mdpi-ai/citelens huggingface dataset:
Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method
Field: AI

This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for addressing missing data in mine ventilation parameters. Using historical ventilation system data from Shangwan Coal Mine, scenarios with different missing rates (5, 10, and 15%) and iteration numbers (30 and 50) were simulated to validate the accuracy and effectiveness of the approach. The results demonstrate that as the missing rate increased from 5 to 15%, the Mean Squared Error (MSE) rose from 0.0445 to 0.3254, while the Explained Variance decreased from 0.988309 to 0.943267. Additionally, the Mean Absolute Error (MAE) increased by 0.29. Iteration experiments on the “frictional resistance per 100 meters” attribute showed convergence of MSE and MAE after six iterations. Overall, the XGBoost-MICE method exhibited high imputation accuracy and stable convergence across various missing data scenarios, providing robust technical support for optimizing intelligent mine ventilation systems.
Detection and classification of ChatGPT-generated content using deep transformer models
Field: AI

Introduction: The rapid advancement of AI, particularly artificial neural networks, has led to revolutionary breakthroughs and applications, such as text-generating tools and chatbots. However, this potent technology also introduces potential misuse and societal implications, including privacy violations, misinformation, and challenges to integrity and originality in academia. Several studies have attempted to distinguish and classify AI-generated textual content from human-authored work, but their performance remains questionable, particularly for AI models utilizing large language models like ChatGPT.Methods: To address this issue, we compiled a dataset consisting of both human-written and AI-generated (ChatGPT) content. This dataset was then used to train and evaluate a range of machine learning and deep learning models under various training conditions. We assessed the efficacy of different models in detecting and classifying AI-generated content, with a particular focus on transformer-based architectures.Results: Experimental results demonstrate that the proposed RoBERTa-based custom deep learning model achieved an F1-score of 0.992 and an accuracy of 0.991, followed by DistilBERT, which yielded an F1-score of 0.988 and an accuracy of 0.988. These results indicate exceptional performance in detecting and classifying AI-generated content.Discussion: Our findings establish a robust baseline for the detection and classification of AI-generated textual content. This work marks a significant step toward mitigating the potential misuse of AI-powered text generation tools by providing a reliable approach for distinguishing between human and AI-generated text. Future research could explore the generalizability of these models across different AI-generated content sources and address evolving challenges in AI text detection.
Family resilience in patients with gynecological malignant tumors after radical hysterectomy: based on the Walsh family resilience framework
Field: ONCOLOGY

Aim: Explore and analyze the family resilience of patients with gynecological malignancies after radical hysterectomy, providing a theoretical basis for the formulation of future intervention measures.Methods: Using a phenomenological descriptive qualitative research method, 17 patients who underwent radical surgery for gynecological malignancies were selected for semi-structured interviews. Data analysis and theme extraction were conducted using Colaizzi data analysis method and NVivo V.12.Results: Three themes and eight sub-themes were extracted: family belief system (confront surgical challenges head-on, attribute positive significance to adversity, stay positive), family organization model (timely adjustment of family roles, family cohesion, get support and help from others), and family communication and problem solving skills (communicate to eliminate negative emotions, collaborative problem solving).Conclusion: This study indicates that the family belief system is the solid foundation of family resilience, the family organizational pattern serves as a buffer when the family faces adversity, and positive communication and collaborative problem solving create a positive feedback loop that enhances family resilience. Future interventions could enhance patients’ family resilience from the perspective of family strengths.
Predicting Workplace Hazard, Stress and Burnout Among Public Health Inspectors: An AI-Driven Analysis in the Context of Climate Change
Field: PSYCHOLOGY

The increasing severity of climate-related workplace hazards challenges occupational health and safety, particularly for Public Health and Safety Inspectors. Exposure to extreme temperatures, air pollution, and high-risk environments heightens immediate physical threats and long-term burnout. This study employs Artificial Intelligence (AI)-driven predictive analytics and secondary data analysis to assess hazards and forecast burnout risks. Machine learning models, including eXtreme Gradient Boosting (XGBoost 3.0), Random Forest, Autoencoders, and Long Short-Term Memory (LSTMs), achieved 85–90% accuracy in hazard prediction, reducing workplace incidents by 35% over six months. Burnout risk analysis identified key predictors: physical hazard exposure (β = 0.76, p < 0.01), extended work hours (>10 h/day, +40% risk), and inadequate training (β = 0.68, p < 0.05). Adaptive workload scheduling and fatigue monitoring reduced burnout prevalence by 28%. Real-time environmental data improved hazard detection, while Natural Language Processing (NLP)-based text mining identified stress-related indicators in worker reports. The results demonstrate AI’s effectiveness in workplace safety, predicting, classifying, and mitigating risks. Reinforcement learning-based adaptive monitoring optimizes workforce well-being. Expanding predictive-driven occupational health frameworks to broader industries could enhance safety protocols, ensuring proactive risk mitigation. Future applications include integrating biometric wearables and real-time physiological monitoring to improve predictive accuracy and strengthen occupational resilience.
The relationship of fear of pain, pain anxiety, and fear-avoidance beliefs with perceived stress in surgical patients with postoperative kinesiophobia
Field: PSYCHOLOGY

Kinesiophobia is one of the most prevalent postoperative problems with negative effects on patient mobility. Fear of pain (FOP), pain anxiety (PA), and fear of avoidance beliefs (FABs) are influential factors on postoperative mobility and may be affected by perceived stress (PS). The present study examined whether perceived stress serves to mediate the relationship between fear of pain, pain anxiety, and fear of avoidance beliefs with kinesiophobia (fear of movement) in postoperative patients. The study was conducted in the neurosurgery, general surgery, and orthopedic wards of a hospital in Amol, Iran. A total of 330 patients (178 men and 152 women), aged 18 to 74 years, who had undergone various surgical procedures, were included. Participants were recruited using a consecutive sampling technique over a defined period to account for the staggered timing of surgeries and ensure broader representation. All patients were assessed six hours post-surgery using validated instruments, including the Tampa Scale for Kinesiophobia, Pain Anxiety Symptoms Scale, Fear of Pain Questionnaire, Fear-Avoidance Beliefs Questionnaire, and Perceived Stress Scale. The majority of the sample were men (53.9%), married (80%), with a mean age of 44.38 (SD = 13.49) years. Of the participants, 119 (36.1%) underwent orthopedic surgery, 139 (42.1%) underwent abdominal surgery, and 72 (21.8%) underwent surgery for discopathy. The path analysis revealed that kinesiophobia exhibited a significant relationship with FABs (β = 0.206; p < 0.001; 95% CI: 0.009 to 0.017) and PA (β = 0.474; p < 0.001; 95% CI: 0.021 to 0.031), while no significant relationship was found with FOP (β = 0.072; p = 0.408; 95% CI: -0.011 to 0.011). Also, the findings indicated that PS as mediator had a significant relationship with FABs (ß = 0.191; P < 0.001; 95% CI: 0.009 to 0.017), PA (ß = 0.393; P < 0.001;95% CI: 0.021 to 0.031), and kinesiophobia (ß = 0.812; P < 0.001; 95% CI: 0.021 to 0.031. The study found that pain anxiety and fear-avoidance beliefs are key factors contributing to kinesiophobia after surgery. Addressing these fears is important for improving postoperative mobility. Perceived stress mediated the relationship between these factors and kinesiophobia. Managing stress may be a helpful intervention to improve outcomes for postoperative patients. Healthcare providers should assess and address psychological factors like pain anxiety and fear-avoidance beliefs to promote better recovery and mobility in patients after surgery.
Processes, challenges, and teacher roles in developing and implementing collaborative STEM curricula: case studies of two Taiwanese schools
Field: EDUCATION

A key research gap in current STEM education lies in the need for a more in-depth exploration of STEM teachers as curriculum designers, particularly in how they collaborate in designing STEM curricula and their roles within that process. This study selected two high-performing STEM teaching teams for investigation and employed a naturalistic approach along with a case study methodology to examine how STEM teachers collaborate to develop and implement STEM curricula in real teaching contexts. After 8 months of data collection and analysis, the main findings are as follows: (1) there are discrepancies between the tasks emphasized at each stage of the collaborative STEM curriculum model by high-performing STEM teaching teams and those outlined in theoretical models. In addition, the resources and drivers valued by these teams are not accounted for in the theoretical models. (2) Both high-performing STEM teaching teams faced several challenges during collaborative curriculum design and implementation, including difficulties with scheduling, limited time for lesson preparation, challenges in assessing higher-order thinking, and integrating team members. The main challenge faced by both schools was the absence of common meeting times for interdisciplinary collaboration. This highlights the need for strategic scheduling and institutional support to enable teacher collaboration in STEM education. (3) The three main roles within STEM teaching teams are leaders, core teachers, and participating teachers. However, in practice, core teachers and participating teachers often do not fulfill the responsibilities they are expected to undertake. This study also discusses potential research limitations and offers relevant suggestions for future research. The study also identified a significant discrepancy between theory and practice. While the PADPIE model outlines a structured six-stage design stages, schools frequently skip or merge stages due to time and resource limitations. An inconsistency was noted in the enactment of teacher roles. While formal assignments such as leaders, core teachers, and participating teachers were established, many core and participating teachers often lacked clarity and initiative in their responsibilities. These findings highlight the need to bridge the gap between theoretical models and real-world implementation.
Evaluating the Efficacy of Deep Learning Models for Identifying Manipulated Medical Fundus Images
Field: AI

(1) Background: The misuse of transformation technology using medical images is a critical problem that can endanger patients’ lives, and detecting manipulation via a deep learning model is essential to address issues of manipulated medical images that may arise in the healthcare field. (2) Methods: The dataset was divided into a real fundus dataset and a manipulated dataset. The fundus image manipulation detection model uses a deep learning model based on a Convolution Neural Network (CNN) structure that applies a concatenate operation for fast computation speed and reduced loss of input image weights. (3) Results: For real data, the model achieved an average sensitivity of 0.98, precision of 1.00, F1-score of 0.99, and AUC of 0.988. For manipulated data, the model recorded sensitivity of 1.00, precision of 0.84, F1-score of 0.92, and AUC of 0.988. Comparatively, five ophthalmologists achieved lower average scores on manipulated data: sensitivity of 0.71, precision of 0.61, F1-score of 0.65, and AUC of 0.822. (4) Conclusions: This study presents the possibility of addressing and preventing problems caused by manipulated medical images in the healthcare field. The proposed approach for detecting manipulated fundus images through a deep learning model demonstrates higher performance than that of ophthalmologists, making it an effective method.
Impact of microlearning on developing soft skills of university students across disciplines
Field: PSYCHOLOGY

Introduction: This study explores the effectiveness of microlearning in developing key soft skills among university students across four academic disciplines: humanities and arts (HA), business studies (BS), medical sciences (MS), and technical and engineering (TE). Addressing the disconnect between academic training and industry expectations, the research investigates how microlearning interventions influence the development of teamwork skills (TWS), leadership skills (LS), communication skills (CS), time management skills (TMS), and emotional intelligence (EI). The study also aims to identify which disciplines benefit most from microlearning for each specific skill.Methods: A total of 384 Chinese university students participated in this study, with a questionnaire recovery rate of 93.23% and near-equal representation from each discipline. Participants completed pre- and post-intervention surveys following tailored microlearning modules. Statistical analyses—including paired sample t-tests, independent sample t-tests, and effect size calculations—were employed to test five hypotheses related to soft skill development across disciplines.Results: Findings indicate that leadership-focused microlearning modules significantly benefited TE and MS students, while EI training was particularly effective for BS students. Notable improvements in CS and TMS were observed among BS and TE students, aligning with skills demanded in corporate project management. Overall, microlearning interventions produced measurable enhancements in specific soft skills, with variation across academic disciplines.Discussion: The results suggest that integrating structured, discipline-specific microlearning into university curricula can effectively bridge academic-industry skill gaps. Faculty are encouraged to adopt scenario-based microlearning strategies to enhance student engagement. Higher education institutions should prioritize microlearning experience in student development and recruitment. Additionally, EdTech providers are urged to develop AI-powered interactive platforms to personalize learning, while students should proactively engage in targeted microlearning to improve academic and career outcomes.
If not police, then who? Building a new workforce for community behavioral health crisis response
Field: PSYCHOLOGY

Introduction: Communities across the United States and elsewhere are working to implement alternatives to law enforcement as primary responders to behavioral health crises. These efforts can only be successful if there is a skilled workforce prepared to take on this role. We argue that this workforce must be developed, and propose a new, credentialed Community Behavioral Health Crisis Responder (CBHCR) role.Methods: Guided by a 13-member advisory board with expertise across behavioral health, crisis services, and law enforcement, we conducted a literature review, key informant interviews, and focus groups to identify the foundational values, competencies, and skills for this proposed role.Results: Interview and focus group participants discussed desired characteristics of CBHCRs and emphasized values such as cultural humility, a nonjudgmental approach, and the importance of lived experience broadly defined. Competencies and skills included engagement and communication strategies that enhance safety and trust, suicide prevention, conflict resolution, and situational awareness. Participants highlighted the need to train CBHCRs to provide compassionate, trauma-informed crisis intervention, de-escalation, support, and connection to needed resources. In conjunction with our advisory board and external experts, we used the findings to iteratively refine the values, competencies, and skills of CBHCRs.Discussion: We discuss the next steps in creating this new, skilled and credentialed crisis response workforce.
A Hybrid and Modular Integration Concept for Anomaly Detection in Industrial Control Systems
Field: AI

Effective anomaly detection is essential for realizing modern and secure industrial control systems. However, the direct integration of anomaly detection within such a system is complex due to the wide variety of hardware used, different communication protocols, and given industrial requirements. Many components of an industrial control system allow direct integration, while others are designed as closed systems or do not have the required performance. At the same time, the effective usage of available resources and the sustainable use of energy are more important than ever for modern industry. Therefore, in this paper, we present a modular and hybrid concept that enables the integration of efficient and effective anomaly detection while optimising the use of available resources under consideration of industrial requirements. Because of the modular and hybrid properties, many functionalities can be outsourced to the respective devices, and at the same time, additional hardware can be integrated where required. The resulting flexibility allows the seamless integration of complete anomaly detection into existing and legacy systems without the need for expensive centralised or cloud-based solutions. Through a detailed evaluation within an industrial unit, we demonstrate the performance and versatility of our concept.
Clinical pathological characteristics and prognostic analysis of renal primitive neuroectodermal tumours: a multicentre retrospective study of 16 cases in Northwest China
Field: ONCOLOGY

Objective Renal primitive neuroectodermal tumours (rPNETs) are extremely rare and highly aggressive malignancy, posing significant diagnostic and therapeutic challenges. This study aims to describe the clinicopathological characteristics, treatment strategies, and survival outcomes of 16 cases of rPNET from multiple centers in Northwest China, and to explore potential prognostic factors. Methods A multicenter retrospective study was conducted, including 16 patients diagnosed with rPNET across five hospitals in Northwest China. Immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) were employed to assess the expression of molecular markers, including P53, BCL-2, Ki-67, and EWSR1 gene rearrangements. Survival analysis was performed using the Kaplan-Meier method, and prognostic factors were evaluated using univariate and multivariate Cox regression models. Results The median age of the patients was 39 years, with a median Ki-67 proliferation index of 50%. P53 mutations were detected in 87.0% of cases, and BCL-2 positive expression was observed in 56.25% of cases. The median overall survival (OS) was 14 months. Univariate analysis revealed that age, tumor stage, BCL-2 expression, and Ki-67 index were significantly associated with OS. Multivariate analysis identified high Ki-67 expression (HR = 1.100, 95% CI: 1.030–1.174, p = 0.004) and negative BCL-2 expression (HR = 0.151, 95% CI: 0.026–0.888, p = 0.037) as independent risk factors for poor prognosis. Kaplan-Meier survival curves demonstrated that the median OS was significantly shorter in patients with high Ki-67 expression (12 months) compared to those with low Ki-67 expression (20 months) (Log-rank test, P < 0.01). Similarly, the median OS was significantly shorter in the BCL-2 negative group (10 months) compared to the BCL-2 positive group (24 months) (Log-rank test, P < 0.05). Conclusion The absence of rosette structures does not exclude the diagnosis of rPNET. BCL-2 and Ki-67 expression are significant prognostic factors, with high Ki-67 expression and negative BCL-2 expression associated with worse outcomes. These findings highlight the importance of molecular markers in risk stratification and treatment planning for rPNET.
Copy number gain of MET gene with low level in a metastatic lung adenocarcinoma patient represents response to salvage treatment with savolitinib and osimertinib: a case report
Field: ONCOLOGY

Background: Mesenchymal–epithelial transition (MET) amplification is one of the molecular mechanisms of abnormal MET oncogenic signaling in non-small cell lung cancer (NSCLC), significantly contributing to tumor cell survival, proliferation, metastasis, and drug resistance. The results of the TATTON trial showed that the combination of savolitinib and osimertinib can prolong the survival of patients with advanced EGFR-TKI-resistant NSCLC and high-level acquired MET amplification.Case presentation: We present a case of an NSCLC patient who exhibited acquired MET amplification with a gene copy number (GCN) of 3 following resistance to EGFR-TKI. The patient achieved a substantial response to salvage therapy with savolitinib and osimertinib, resulting in a 7-month progression-free survival (PFS).Conclusions: We considered that a regimen of savolitinib + osimertinib combination sometimes may still be potentially beneficial for NSCLC patients with low-GCN-level MET amplification. However, it needs further confirmation in a larger cohort.
Radiomics model based on dual-energy CT venous phase parameters to predict Ki-67 levels in gastrointestinal stromal tumors
Field: ONCOLOGY

Objective: To develop and validate a radiomics model based on the features of the Dual-Energy CT (DECT) venous phase iodine density maps and effective atomic number maps to predict Ki-67 expression levels in gastrointestinal stromal tumors (GISTs).Methods: A total of 91 patients with GIST were retrospectively analyzed, including 69 patients with low Ki-67 expression (≤5%) and 22 patients with high Ki-67 expression (>5%). Four clinical features (gender, age, maximum tumor diameter, and tumor location) were extracted to construct a clinical model. The venous phase enhanced CT iodine density maps and effective atomic number maps of DSCT were used to build radiomics models. Logistic regression was used to combine radiomics features with clinical features to build a combined model. Finally, the optimal model’s discrimination, calibration, and clinical decision curve were validated using the Bootstrap method.Results: The combined model was identified as the best model, with high predictive performance. The model’s discrimination had an AUC of 0.982 (95% CI, 0.9603-1). The calibration test showed a Hosmer-Lemeshow test P-value of 0.99. The clinical decision curve demonstrated a probability threshold range of 15% to 98%, with a high net benefit.Conclusion: The nomogram model combining clinical features and radiomics (iodine density map radscore + effective atomic number map radscore) has the highest accuracy for preoperative prediction of Ki-67 expression in GISTs.