Justice-Oriented Conceptual and Analytical Framework for Decolonising and Desecularising the Field of Educational Technology
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Education systems globally are increasingly being shaped by the logics, assumptions and pedagogical underpinnings of educational technology (EdTech) products, services, programmes, policies, and systems. These often promote rationalistic, secular, universal, objectivist, (post)modernist, written, behaviourist, and individualistic ways of being, marginalising religious, spiritual, oral, subjective, critical, and communitarian ways of being. Given that technological ways of being have been propagated globally, these logics are no longer predominantly promoted by those in the Global North, but by techno-solutionists globally, although the core-to-periphery flows of ideology and funding are still prominent. This article develops a conceptual and analytical framework for decolonising and desecularising the field of EdTech. Concepts are drawn from various discourses: the desecularisation of knowledge to set the ontological framing; embodied cognition to set the epistemological framing; and social justice and decolonial discourses to set the axiological framing. From this, the article develops the Dimensions of Human Injustice Analytical Framework—covering material, ontological and epistemic, and (geo)political injustices—to assist policymakers, educators, EdTech developers, and international development practitioners in identifying and confronting coloniality in their EdTech. Acknowledging the complexity and contentions within decolonial thought, this article does not claim a unified stance on achieving justice but aims to offer a tool for deconstructing and questioning injustices.
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22277102
|
EDUCATION
|
10.3389/fonc.2024.1438179
|
New insights into acinic cell carcinoma of the breast: clinicopathology, origin of histology, molecular features, prognosis, and treatment
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Acinic cell carcinoma (AciCC) of the breast is a rare malignant epithelial neoplasm, with approximately 60 cases reported in the literature. It predominantly affects women and exhibits significant histological heterogeneity. The diagnosis of breast AciCC is primarily based on the presence of eosinophilic and/or basophilic granular cytoplasm and markers of serous acinar differentiation. Despite being considered a low-grade variant of conventional triple-negative breast cancer (TNBC), over 25% of patients with breast AciCC have adverse clinical outcomes. Additionally, in early research, microglandular adenosis (MGA) and atypical MGA were considered potential precursors for various breast cancers, including intraductal carcinoma, invasive ductal carcinoma, adenoid cystic carcinoma, metaplastic carcinoma, and AciCC. Similarly, some studies have proposed that breast AciCC should be considered a type of carcinoma developing in MGA with acinic cell differentiation rather than a distinct entity. Therefore, the pathogenesis of breast AciCC has not yet been clarified. Moreover, to the best of our knowledge, the literature has not summarized the latest prognosis and treatment of breast AciCC. In this review, we synthesized the current literature and the latest developments, aiming at exploring the clinicopathology, histological origin, molecular features, prognosis, and treatment of breast AciCC from a novel perspective.
|
2234943X
|
ONCOLOGY
|
10.1007/s44196-024-00635-0
|
An Improved Adaptive Neuro-fuzzy Inference Framework for Lung Cancer Detection and Prediction on Internet of Medical Things Platform
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It has become increasingly difficult for medical practitioners to recognize illness in recent years due to the emergence of new diseases from their myriad causes on a daily basis. Due in large part to inadequate diagnostic and monitoring infrastructure, a substantial amount of illness and death are associated with lung cancer (LC). The aim of the paper is to find lung cancer early and help patients receive curative treatment. Quitting smoking or never starting is the best way to mitigate the potential for disease-related death. As a result, cutting-edge detection and monitoring technologies must be developed to enable rapid, accurate, and timely diagnosis. Fuzzy logic (FL) is one of the best approaches to modeling complex and uncertain systems; therefore, it helps us deal with these challenges. Fuzzy expert system for lung cancer [FES-LC] detection and prediction on Internet of medical things (IoMT) is employed to overcome the challenges. Hence, an enhanced adaptive neuro-fuzzy inference framework [ANF-IF] is proposed in the current research. The cloud-based application of an adaptive neuro-fuzzy inference system yields four risk categories: not at risk, slightly at risk, moderately at risk, and severely at risk. New methods and theoretical frameworks have made it possible to diagnose LC in its earliest stages with the help of magnetic nanoparticles (MNPs), which allow researchers to overcome the limitations of conventionally slow diagnostic efficiency. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, false positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others.
|
18756883
|
AI
|
10.3389/frai.2024.1457586
|
AI integration in nephrology: evaluating ChatGPT for accurate ICD-10 documentation and coding
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Background: Accurate ICD-10 coding is crucial for healthcare reimbursement, patient care, and research. AI implementation, like ChatGPT, could improve coding accuracy and reduce physician burden. This study assessed ChatGPT’s performance in identifying ICD-10 codes for nephrology conditions through case scenarios for pre-visit testing.Methods: Two nephrologists created 100 simulated nephrology cases. ChatGPT versions 3.5 and 4.0 were evaluated by comparing AI-generated ICD-10 codes against predetermined correct codes. Assessments were conducted in two rounds, 2 weeks apart, in April 2024.Results: In the first round, the accuracy of ChatGPT for assigning correct diagnosis codes was 91 and 99% for version 3.5 and 4.0, respectively. In the second round, the accuracy of ChatGPT for assigning the correct diagnosis code was 87% for version 3.5 and 99% for version 4.0. ChatGPT 4.0 had higher accuracy than ChatGPT 3.5 (p = 0.02 and 0.002 for the first and second round respectively). The accuracy did not significantly differ between the two rounds (p > 0.05).Conclusion: ChatGPT 4.0 can significantly improve ICD-10 coding accuracy in nephrology through case scenarios for pre-visit testing, potentially reducing healthcare professionals’ workload. However, the small error percentage underscores the need for ongoing review and improvement of AI systems to ensure accurate reimbursement, optimal patient care, and reliable research data.
|
26248212
|
AI
|
10.1007/s44196-024-00639-w
|
Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention
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Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.
|
18756883
|
AI
|
10.3390/ai5030078
|
Generative Models Utilizing Padding Can Efficiently Integrate and Generate Multi-Omics Data
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Technological advances in information-processing capacity have enabled integrated analyses (multi-omics) of different omics data types, improving target discovery and clinical diagnosis. This study proposes novel artificial intelligence (AI) learning strategies for incomplete datasets, common in omics research. The model comprises (1) a multi-omics generative model based on a variational auto-encoder that learns tumor genetic patterns based on different omics data types and (2) an expanded classification model that predicts cancer phenotypes. Padding was applied to replace missing data with virtual data. The embedding data generated by the model accurately classified cancer phenotypes, addressing the class imbalance issue (weighted F1 score: cancer type > 0.95, primary site > 0.92, sample type > 0.97). The classification performance was maintained in the absence of omics data, and the virtual data resembled actual omics data (cosine similarity mRNA gene expression > 0.96, mRNA isoform expression > 0.95, DNA methylation > 0.96). Meanwhile, in the presence of omics data, high-quality, non-existent omics data were generated (cosine similarity mRNA gene expression: 0.9702, mRNA isoform expression: 0.9546, DNA methylation: 0.9687). This model can effectively classify cancer phenotypes based on incomplete omics data with data sparsity robustness, generating omics data through deep learning and enabling precision medicine.
|
26732688
|
AI
|
10.3389/fpsyg.2024.1335886
|
Effect of social support on Muslim women’s sporting activities: mediating effect of psychological adjustment
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Objective: This study explores the relationship between social support and sporting activities of Muslim women and constructs a mediation model through role of psychological adjustment.Methods: Using stratified cluster sampling, 301 Muslim women were measured in group psychology using the Social Support Scale and the Sports Activities and Psychological Adjustment Scale. The statistical software SPSS 24.0 and SPSS PROCESS 3.3 were used for statistical processing. The common-method variation test was carried out using the Harman single-factor control test. Finally, the Bootstrap sampling test method and process plug-in were used to test the significance of the intermediary effect.Results: (1) Social support has a significant predictive effect on sports activities (β = 0.32, p < 0.001); (2) psychological adjustment (β = 0.552, p < 0.001) mediates the relationship between social support and sporting activities [social support → psychological adjustment → sporting activities (95% Cl, 0.093, 0.323)].Conclusion: Social support positively influences sporting participation among Muslim women, and psychological adjustment mediates this relationship. Strengthening social support for Muslim women can enhance their psychological adjustment, thereby improving their participation in sporting activities and offering valuable theoretical and practical guidance.
|
16641078
|
PSYCHOLOGY
|
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