mdpi-ai/citelens huggingface dataset:
Teaching Experience as a Key Factor in Dealing with Digital Teaching Stress
Field: EDUCATION

Digital pandemic stress among university faculty has become a key issue in the contemporary era, marked by the rapid transition to online teaching. This study conducts a quantitative investigation into the teaching experience as a key explanatory variable in explaining the levels of such stress. For this purpose, a validated instrument has been used, which has been answered by a sample of 1240 university professors. The results show that, although the participating professors do not express high self-concepts of their digital competence or professional aspects, they do not express high levels of digital stress due to the pandemic. However, strong divergences have been identified between the levels of digital pandemic stress of more experienced professors and those of younger professors. Specifically, more experienced professors report lower levels of stress than younger professors, although there are no significant differences in their respective digital competencies. Consequently, the results suggest that teaching experience mitigates teaching digital stress, even when this greater experience does not concur with greater digital competence. It has also been found that the evolution of ratings with teaching experience depends on whether the professor is a specialist in scientific–technical or humanistic–social areas. Specifically, professors in scientific–technical areas with 15 to 25 years of experience are those who suffer more digital stress. Moreover, the digital stress of professors in scientific–technical areas increases between 10 and 25 years of experience, while it decreases among professors with less than 10 years of experience. In contrast, among professors in humanistic–social areas, the trend in the evolution of digital stress is the opposite: it increases among those with less than 10 years of experience and decreases among those with more than 10 years of experience.
MLAWSMOTE: Oversampling in Imbalanced Multi-label Classification with Missing Labels by Learning Label Correlation Matrix
Field: AI

Missing labels in multi-label datasets are a common problem, especially for minority classes, which are more likely to occur. This limitation hinders the performance of classifiers in identifying and extracting information from minority classes. Oversampling is an effective method for addressing imbalanced multi-label problems by generating synthetic instances to create a class-balanced dataset. However, the existing oversampling algorithms mainly focus on the location of the generated data, and there is a lack of design on how to complete the labels of the synthetic data. To address this issue, we propose MLAWSMOTE, a synthetic data generation algorithm based on matrix factorization weights. We introduce a weak supervised learning method in the oversampling method, optimize the weights of features and labels by using label correlation, and iteratively learn the ideal label weights. The mapping relationship between features and labels is learned from the dataset and the label correlation matrix. The oversampling ratio is defined based on the discrepancy between observed labels and the ideal label of synthetic instances. It mitigates the impact of missing minority labels on the model’s predictions. The labeling of synthetic instances is performed based on label prediction, and the potential labeling distribution is complemented. Experimental results on multiple multi-label datasets under different label missing ratios demonstrate the effectiveness of the proposed method in terms of ACC, Hamming loss, MacroF1 and MicroF1. In the validation of the four classifiers, MacroF1 decreased by 24.78%, 17.81%, 3.8% and 19.56%, respectively, with the increase of label loss rate. After applying MLAWSMOTE only decreased by 15.79%, 13.63%, 3.78% and 15.21%.
“It actually helped”: students’ perceptions of feedback helpfulness prior to and following a teacher professional learning intervention
Field: EDUCATION

This study investigated the effects of a teacher professional learning intervention, underpinned by a student-centred model of feedback, on student perceptions of feedback helpfulness. The study was conducted in the context of primary education English writing in Queensland, Australia. No overall differences in feedback perceptions of students in 13 intervention and 9 comparison schools were identified following the intervention. However, more detailed analyses revealed significantly greater increases in perceived helpfulness among intervention group students for six feedback strategies. This suggests the intervention changed teachers’ feedback practices, enhancing student perceptions of feedback helpfulness. Student focus group data provided valuable qualitative insights into student feedback perceptions. Overall findings highlight the interrelatedness between feedback strategies across the feedback cycle for enhancing student learning.
Construction of Risk Prediction Models for Enterprise Finance Sharing Operations Using K-Means and C4.5 Algorithms
Field: AI

The evaluation of financial sharing centres in enterprises typically relies on outdated financial data, lacks comprehensive assessment, and presents risks such as employee misconduct. To address these challenges, we propose a risk prediction model for enterprise financial sharing operations based on the K-means clustering algorithm for performance evaluation and the C4.5 algorithm for managing employee risks. Our approach enhances the accuracy and objectivity of performance evaluation while improving the efficiency of personnel risk management. Results indicate that the K-means algorithm classifies employee performance into five levels, facilitating comprehensive performance evaluation. Furthermore, through risk management optimisation, accuracy and recall rates increase to 0.905 and 0.890, respectively. The proposed risk prediction model achieves high accuracy rates of 90.5% and 92.4% in the training and test sets, respectively. Practical application of our methodology and model in A Group's financial sharing centre demonstrates their effectiveness and potential for enhancing the operation and management of enterprise financial sharing centres.
A methodology for planning, implementation and evaluation of skills intelligence management – results of a design science project in technology organisations
Field: AI

Introduction: The evolving labour market requirements amidst digital transformation necessitate robust skills intelligence for informed decision-making and adaptability. Novel technologies such as Big Data, Machine Learning, and Artificial Intelligence have significant potential for enhancing skills intelligence.Methods: This study bridges the gap between theory and practice by designing a novel software artefact for skills intelligence management. With its systematic framework for identifying skills intelligence elements, an assessment instrument, and an implementation methodology, the artefact ensures a thorough approach to skills intelligence management.Results: The artefact was demonstrated in 11 organisations. Feedback collected from interviews, focus group sessions, and observations (N = 19) indicated that the artefact is a feasible starting point for implementing or systematising skills intelligence management. Participants suggested improvements but concurred that the systematic approach enhances skills intelligence data collection and quality.Discussion: The study shows that the artefact facilitates the application of advanced technologies in skills intelligence management. Additionally, it contributes a set of principles for effective skills intelligence management, fostering a broader conversation on this critical topic. Participants’ feedback underscores the artefact’s potential and provides a basis for further refinement and application in diverse organisational contexts.
Analysis by TeloView® Technology Predicts the Response of Hodgkin’s Lymphoma to First-Line ABVD Therapy
Field: ONCOLOGY

Classic Hodgkin’s lymphoma (cHL) is a curable cancer with a disease-free survival rate of over 10 years. Over 80% of diagnosed patients respond favorably to first-line chemotherapy, but few biomarkers exist that can predict the 15–20% of patients who experience refractory or early relapsed disease. To date, the identification of patients who will not respond to first-line therapy based on disease staging and traditional clinical risk factor analysis is still not possible. Three-dimensional (3D) telomere analysis using the TeloView® software platform has been shown to be a reliable tool to quantify genomic instability and to inform on disease progression and patients’ response to therapy in several cancers. It also demonstrated telomere dysfunction in cHL elucidating biological mechanisms related to disease progression. Here, we report 3D telomere analysis on a multicenter cohort of 156 cHL patients. We used the cohort data as a training data set and identified significant 3D telomere parameters suitable to predict individual patient outcomes at the point of diagnosis. Multivariate analysis using logistic regression procedures allowed for developing a predictive scoring model using four 3D telomere parameters as predictors, including the proportion of t-stumps (very short telomeres), which has been a prominent predictor for cHL patient outcome in a previously published study using TeloView® analysis. The percentage of t-stumps was by far the most prominent predictor to identify refractory/relapsing (RR) cHL prior to initiation of adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD) therapy. The model characteristics include an AUC of 0.83 in ROC analysis and a sensitivity and specificity of 0.82 and 0.78 respectively.
The moderating effect of altruism on the relationship between occupational stress and turnover intentions: a cross-sectional study of community rehabilitation workers in China
Field: PSYCHOLOGY

Background: In China, community rehabilitation workers are facing a growing challenge related to heavy occupational stress, which is having an impact on employment turnover. Previous studies have explored the effect of the public service motivation of workers in “helping” jobs on occupational stress or turnover intention, but there is a lack of clarification of the impact of altruism on turnover intention in the case of complex pathways involving various factors. Methods: A stratified sampling method was used, and a total of 82 community rehabilitation workers who assist disabled people from 34 community health centres in Jiangmen city were included in the study from August to October 2022. The turnover intention, occupational stress, burnout, quality of life, altruism, and certain sociodemographic information of community rehabilitation workers were measured using a structured questionnaire. The partial least squares method was employed to construct and test the structural equation model. Results: Although altruism had no direct impact on occupational stress or turnover intention, altruism moderated the effect of occupational stress on burnout (βMod = −0.208) and quality of life (βMod = 0.230) and weakened the mediation of burnout and quality of life between occupational stress and turnover intention. Conclusions: This study proposes to address the dilemma of “strong function” and “weak specialty” in community rehabilitation services and to conduct positive psychological interventions for community rehabilitation workers through the guidance of altruistic values.
Person-based design and evaluation of MIA, a digital medical interview assistant for radiology
Field: AI

Introduction: Radiologists frequently lack direct patient contact due to time constraints. Digital medical interview assistants aim to facilitate the collection of health information. In this paper, we propose leveraging conversational agents to realize a medical interview assistant to facilitate medical history taking, while at the same time offering patients the opportunity to ask questions on the examination.Methods: MIA, the digital medical interview assistant, was developed using a person-based design approach, involving patient opinions and expert knowledge during the design and development with a specific use case in collecting information before a mammography examination. MIA consists of two modules: the interview module and the question answering module (Q&A). To ensure interoperability with clinical information systems, we use HL7 FHIR to store and exchange the results collected by MIA during the patient interaction. The system was evaluated according to an existing evaluation framework that covers a broad range of aspects related to the technical quality of a conversational agent including usability, but also accessibility and security.Results: Thirty-six patients recruited from two Swiss hospitals (Lindenhof group and Inselspital, Bern) and two patient organizations conducted the usability test. MIA was favorably received by the participants, who particularly noted the clarity of communication. However, there is room for improvement in the perceived quality of the conversation, the information provided, and the protection of privacy. The Q&A module achieved a precision of 0.51, a recall of 0.87 and an F-Score of 0.64 based on 114 questions asked by the participants. Security and accessibility also require improvements.Conclusion: The applied person-based process described in this paper can provide best practices for future development of medical interview assistants. The application of a standardized evaluation framework helped in saving time and ensures comparability of results.
Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania
Field: AI

Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of the residents depending on it for survival. Maize is the country’s dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.
Fibrosis to carcinogenesis: unveiling the causal dynamics between pulmonary fibrosis and lung cancer
Field: ONCOLOGY

Background: Previous clinical evidence has shown a correlation between pulmonary fibrosis (PF) and lung cancer (LC), but their causal relationship remains unknown.Methods: This study utilized a bidirectional two-sample Mendelian randomization (MR) approach to explore the causal relationship between PF and LC, including its subtypes. Genetic data were obtained from the IEU and FinnGen Genome-Wide Association Studies (GWAS). SNPs with genome-wide significance were selected, and analyses were conducted using Inverse-Variance Weighted (IVW), MR Egger, and Weighted Median methods. The IVW results for various subtypes of lung cancer and PF were used in a meta-analysis to investigate the overall causal effect between PF and lung cancer. Sensitivity analysis was used for both MR and meta-analysis to investigate the robustness of the results.Results: The bidirectional MR analysis showed no significant causal relationship between PF and overall, LC or its subtypes, except for SCLC, which had a significant positive association (OR = 1.29, 95% CI 1.07-1.57, p = 0.009). The meta-analysis results indicated no overall causal effect (OR = 1.067, 95% CI: 0.952-1.195, P = 0.265, I² = 57.3%). In the reverse MR analysis, NSCLC and LUSC showed significant associations with PF (OR = 1.12, 95% CI 1.01-1.23, p = 0.028 and OR = 1.04, 95% CI 1.01-1.08, p = 0.012, respectively), while the meta-analysis results indicated no significant causal effect (OR = 1.006, 95% CI: 0.973-1.040, P = 0.734, I² = 55.9%). Sensitivity analyses indicated no evidence of horizontal pleiotropy or significant heterogeneity.Conclusion: This study suggests a potential causal relationship between PF and SCLC, as well as between NSCLC and LUSC with PF. However, the overall causal relationship between PF and LC was not statistically significant, possibly due to individual variability and other influencing factors. Further research using data from diverse populations is needed to validate these findings.
The significance of school bullying prevention program: a narrative inquiry from the perspective of a school police officer at a Youth Police Academy in Korea
Field: EDUCATION

The need for effective school bullying prevention programs is more pronounced than ever. To address school bullying, Korea has operated the Youth Police Academy (YPA) since 2014. Although the School Police Officers (SPOs) in charge at YPA can provide valuable insights into the significance of school bullying prevention programs, there has been limited research in this area. The purpose of this study is to explore the relevance of school bullying prevention programs and delineate the role of YPA in preventing school bullying, based on the professional experiences and perspectives of YPA’s SPOs. We employed narrative analysis based on interviews with SPOs. The findings revealed that while the majority of SPOs experienced career crises, they overcame these challenges and developed professional perspectives on the YPA program and anti-bullying program. SPOs perceive that school bullying prevention program should focus on “resolving relationships,” “collaborative care,” and “teaching coping behaviors.” Accordingly, YPA can function as a “place of reconciliation,” “place helping students understand others’ perspectives through experiential and case-based educational approaches,” “hub for school bullying prevention education grounded in collaboration with relevant institutions and local experts,” “provider of coping information,” and an “active protector of victims.”
Task-irrelevant visual distractions and mindful self-regulated learning in a low-stakes computer-based assessment
Field: EDUCATION

Introduction: There is a growing concern about the threat of distractions in online learning environments. It has been suggested that mindfulness may attenuate the effects of distraction. The extent to which this translates to academic performance is under investigation. We aimed to investigate the relationship between task-irrelevant visual distraction, time pressure, and mindful self-regulated learning in the context of a low-stake computer-based assessment.Methods: The study sampled 712 registered users of Prolific.co who were prescreened, current undergraduate university students. After data quality screening, 609 were retained for analyses. A 2 × 2 between-subjects design was used. Participants were randomly assigned to the following groups: (1) a control condition, (2) a distract condition, (3) a time pressure condition, or (4) a distract and time pressure condition. All participants completed reading comprehension questions, demographic questions, and the Mindful Self-Regulated Learning Scale.Results: Presenting a visual distraction increased self-reported distraction and having a clock present increased self-reported time pressure. The distraction did not have a statistically significant effect on test performance. Mindfulness was negatively correlated with test performance, self-reported distraction, and self-reported time pressure.Discussion: Continuous task-irrelevant visual distractions may not be distracting enough to influence low-stakes testing performance, but they do influence self-perceptions.
Sustainability in undergraduate course curricula at Andalusian (Spain) universities: a critical analysis
Field: EDUCATION

Education is one of the main tools used to implement sustainable development goals (SDGs). Higher education institutions (HEIs) have a major social responsibility regarding sustainability given the relevance and impact of their educational work and the creation of knowledge through their research. Sustainability is promoted and linked to values, teaching-learning methodologies, and studying of global–local problems. Within this framework, the objective of our research is to determine the presence and means by which sustainability appears in the course curricula of university bachelor’s degrees of the public universities of Andalusia (Spain). The study used quantitative methodology. As in other studies, major deficiencies have been revealed in terms of the inclusion of sustainability in the universities, determining a limited presence of local problems to address sustainability. Thus, Andalusian universities distance themselves from the society and community in which they exist. This may also limit student knowledge of sustainability issues in which they could potentially be relevant participants.
Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges
Field: AI

Physics-informed neural networks (PINNs) represent a significant advancement at the intersection of machine learning and physical sciences, offering a powerful framework for solving complex problems governed by physical laws. This survey provides a comprehensive review of the current state of research on PINNs, highlighting their unique methodologies, applications, challenges, and future directions. We begin by introducing the fundamental concepts underlying neural networks and the motivation for integrating physics-based constraints. We then explore various PINN architectures and techniques for incorporating physical laws into neural network training, including approaches to solving partial differential equations (PDEs) and ordinary differential equations (ODEs). Additionally, we discuss the primary challenges faced in developing and applying PINNs, such as computational complexity, data scarcity, and the integration of complex physical laws. Finally, we identify promising future research directions. Overall, this survey seeks to provide a foundational understanding of PINNs within this rapidly evolving field.
Exploring the assessment of musical praxis through ICT in the academic context
Field: EDUCATION

The evaluation of musical praxis involves a nuanced assessment of performer competencies within the intricate dynamics of musical elements, often hindered by subjective influences and the transient nature of performance. This study investigates the integration of Information and Communication Technology (ICT) tools to enhance instrumental praxis evaluation, focusing on French horn applicants to the University of Valencia Philharmonic Orchestra (OFUV). Employing a descriptive observational methodology and utilizing the MAXQDA application for analysis, the study examines key aspects of interpretation through individual recordings. Results demonstrate that ICT applications facilitate transparent and precise evaluation of performance aspects, underscoring the importance of incorporating these tools in performative education. In this regard, 89% of participants found the feedback to be very useful. Leveraging audio-video recordings offers a promising avenue for comprehensive analysis, providing clearer feedback and advocating for their integration by educational authorities and instructors to foster objective evaluation and enhance musical pedagogy.