mdpi-ai/citelens huggingface dataset:
Computer Vision for Safety Management in the Steel Industry
Field: AI

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.
Distance education challenges: insight from a nationwide teacher-centric study post- COVID-19 for informed advancements
Field: EDUCATION

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.
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift
Field: AI

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.
Understanding the public stigma of mental illness: a mixed-methods, multi-level, exploratory triangulation study
Field: PSYCHOLOGY

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.
Enhancing Technology-Focused Entrepreneurship in Higher Education Institutions Ecosystem: Implementing Innovation Models in International Projects
Field: EDUCATION

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.
Well-Being and Dispositional Hope in a Sample of Portuguese Citizens: The Mediating Role of Mental Health
Field: PSYCHOLOGY

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.
Dynamic Programming-Based White Box Adversarial Attack for Deep Neural Networks
Field: AI

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.
A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method
Field: AI

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 $${K}^{2}$$
Unveiling mode effects in grade 1 vocabulary assessment: the intriguing influence of test mode
Field: EDUCATION

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.
Exercise motivation, physical exercise, and mental health among college students: examining the predictive power of five different types of exercise motivation
Field: PSYCHOLOGY

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.
Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey
Field: AI

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.
Evaluating the before operational stress program: comparing in-person and virtual delivery
Field: PSYCHOLOGY

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.
The Design and Impact of Interactive Online Modules for Dental Faculty Calibration
Field: EDUCATION

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.