Earth and Environmental Sciences

This perspective article examines the complex and increasingly consequential relationship between emotional health and organizational performance in the healthcare sector, positioning emotional well-being not as an individual psychological concern but as a strategic organizational and public health issue with direct implications for service quality, workforce sustainability, and system resilience. Against the backdrop of escalating workload pressures, workforce shortages, digital transformation, and rising patient expectations, healthcare systems worldwide are experiencing unprecedented levels of emotional strain among professionals, manifesting in burnout, emotional exhaustion, moral distress, and disengagement. Drawing on interdisciplinary insights from public health, organizational behavior, and health services management, this perspective argues that emotional health functions as a critical mediating mechanism through which organizational structures, leadership practices, and governance models shape both employee behavior and patient-facing outcomes. Rather than conceptualizing emotional health as a residual outcome of individual resilience or coping capacity, the article reframes it as a core input in healthcare service production, comparable in strategic importance to staffing levels, clinical competence, and technological infrastructure. Synthesizing recent empirical and conceptual research, the perspective highlights that emotional strain does not uniformly or linearly translate into reduced performance; instead, its effects are highly context-dependent and are mediated through behavioral, relational, and cultural mechanisms at multiple organizational levels. Emotionally strained healthcare professionals are more likely to disengage from discretionary behaviors such as empathic communication, proactive problem-solving, teamwork, and knowledge sharing, which are essential for maintaining service quality, patient safety, and continuity of care in complex clinical environments. Over time, this disengagement erodes psychological safety, weakens trust, and diminishes collective efficacy, thereby undermining organizational climate and long-term performance capacity rather than merely affecting short-term productivity indicators. From a public health perspective, the consequences of deteriorating emotional health extend beyond organizational boundaries, influencing treatment adherence, health equity, patient satisfaction, and public trust in health systems, particularly in primary care, chronic disease management, mental health services, and end-of-life care. The perspective further situates these dynamics within the context of contemporary health system transformations, including the rapid expansion of digital health technologies, artificial intelligence–supported decision-making, and performance-based governance models. While such innovations offer opportunities to reduce cognitive overload and improve efficiency, the article critically notes that algorithmic management, intensified performance monitoring, and reduced relational time may exacerbate emotional alienation if emotional labor and psychological safety are not explicitly addressed in system design and governance. A central contribution of this perspective lies in its emphasis on the moderating role of organizational support and institutional culture. Evidence from recent organizational research consistently demonstrates that perceived organizational support, supportive leadership behaviors, participatory governance structures, and cultures that legitimize emotional vulnerability significantly buffer the negative effects of emotional strain on outcomes such as turnover intention, disengagement, and service quality. Conversely, performance-driven cultures that normalize emotional suppression and individualize responsibility for coping risk amplifying psychological risks, silent withdrawal, and moral injury, even when formal well-being initiatives are present. In critically assessing the strengths and limitations of current policy and management approaches, the article identifies a key strength in the growing recognition of workforce well-being as a system-level concern within international health policy discourse, alongside a persistent weakness in the dominance of outcome-focused performance metrics that neglect emotional labor as a process variable. This misalignment creates both risks and opportunities: while failure to integrate emotional health into governance frameworks threatens workforce sustainability and public trust, embedding emotional health indicators into quality management systems, accreditation processes, and performance dashboards offers a powerful opportunity for earlier risk detection, preventive intervention, and more equitable and resilient health system design. Translating these insights into action, the perspective outlines a set of evidence-informed policy and management implications that emphasize the need to move beyond fragmented, individual-level interventions toward integrated organizational redesign. Key recommendations include incorporating emotional health metrics—such as burnout, emotional exhaustion, moral distress, and psychological safety—into organizational performance frameworks; prioritizing workload regulation, staffing adequacy, and emotionally sustainable service models; strengthening leadership accountability for emotional climates; embedding emotional health considerations into workforce planning and retention strategies; and fostering organizational cultures that normalize emotional support and collective responsibility for well-being. Importantly, the article underscores that leadership capacity to recognize and respond to emotional strain constitutes a core managerial competency rather than a peripheral “soft skill,” with direct implications for service quality and organizational legitimacy. From a future research directions standpoint, the perspective calls for a paradigmatic shift in research design and focus. It highlights the limitations of cross-sectional, individual-level studies and advocates for multi-level, longitudinal, and comparative research capable of capturing how emotional health dynamics evolve over time across individuals, teams, organizations, and health systems, and how they interact with governance reforms, digital innovations, and labor market conditions. Comparative policy research across different health system models is identified as a particularly promising avenue for understanding how institutional design influences emotional health trajectories and performance resilience. At the same time, the article cautions against the uncritical adoption of emotional health metrics, emphasizing the ethical and methodological challenges associated with measurement, data use, and potential stigmatization if emotional indicators are instrumentalized without supportive governance. Overall, this perspective advances an integrative framework that positions emotional health as a central pillar of organizational performance, service quality, and public health outcomes in the healthcare sector. By bridging disciplinary silos and aligning research agendas with the realities of policy-making and management practice, the article offers a compelling case for emotionally informed governance as both a scientific priority and a practical necessity. In doing so, it provides clear key takeaways for policy and practice: emotional health must be treated as a strategic performance input; organizational context matters as much as individual capacity; leadership and culture are decisive leverage points; and sustainable healthcare systems depend on governance models that recognize, protect, and actively cultivate the emotional foundations of care work.

This perspective paper examines how AI is reshaping talent management in the emerging intelligence age, where value creation depends on the ability to interpret, apply, and scale insight. Despite advances in talent analytics, learning, and skills models, current approaches are static and generally misaligned with the dynamic, AI-mediated future of work. We use three lenses–AI as an evolving, permeating, and performance-amplifying force–to investigate how this technology is transforming capability dynamics, decision-making, and performance distribution, while probing the limitations of existing talent practices. We put forward a framework–Assess, Access, Accelerate, and Amplify–that reframes talent management as an integrated approach to capability development.

Introduction Teacher evaluation can operate not only as an administrative performance-management procedure but also as a psychological work condition. This study examined whether teachers’ perceptions of evaluation were associated with occupational wellbeing through professional feelings. Methods A cross-sectional self-report survey was conducted with 476 teachers from four private higher education institutions in Guangdong Province, China. Teacher evaluation was measured through perceptions of purpose, methods, indicators, content, feedback, and use of results. Reliability, factorability, correlations, regression models, and mediation analysis were examined in SPSS Statistics 26.0. Results Perceived teacher evaluation was positively associated with occupational wellbeing. Evaluation content and feedback showed stronger associations with wellbeing than evaluation purpose. Professional feelings partially mediated the association between overall teacher evaluation and occupational wellbeing. Discussion The findings suggest that evaluation systems are psychologically relevant when teachers interpret them as fair, comprehensive, feedback-rich, and recognition-oriented. The results extend organizational justice theory, self-determination theory, and the Job Demands-Resources model in a private higher education context.

Background Post-traumatic stress disorder (PTSD) in children and adolescents poses a significant public health challenge, particularly in settings with limited access to traditional mental health services. eHealth technologies are increasingly used to manage pediatric PTSD; however, evidence remains fragmented and unsynthesized. Aim To comprehensively map the application of eHealth technologies in the management of PTSD among children and adolescents, with a focus on technology modalities, target populations, implementation settings, content elements, outcomes, feasibility, and acceptability. This review also aimed to identify current evidence gaps and highlight priorities for future research. Study design Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, seven databases were searched up to November 2025. Eligible studies involved eHealth tools targeting pediatric PTSD and included quantitative, qualitative, or mixed-methods designs. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). Results A total of 26 studies published between 2010 and 2025 were included. eHealth modalities encompassed websites, mobile applications, digital games, telemedicine, virtual reality, and wearable sensors, covering the full management continuum from training, assessment, prediction, prevention, intervention, to monitoring. Most studies focused on school-aged children and adolescents, with limited attention to preschoolers or vulnerable populations. Non-randomized studies generally reported short-term PTSD symptom improvements, whereas randomized controlled trials yielded inconsistent results, with limited long-term follow-up data. Overall, feasibility and acceptability were high; however, challenges included distractions in home environments, technical issues, privacy concerns, and variable adherence. Conclusion eHealth demonstrates broad applicability and feasibility in pediatric PTSD management, yet evidence for clinical efficacy remains insufficient. The field is transitioning from “technology validation” toward “efficacy evaluation and system integration.” Future research should prioritize rigorous randomized controlled trials, extended follow-ups, culturally and developmentally appropriate designs, cost-effectiveness analyses, and deeper integration of eHealth into clinical care pathways. Policy support is essential to ensure sustainable implementation, especially in resource-limited settings.

Background Generative artificial intelligence is increasingly embedded in learning as a source of explanation, feedback, and interactive support. However, it remains unclear how such support shapes learners' cognitive processing and their subsequent willingness to continue learning. Objectives From an educational psychology perspective, this study examines how information quality, perceived ease of use, and perceived interactivity are associated with sustained learning intention through intrinsic and extraneous cognitive load, and whether prior cultural knowledge moderates the associations between cognitive load and sustained learning intention. Methods This cross-sectional survey study was conducted in The Art of Life: Mawangdui Han Culture Immersive Digital Exhibition as a high-complexity digital cultural learning context. Survey data were collected from 572 learners who reported a recent and complete experience of using generative AI tools for understanding or further learning content related to Mawangdui Han culture. Partial least squares structural equation modeling was used to test net effects, statistical indirect associations, and moderation, and fuzzy-set qualitative comparative analysis was used to identify configurations associated with high and low sustained learning intention. Results and conclusions Information quality and perceived ease of use were significantly negatively associated with both intrinsic and extraneous cognitive load, whereas perceived interactivity was significantly positively associated with both forms of load. Intrinsic and extraneous cognitive load were both negatively associated with sustained learning intention. Prior cultural knowledge weakened the negative association between extraneous cognitive load and sustained learning intention but strengthened the negative association between intrinsic cognitive load and sustained learning intention. The fsQCA results further revealed multiple asymmetric configurations associated with high and low sustained learning intention. These findings suggest that the value of generative AI-supported learning in this context lies not in maximizing interaction, but in providing cognitively manageable support. In this study, sustained learning intention refers to a self-reported intention measure rather than observed long-term learning behavior.

As artificial intelligence (AI) technologies become increasingly embedded in higher education, understanding students’ psychological experiences with these tools is essential. Drawing on the METUX model (Motivation, Engagement, and Thriving in User Experience), this study validated five Technology-based Experience of Need Satisfaction (TENS) scales (Adoption, Interface, Task, Behavior, and Life) among Chinese university students. In Phase I, 320 students completed the translated TENS scales. Three scales demonstrated acceptable reliability and model fit, while the TENS-Interface and TENS-Task scales showed poor performance, particularly in dimensions that combined positively and negatively worded items. In Phase II, revised versions of these two scales were administered to a new sample ( N = 189), with mixed-directional items reworded into a consistent positive format. CFA results indicated substantial improvements in model fit, internal consistency, and convergent validity. The findings underscore the importance of item wording consistency and the value of iterative validation when adapting instruments across cultural contexts. Importantly, this study extends the applicability of the METUX framework to Chinese higher education, offering empirical evidence that its core constructs are transferable when appropriate linguistic and contextual modifications are made. The refined TENS scales provide a robust foundation for assessing students’ basic psychological need satisfaction in AI-supported learning environments and offer methodological guidance for future scale adaptation in non-English-speaking contexts.

Quiet quitting emerged as a widespread form of employee withdrawal in the post-pandemic era, reflecting shifting work norms, unmet psychological needs, and deteriorating relationships. Although previous research has highlighted its detrimental consequences for organizational functioning, empirical evidence on its antecedents and underlying mechanisms remains limited. Drawing on the conservation of resources and self-determination theories, this study investigates how authoritarian leadership leads to quiet quitting among employees of Chinese small- and medium-sized enterprises (SMEs). Specifically, it proposes and tests a moderated mediation model in which authoritarian leadership increases quiet quitting through its positive effect on job burnout, whereas involuntary presenteeism strengthens the effect of burnout on quiet quitting. Focusing on Chinese SMEs, this study provides contextually grounded evidence on how employees interpret and respond to authoritarian leadership. To achieve this research purpose, data were collected from 363 employees working in Chinese SMEs. The results demonstrate that job burnout serves as a key psychological mechanism linking authoritarian leadership to quiet quitting. Furthermore, involuntary presenteeism amplifies the transition from job burnout to quiet quitting by exerting a positive reinforcing effect on this pathway and thereby intensifying the overall negative process. By uncovering these mechanisms, this study contributes to a deeper understanding of the harmful consequences associated with authoritarian leadership and clarifies the emergence and evolution of quiet quitting in the Chinese cultural context.

Introduction This study examined whether the integration of Project-Based Learning and Flipped Classroom into a Flipped Project-Based Learning (FPBL) model was associated with stronger student engagement and historical critical thinking in secondary vocational history learning. Methods A quantitative quasi-experimental design with a non-equivalent control group was employed. The participants were 60 eleventh-grade vocational students divided into an experimental group receiving FPBL-based instruction and a control group receiving conventional history instruction. Data were collected across three measurement points using the Student Engagement Scale and the Historical Critical Thinking Skills Inventory. Results The experimental group showed stronger improvement than the control group across behavioral, emotional, and cognitive engagement. Similar patterns were found for historical critical thinking, including analyzing historical sources, interpreting and contextualizing historical information, and synthesizing historical arguments. The mixed-design repeated measures ANOVA indicated significant effects of time, group, and time × group interaction. Discussion These findings suggest that FPBL may support vocational students' engagement and historical reasoning by connecting digital pre-class preparation, collaborative project based inquiry, and post-class reflection. Because the study used a quasi-experimental design without full random assignment, the findings should be interpreted as evidence of association rather than definitive causality.

Objective Given the high prevalence of mental health problems among university freshmen and the limited explanatory capacity of traditional unidimensional models, this study adopts a biopsychosocial (BPS) framework. Network analysis combined with simulation techniques based on the Node Identify via Recursive Graphs (NIRA) algorithm was applied to explore system-level interactions and potential targets within the network. Methods A total of 3,116 first-year university students were recruited. The network comprised biological-related functional indicators (e.g., TCM constitution types such as Qi stagnation), psychological symptoms (depression, anxiety, suicide risk), psychological traits (resilience, emotion regulation, insight), and social factors (perceived stress, childhood trauma, and social support). An Ising network model was estimated, and centrality and bridge indices were calculated. Simulation analyses were conducted by manipulating node activation probabilities to examine potential changes in overall network activation. Results Psychological resilience emerged as the central hub node, while perceived stress acted as the strongest bridge node linking social and psychological domains. Simulation analyses suggested that reductions in stress-related nodes and improvements in Qi stagnation–related indicators were associated with decreases in overall psychological network activation. Conclusion These findings support the utility of a network-based BPS framework for understanding freshmen’s mental health. Psychological resilience and perceived stress may represent important components within the system. However, simulation findings should be interpreted cautiously, as they do not imply causal intervention effects.

Verbal interactions between teachers and children play a significant role in child development. This study systematically examined the linguistic features and bidirectional dynamics of inferential talk in teacher-child dyads across three play contexts—free play, guided play, and teacher-directed play—based on 54 Chinese preschool play activities categorized according to the play continuum framework. Participants were 41 teachers and up to 637 children from their classroom aged 3–6. By employing a four-level coding scheme of abstraction (i.e., a literal-to-inferential discourse continuum from Level 1 to Level 4), the study revealed significant differences in the level of inferencing in teacher-child language across different play types. Moreover, a significant alignment was found between the level of children’s responses and teachers’ initiating utterances, as well as between teachers’ responses and children’s initiations. Each play context displayed distinct bidirectional dynamics: free play was characterized by low-level reciprocity, guided play by matched-level engagement, and teacher-directed play by progressive scaffolding that elevated cognitive complexity. These findings affirm that play types situated along the play continuum modulate cognitive tension through differential distributions of control. The study offers empirical linguistic evidence to support the optimization of multimodal play practices and the enhancement of dialogic quality in early childhood educational settings.

Wear of the pantograph slider on straddle-type monorail systems is a primary failure source affecting operational safety, and achieving accurate early warning of such wear is of great significance for intelligent maintenance. Given the temporal evolution characteristics and local abnormal features of the slider wear contour profile, this paper comparatively evaluates the warning performance of four deep learning models: BiLSTM, CNN-BiLSTM, CNN-BiLSTM-Attention, and HFOA-CNN-BiLSTM-Attention. Experimental results show that the BiLSTM model achieves an accuracy of 64.17% on the test set, indicating insufficient sensitivity to local features. With the introduction of local feature extraction, the CNN-BiLSTM model improves accuracy to 78.33%, demonstrating that integrating local pattern recognition with temporal modeling is key to enhancing diagnostic precision. The CNN-BiLSTM-Attention model without fine hyperparameter tuning exhibits performance fluctuations, achieving 74.17% accuracy. In contrast, the HFOA-CNN-BiLSTM-Attention model, optimized via global search using the HawkFish Optimization Algorithm, attains a test accuracy of 96.67% and a recall of 96.89%, achieving optimal synergy among feature extraction, temporal modeling, and dynamic weighting. The results indicate that the HFOA-CNN-BiLSTM-Attention model can not only accurately identify progressive normal wear of the pantograph slider but also effectively classify sudden fault patterns such as abnormal U-shaped wear, shifting fault warning from post-event diagnosis to pre-event prediction. This provides key technical support for predictive maintenance of pantograph sliders and operational safety.

Chipless Radio Frequency Identification (RFID) tags are widely adopted due to their cost-effectiveness, lightweight design, and passive operation. However, their lack of computational capabilities makes them vulnerable to cloning and counterfeit attacks. This paper proposes a counterfeit detection framework that combines Differential Constellation Trace Figures (DCTFs) with machine learning techniques to address these challenges. Backscattered Time-Domain (TD) signals from seven identical chipless RFID tags were processed to generate DCTFs, which were enhanced using colormaps such as Turbo, Colorcube, and Prism to highlight subtle variations. Red, Green and Blue (RGB) color features were extracted across spatial (width and height), radial, and angular dimensions, forming a multi-dimensional dataset. Gaussian noise was added to simulate real-world conditions, with Signal-to-Noise Ratios (SNRs) ranging from 100 decibels (dB) to 0 dB. Machine learning models, Support Vector Machine (SVM) and Random Forest (RForest), were trained to classify authentic and counterfeit tags. RForest demonstrated superior performance, achieving an accuracy of 99.62% and Area Under the Receiver Operating Characteristic curve (ROC–AUC) of 99.18% with multi-dimensional input data at 70 dB SNR noise.

Riparian vegetation is vital for river ecosystem sustainability, yet its multi-scale assembly mechanisms remain unclear. This study developed a framework for assessing three-dimensional riparian vegetation structure and used the Lateral–Vertical Coordination (LVC) index as a structural perspective to link longitudinal continuity with lateral flood-pulse dynamics. Results showed that vegetation biomass generally declined downstream with local non-monotonic fluctuations, whereas the Margalef richness index increased, suggesting the combined influence of watershed-scale longitudinal riverine processes and local elevational habitat filtering. Homogeneous Type riparian zones exhibited clear vertical stratification and the highest level of structural coordination (LVC = 0.418), suggesting strong lateral and vertical structural development, whereas Fluctuating Type communities showed the lowest vertical structural evenness, characterized by a canopy–understory discontinuity. Riparian zone width (RZW) emerged as the most important factor driving variation in vegetation composition, contributing 27.09% uniquely and 7.01% jointly with other predictors (total = 34.10% of adjusted R 2 ). Higher LVC suggests that vegetation structures across different dimensions are well-developed and tightly linked, thereby indicating the potential for a more structurally stable vegetation community. This study investigated the combined effects of local hydrological and geomorphic conditions and regional elevational gradients on the formation of three-dimensional riparian vegetation patterns in hilly river systems. The framework offers a robust basis for assessing riparian plant community integrity and for supporting vegetation management and restoration in river systems.

Frequent extreme precipitation under climate change threatens transboundary watershed ecosystem services, vital to regional ecological security and sustainable development. Taking the transboundary Tumen River Basin as a case, this research coupled the Empirical Orthogonal Function (EOF) - based MEP (More Extreme Precipitation) / LEP (Less Extreme Precipitation) classification, the InVEST habitat quality model, and the Geodetector to unravel how the interaction between extreme precipitation and land use modulates habitat quality spatial patterns. The results indicated: (1) extreme precipitation exhibited a spatiotemporal pattern of “high intensity in the upper reaches, strong intensification in the middle reaches, and a compound pattern in the lower reaches”, concentrated in urban agglomerations and agricultural reclamation areas of the middle and lower reaches; (2) habitat quality showed “macro-level stability with local reorganization”, forests sustained overall stability, while cold spot effects in vulnerable areas such as farmland and grassland intensified significantly during extreme precipitation years; (3) land use dominated habitat quality differentiation (explanatory power 74.4%), and its nonlinear interaction with extreme precipitation (75.3%) was the core driver of local degradation, as extreme precipitation directly amplified inherent ecological risks of vulnerable land types. This study reveals a transboundary ecological risk mechanism of “Land use sets the tone, extreme precipitation determines the intensity”, providing a scientific basis for climate-adaptive basin management, ecological conservation redline optimization, and transboundary coordinated governance.

In the context of global warming and rising sea levels, investigating the natural vulnerability patterns of island groups is essential for establishing comprehensive ecological security barriers. Using the Google Earth Engine (GEE) platform, we identified 869 islands (>0.2 km 2 ) along China's coast MNDWI and Otsu threshold segmentation. Based on multi-dimensional spatial features, K-means clustering classified these islands into four types: Large Irregular Nearshore Islands (LINI), Medium Regular Offshore Islands (MROI), Small Irregular Nearshore Islands (SINI), and Medium Regular Nearshore Islands (MRNI). An Island Natural Vulnerability Index (INVI), incorporating ecological, topographical, and hydrodynamic characteristics, was developed to assess the spatiotemporal evolution of different island types from 2015 to 2024. The results indicate that China's coastal islands showed an overall increasing trend in vulnerability. Moderately and highly vulnerable islands increased from 168 to 312 and from 8 to 36, respectively. LINIs and SINIs degraded fastest, while MROIs remained relatively stable. Different factors influenced island natural vulnerability over different timescales. Topography and vegetation coverage served as primary long-term controls, while marine dynamic factors, especially mean wave height (MWH), exerted short-term amplification. Unlike previous single-island studies, this study integrates differences in island area, shape, and location to effectively assess the natural vulnerability of different island types, thereby enhancing the comparability and regional applicability of the assessment results. Based on the findings, we propose targeted strategies to mitigate the increasing vulnerability of islands, including prioritizing interventions for rapidly deteriorating islands and implementing preventive conservation for highly sensitive SINIs.