Search Results (76)

In the healthcare sector, burnout has become a critical concern due to the combination of high job demands and sustained emotional strain. Burnout is closely linked to systemic and organizational pressures, and psychosocial risks are widely recognized as central determinants of burnout. Within this multidimensional framework, Work Values are understood as an integral component of psychosocial risks, shaping how professionals interpret and respond to these pressures. The present study aims to analyze whether emotional intelligence moderates the relationship between psychosocial risk factors, namely work values conflict and burnout, among healthcare professionals. A cross-sectional online survey, based on a snowball sample with 205 healthcare professionals, was performed. Measurement instruments included the Burnout Assessment Tool (BAT-23), used to assess burnout dimensions; the Health and Work Survey (ERPS_INSAT), used to evaluate psychosocial risk factors; and the Wong and Law Emotional Intelligence Scale (WLEIS-P), used to assess emotional intelligence. A moderation analysis using the PROCESS macro (model 1) was conducted to examine whether emotional intelligence moderates the relationship between psychosocial risk, work values factor, and burnout among healthcare professionals. The results show that the psychosocial risk–work values dimension was a significant positive associated factor of burnout (total scale: B = 0.27, p < 0.001; Exhaustion: B = 0.33, p < 0.001; Mental distance: B = 0.32, p < 0.001; Cognitive Impairment: B = 0.14, p < 0.001; Emotional Impairment: B = 0.30, p < 0.001), indicating that higher perceived risk was associated with higher burnout symptoms. Emotional intelligence did not significantly predict burnout on its own (total scale: B = 0.07, p > 0.05; Exhaustion: B = 0.09, p > 0.05; Mental Distance: B = 0.11, p > 0.05; Cognitive Impairment: B = 0.11, p > 0.05; Emotional Impairment: B = −0.04, p > 0.05). The interaction term (psychosocial risk = work values × emotional intelligence) was not significant, suggesting that no significant moderating effect was detected in this sample for emotional intelligence in the relationship between work values and burnout. These findings highlight the central role of psychosocial risk factors in the development of burnout among healthcare professionals, and emotional intelligence does not seem to have a significant moderating effect against burnout in this study. Such findings highlight the crucial role that organizational-level interventions at the workplace play in resolving conflicts between work values and lower burnout and improved worker wellbeing. Full article

Inter-turn insulation failure is the primary cause of dry-type air-core reactor burnout, yet early detection remains challenging due to weak power-frequency fault signatures. This paper proposes an integrated diagnostic framework combining impulse oscillation testing, electromagnetic simulation, and a physics-informed graph neural network. A scaled-down four-layer parallel reactor model and an impulse oscillation platform are developed to extract dynamic equivalent inductance and resistance as sensitive fault indicators. Validated finite element simulations reveal that inter-layer insulation near high-voltage terminals endures the highest electric field stress, with local field strength increasing nearly eightfold under short-circuit faults. For fault localization, a Spatio-Temporal Physics-Informed Graph Neural Network (ST-PIGNN) is constructed, representing winding topology as a heterogeneous graph and embedding electromagnetic transient equations as physical constraints. On a test set of 120 samples, the proposed method achieves 94.17% fault layer classification accuracy and 6.84% axial localization mean absolute error under low-noise conditions, and maintains 85.83% accuracy with 8.12% error under strong-noise interference. The proposed method is currently at the proof-of-concept stage, and further validation on full-scale reactors is required before field deployment. Full article

Underground duct cables are widely used in urban power distribution systems, but their enclosed installation environment makes defect inspection difficult, labor-intensive, and potentially hazardous. Infrared thermography can capture abnormal temperature distributions caused by insulation degradation, conductor damage, sheath failure, or severe structural defects, while robot-based inspection provides a promising solution for confined duct environments. However, thermographic fault detection for underground small-diameter duct cables remains insufficiently studied, and practical deployment requires lightweight models suitable for embedded edge devices. In this study, an improved YOLO26-based thermographic fault detection framework is proposed for underground duct cable inspection. A Cable-Thermo dataset is constructed using an ANSYS 2025 R2-based thermoelectric coupling simulation, covering four defect categories: hollow-type damage, conductor burnout, sheath damage, and severe damage. To balance detection accuracy and deployment efficiency, two model variants are developed. YOLO26-Thermo-E retains the original detection scales and integrates CDA and SimSPPF modules for accuracy-prioritized diagnosis. YOLO26-Thermo-H further removes the small-scale detection branch as a deployment-oriented design choice, based on the scale distribution observed in the simulation dataset, where most fault-induced thermal anomalies appear as spatially continuous medium- or large-scale regions. This design assumption still requires further validation using real duct thermographic data. Experiments show that YOLO26-Thermo-E achieves the highest mAP50 of 99.20%. YOLO26-Thermo-H maintains a mAP50 of 99.00% while reducing GFLOPs by 34.3% and parameters by 16.2% compared with YOLO26. On an NVIDIA Jetson Orin NX, YOLO26-Thermo-H reaches 34 FPS under FP16 inference and 45 FPS under INT8 inference. These results demonstrate the feasibility of the proposed framework under controlled simulation conditions and its potential for edge deployment. The limitations of the simulation-based dataset are also discussed, and future work will focus on real-scene data collection and simulation-to-real generalization. Full article

Background: Burnout is a relevant occupational health concern in Emergency Medical Services (EMSs), with potential implications for workforce well-being, occupational health, and the sustainability of prehospital care. Although burnout has been widely studied among healthcare professionals, evidence concerning Emergency Medical Technician (EMT) students remains limited. This exploratory study aimed to estimate high burnout prevalence among EMT students and practising EMT professionals in Madrid, Spain, describe burnout dimensions in both groups, and examine sociodemographic correlates of high burnout status. Methods: A cross-sectional comparative study was conducted between March and June 2024 using a convenience sample of 85 participants: 43 EMT students and 42 practising EMT professionals. Burnout was assessed using validated Spanish versions of the Maslach Burnout Inventory: the MBI-SS for students and the MBI-HSS for professionals. Because these instruments are population-specific and rely on different norms and thresholds, between-group comparisons of raw scores were interpreted as exploratory. Descriptive analyses, between-group comparisons with effect sizes, correlation analyses, and an exploratory binary logistic regression model were performed. Results: High burnout was identified in 22 EMT students (51.2%) and 23 practising EMT professionals (54.8%), with no statistically significant between-group difference detected (p = 0.73; Cramer’s V = 0.04). Between-group comparisons of burnout dimensions showed small effect sizes for Emotional Exhaustion (Cohen’s d = 0.17), Depersonalisation (Cohen’s d = 0.24), and Personal Accomplishment (Cohen’s d = −0.26). Age was positively associated with Emotional Exhaustion (r = 0.29, p = 0.008) and Depersonalisation (r = 0.24, p = 0.028), and negatively associated with Personal Accomplishment (r = −0.26, p = 0.019). In the exploratory adjusted logistic regression model, age was associated with high burnout status (OR = 1.05; 95% CI 1.01–1.10; p = 0.017), whereas group and sex were not significant correlates. Conclusions: High burnout levels were observed in both EMT students and practising EMT professionals in this regional exploratory sample. However, the findings should be interpreted cautiously due to the cross-sectional design, convenience sampling, modest sample size, limited statistical power, and use of population-specific burnout instruments. These results suggest that burnout-related distress may be relevant across the EMT training-to-practice pathway and support the need for larger longitudinal and multicentre studies incorporating occupational, educational, and organisational variables. Full article

Background: Healthcare professionals experience continuous biological and psychosocial stressors that may disturb oral and systemic homeostasis. Alterations in salivary secretion, mucosal immunity, and microbiome composition reflect adaptive cellular responses to chronic occupational stress. Understanding these mechanisms may provide a biological framework for resilience and wellbeing in everyday clinical practice. Objective: To narratively review the evidence linking oral cellular and molecular mechanisms—salivary biomarkers, epithelial and immune cell activity, and microbiome dynamics—with stress, fatigue, burnout, and wellbeing outcomes among healthcare professionals. Methods: This narrative review employed a PRISMA-guided literature search of PubMed, Scopus, Web of Science, and Cochrane Oral Health to enhance transparency and coverage across databases. Given the heterogeneity of study designs and outcomes, data were synthesized thematically without quantitative pooling or formal meta-analysis. Methodological strength was evaluated qualitatively, focusing on biomarker validity, sampling conditions, and conceptual relevance. Eligible designs included observational, experimental, and interventional studies. Results: Evidence from 99 studies suggests that chronic occupational stress elevates salivary cortisol, oxidative stress markers, and pro-inflammatory cytokines (IL-6, TNF-α), while reducing protective salivary immunoglobulin A and microbiome diversity. Balanced oral immune and microbial profiles were associated with better psychological adaptation and lower fatigue indices. Conclusions: Oral cellular homeostasis offers a promising window into the biological underpinnings of occupational stress and resilience in healthcare professionals. Systematic integration of salivary and mucosal biomarkers into workplace wellbeing programs could enhance early detection of dysregulated stress physiology. Future interdisciplinary research should bridge oral biology, occupational medicine, and mental health to strengthen sustainable wellbeing strategies across the health workforce. Full article

This paper conceptualises burnout in Project-Based Organisations (PBOs) as a systemic emergent property arising from the non-linear interaction between structural demands and human capital. Utilising a System Dynamics (SD) methodology, the study constructs a Causal Loop Diagram (CLD) to visualise the feedback architecture governing the burnout cycle. The analysis identifies the dynamic tension between the Reinforcing Loop of exhaustion (R1) and the Balancing Loop of adaptation (B1). A key theoretical contribution is the positioning of the Project Manager’s Emotional Intelligence (EI) not merely as a soft skill but as a systemic control lever (B2) capable of reducing information delays and shifting the system from reactive to proactive homeostasis. Crucially, the study operationalises these conceptual findings into a Burnout Risk Management Framework (BRMF), accompanied by a practical diagnostic dashboard. This tool offers managers a set of leading and lagging indicators for early detection, bridging the gap between theoretical plausibility and applied risk management in high-entropy project environments. Full article

Stress is a pervasive global health concern that adversely contributes to morbidity and reduced productivity, yet it often remains unquantified due to its subjective and variant presentation. Although artificial intelligence offers an encouraging path toward automated monitoring of mental states, current state-of-the-art approaches are challenged by the reliance on single-source data, sparsity of labeled samples, and significant class imbalance. This paper proposes NeuroStrainSense, a novel deep multimodal stress detection model that integrates three complementary datasets—WESAD, SWELL-KW, and TILES—through a Transformer-based feature fusion architecture combined with a Variational Autoencoder for generative data augmentation. The Transformer architecture employs four encoder layers with eight multi-head attention heads and a hidden dimension of 512 to capture complex inter-modal dependencies across physiological, audio, and behavioral modalities. Our experiments demonstrate that NeuroStrainSense achieves a state-of-the-art performance with accuracies of 87.1%, 88.5%, and 89.8% on the respective datasets, with F1-scores exceeding 0.85 and AUCs greater than 0.89, representing improvements of 2.6–6.6 percentage points over existing baselines. We propose a robust evaluation framework that quantifies discrimination among stress types through clustering validity metrics, achieving a Silhouette Score of 0.75 and Intraclass Correlation Coefficient of 0.76. Comprehensive ablation experiments confirm the utility of each modality and the VAE augmentation module, with physiological features contributing most significantly (average performance decrease of 5.8% when removed), followed by audio (2.8%) and behavioral features (2.1%). Statistical validation confirms all findings at the p < 0.01 significance level. Beyond binary classification, the model identifies five clinically relevant stress profiles—Cognitive Overload, Burnout, Acute Stress, Psychosomatic, and Low-Grade Chronic—with an expert concordance of Cohen’s κ = 0.71 (p < 0.001), demonstrating the strong ecological validity for personalized well-being and occupational health applications. External validation on the MIT Reality Mining dataset confirms the generalizability with minimal performance degradation (accuracy: 0.785, F1-score: 0.752, AUC: 0.849). This work underlines the potential of integrated multimodal learning and demographically aware generative AI for continuous, precise, and fair stress monitoring across diverse populations and environmental contexts. Full article

Background/Objectives: Ecological momentary assessment (EMA) is a methodology that offers a real-time approach to monitoring clinician well-being, but its utility during high-intensity operational periods remains underexplored. This study examines the feasibility and performance of an EMA-based system for tracking clinical responsibilities and burnout among healthcare workers during the first year of the COVID-19 pandemic. Methods: Utilizing an intensive longitudinal design, 398 healthcare workers, including physicians, physician assistants, nurses, and trainees, completed brief EMA surveys every five days from April 2020 to March 2021. Burnout was assessed with a validated single-item measure and analyzed in relation to redeployment status and hospital caseloads. Results: The EMA approach successfully captured meaningful temporal fluctuations in burnout. Redeployment was associated with higher burnout levels (b = 0.125; p = 0.01), and rising caseloads amplified this effect (interaction b = 0.169; p = 0.001). Nurses showed the strongest caseload-related increases in burnout (b = 0.359; p < 0.001). These patterns persisted even after individuals returned to their usual roles. Conclusions: This study demonstrates that EMA is a scalable and sensitive approach for continuous burnout surveillance, capable of detecting role-specific and context-dependent stress responses in real time. EMA-based monitoring can support early identification of at-risk groups, guide staffing and redeployment decisions, and inform timely organizational interventions during crises and other periods of operational strain. Full article

Background: Academic burnout significantly impacts students’ cognitive and psychological well-being and may result in adverse behavioral changes. An effective and timely detection of burnout in the student population is crucial as it enables educational institutions to mobilize necessary support systems and implement intervention strategies. However, current survey-based detection methods are susceptible to response biases and administrative overhead. This study investigated the feasibility of detecting academic burnout symptoms using machine learning trained exclusively on university records, eliminating reliance on psychological surveys. Methods: We developed models to detect three burnout dimensions—exhaustion, cynicism, and low professional efficacy. Five machine learning algorithms (i.e., logistic regression, support vector machine, naive Bayes, decision tree, and extreme gradient boosting) were trained using features engineered from administrative data. Results: Results demonstrated considerable variability across burnout dimensions. Models achieved the highest performance for exhaustion detection, with logistic regression obtaining an F1 score of 68.4%. Cynicism detection showed moderate performance, while professional efficacy detection has the lowest performance. Conclusions: Our findings showed that automated detection using passively collected university records is feasible for identifying signs of exhaustion and cynicism. The modest performance highlights the challenges of capturing psychological constructs through administrative data alone, providing a foundation for future research in unobtrusive student burnout detection. Full article

This paper proposes the Segment Structure with Controllable Realistic Synthetic (SCRS) to address the challenge of detecting scratches on laser diode chip emitting facets, which can impair laser emitting and cause chip burnout. Scratch detection is critical for ensuring laser quality and stability, but low-contrast images hinder comprehensive dataset creation. SCRS leverages a mask-guided diffusion model to generate diverse, realistic synthetic scratch images, enabling robust training data synthesis. The generated dataset trains a novel TransCNN network, which combines vision transformer blocks and convolutional decoding for accurate scratch segmentation. Experimental results show that SCRS achieves mean Intersection over Union (mIoU) values of 74.4% for deep scratches and 75.8% for shallow scratches, demonstrating its significant potential for industrial applications. Full article

Burnout is increasingly recognized as both a psychosocial and a chronobiological disorder characterized by endocrine dysregulation and circadian disruption. It arises from chronic occupational stress and manifests through psychological, physical, and physiological symptoms. Although psychosocial determinants are well established, the biological and chronobiological mechanisms, particularly those involving cortisol and melatonin, remain less explored. This systematic review synthesizes current evidence on hormonal and circadian dysregulation in burnout and complements it with exploratory observational data from healthcare professionals. Peer-reviewed studies evaluating endocrine or circadian biomarkers in individuals with burnout were systematically reviewed. In addition, an exploratory observational analysis was carried out among 195 Romanian clinicians using an adapted Maslach Burnout Inventory. Morning salivary cortisol was measured once at 9 a.m. in a small subsample (n = 26) to provide preliminary physiological data. Because only a single time point was obtained, these values were interpreted as indicative of stress-related activation rather than circadian rhythm. Thirty-seven studies met the inclusion criteria. Across the literature, burnout was associated with altered HPA-axis activity, blunted diurnal cortisol variation, and irregular melatonin secretion related to shift work and disrupted sleep–wake cycles. Complementary exploratory data from our Romanian cohort indicated strong correlations between burnout severity, physical symptoms, and higher morning cortisol values among shift-working clinicians. These findings are preliminary and not representative of full circadian profiles. Burnout should be considered both a psychosocial and a systemic disorder influenced by endocrine and circadian dysregulation. Recognizing alterations in cortisol and melatonin as objective indicators may facilitate earlier detection and inform chronobiological interventions such as optimized scheduling, light exposure management, or melatonin therapy. The observational data presented here is preliminary and intended to generate hypotheses; future research should employ repeated cortisol sampling under controlled Zeitgeber conditions to confirm circadian associations. Full article

Background: Burnout is a widespread concern across frontline professions, with healthcare, education, and emergency services workers experiencing particularly high rates of stress and emotional exhaustion. Passive artificial intelligence (AI) technologies may provide novel means to monitor and predict burnout risk using data collected continuously and non-invasively. Objective: This review aims to synthesize recent evidence on passive AI approaches for detecting stress and burnout among frontline workers, identify key physiological and behavioral biomarkers, and highlight current limitations in implementation, validation, and generalizability. Methods: A narrative review of peer-reviewed literature was conducted across multiple databases and digital libraries, including PubMed, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science. Eligible studies applied passive AI methods to infer stress or burnout in individuals in frontline roles. Only studies using passive data (e.g., wearables, Electronic Health Record (EHR) logs) and involving healthcare, education, emergency response, or retail workers were included. Studies focusing exclusively on self-reported or active measures were excluded. Results: Recent evidence indicates that biometric data (e.g., heart rate variability, skin conductance, sleep) from wearables are most frequently used and moderately predictive of stress, with reported accuracies often ranging from 75 to 95%. Workflow interaction logs (e.g., EHR usage patterns) and communication metrics (e.g., email timing and sentiment) show promise but remain underexplored. Organizational network analysis and ambient computing remain largely conceptual in nature. Few studies have examined cross-sector or long-term data, and limited work addresses the generalizability of demographic or cultural findings. Challenges persist in data standardization, privacy, ethical oversight, and integration with clinical or operational workflows. Conclusions: Passive AI systems offer significant promise for proactive burnout detection among frontline workers. However, current studies are limited by small sample sizes, short durations, and sector-specific focus. Future work should prioritize longitudinal, multi-sector validation, address inclusivity and bias, and establish ethical frameworks to support deployment in real-world settings. Full article

The spontaneous combustion of sulfide ores (SOSC) is an extremely dangerous mining disaster that directly threatens safety production in mines and causes far-reaching negative impacts on the surrounding ecosystem. In this study, oxidation weight gain experiments, self-heating temperature and ignition temperature tests, and thermogravimetric analysis (TGA) were conducted to detect the spontaneous combustion characteristics of sulfide ores with different sulfur contents (40.29%, 34.56%, 24.81%, and 14.2%). The results show that the sulfur content significantly affects the spontaneous combustion characteristics of sulfide ores. As the sulfur content decreased, the oxidized weight gain rate decreased overall, and the self-heating temperature (135, 152.5, 162.5, and 176.9 °C) and ignition temperature (425.3, 438.6, 455.4, and >500 °C) increased. The three combustion stages of the SOSC were divided based on the TG and DTG curves: low-temperature oxidation stage, combustion decomposition stage, and slow burnout stage. Furthermore, KAS and FWO methods were used to obtain the apparent activation energy in the combustion decomposition stage. The apparent activation energy decreased significantly with the increase in the sulfur content. The results of all experiments and analyses showed that sulfide ores with high sulfur content have a stronger tendency to undergo spontaneous combustion. The research results have important theoretical and practical implications for the prevention of SOSC. Full article

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

Background: The introduction of lung cancer screening (LCS) programmes will lead to a surge in imaging volumes and place greater demands on radiologists to provide timely and accurate interpretation. This increased workload risks overburdening a limited radiologist workforce, delaying diagnosis, and worsening burnout. Advancements in artificial intelligence (AI) models offer the potential to detect and classify pulmonary nodules without a loss in diagnostic performance. Methods: A systematic review of AI performance in lung cancer detection on computed tomography (CT) scans was conducted. Multiple databases like Medline, Embase, PubMed, and Cochrane were searched within a 12-year range from 1 January 2010 to 21 December 2022. Results: Fourteen studies were selected for this systematic review, with seven in the detection subgroup and eight in the classification subgroup. Compared to radiologists’ performance in the respective articles, the AI models demonstrated a higher sensitivity (86.0–98.1% against 68–76%) but lower specificity (77.5–87% against 87–91.7%) for the detection of lung nodules. In classifying the malignancy of lung nodules, AI models generally showed a greater sensitivity (60.58–93.3% against 76.27–88.3%), specificity (64–95.93% against 61.67–84%), and accuracy (64.96–92.46% against 73.31–85.57%) over radiologists. Conclusion: AI models for the detection and classification of pulmonary lesions on CT have the potential to augment CT thorax interpretation while maintaining diagnostic accuracy and could potentially be harnessed to overcome challenges in the implementation of lung cancer screening programmes. Full article