Search Results (10,702)

Organizations ought to demonstrate a responsibility for conditions that reduce moral stress and enhance moral resilience for their employees. No literature to date has explored how Emergency Management Personnel (EMP) experience both moral stress and distress [(di)stress], building up to stigma during health crises, given their role in emergency management operations. This study draws from a primary study of EMP, including frontline and first responders and those in leadership, who reported structural stigma during the COVID-19 pandemic. Our research question was, In what ways did structural stigma shape the moral landscape of emergency management practice during COVID-19? This qualitative study draws on the paradigm of critical realism to conduct thematic analysis. Interviews and focus groups were collected in 2024 from a total of 23 participants in the Greater Toronto Area, Canada. Participants represented EMP across emergency and public service sectors. System-level stressors revealed disruptions or “cracks” from an overwhelmed public services system. In sum, systemic “cracks” gave rise to organizational mechanisms designed to compensate for system failures, inadvertently propagating structural stigma. At times these mechanisms generated moral distress and/or resilience, through simultaneously expanding and limiting EMP’s responsibility and agency. The authors suggest that EMP build their leadership capacity to enhance skills of structural competency. Full article

Sustainable urban development depends not only on efficient design and construction but also on the long-term governance of built assets during their operational phase. However, Building Information Modeling (BIM) is still predominantly applied to design and delivery processes, with limited integration into structured maintenance and renewal planning. This study develops a BIM-enabled lifecycle governance methodology that integrates lifecycle cost modeling, service-life estimation, and time-based renewal scheduling into a unified digital asset environment. Rather than proposing a new theoretical model, the study focuses on the systematic integration and operationalization of these components into a reproducible and auditable workflow. The methodology is validated through an anonymized multi-asset industrial portfolio comprising buildings, technical infrastructure, and external works, modeled over a 30-year planning horizon using structured maintenance and renewal data. Comparative scenario analysis between reactive and planned lifecycle strategies evaluates expenditure distribution, capital concentration, and intervention synchronization. The results demonstrate that embedding structured lifecycle parameters within BIM improves the predictability of annual expenditures, reduces cost concentration in peak renewal years, and enhances transparency of long-term asset planning without significantly altering cumulative lifecycle costs. These outcomes support more structured financial planning and coordination of maintenance and renewal activities at the portfolio level. The study does not quantify environmental or social sustainability impacts; its contribution lies in providing a governance-oriented methodology that transforms BIM-based asset data into decision-support outputs for long-term lifecycle planning. Full article

Entrepreneurs represent a key piece in the generation of jobs and contribution to the economy through the performance of their businesses. Taking into account that literacy and financial inclusion constitute a business facilitator for the development of businesses, this study was based on analyzing the three variables, aiming to identify whether inclusion and financial literacy influence business performance. Through a non-experimental, quantitative study based on structural equations, a sample of 469 entrepreneurs from Peru, Bolivia, and Colombia was studied. The hypotheses were supported by observing the positive effect of one component of financial literacy (Cash Forecasting) and three components of financial inclusion (Access, Barriers, and Use) on Business Performance. However, the proposed model shows that the direct effect of two components (Bookkeeping and Financial Education) of financial literacy is not statistically significant. Therefore, these factors are vital tools that can help Latin American entrepreneurs make informed financial decisions, manage resources effectively, and build solid and sustainable businesses. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article

Background/Objectives: The digital transformation of healthcare has become a key component of building resilient and sustainable health systems. However, the long-term sustainability of digital health technologies depends not only on user acceptance but also on the institutional governance conditions that shape how these technologies are implemented and utilised in practice. This study examines how institutional factors shape the sustainable utilisation patterns of Internet of Things (IoT) technologies in regulated healthcare environments, with hospital IoT-based asset management systems, a mature and widely deployed use case in China’s public hospitals, providing the empirical context for the investigation. Methods: Drawing on institutional theory and the Technology Acceptance Model (TAM), we conceptualise user perceptions as behavioural micro-foundations through which institutional conditions influence technology utilisation. A survey of 293 healthcare professionals from two large public hospitals in China was analysed using Structural Equation Modelling (SEM), incorporating mediation and Multi-Group Analysis (MGA). Results: The results demonstrate that technical compatibility (TC) significantly enhances perceived ease of use (PEU) (β = 0.40), while organisational support (OS) positively influences both perceived usefulness (PU) (β = 0.35) and PEU (β = 0.30). Conversely, regulatory compliance (RC) negatively affects PU (β = −0.25) and PEU (β = −0.20), revealing a tension between accountability requirements and operational efficiency. The model explains between 58% and 67% of the variance in key constructs. Conclusions: Overall, the findings indicate that sustainable utilisation patterns depend on alignment between technological capabilities and institutional governance conditions, with user perceptions operating as behavioural micro-foundations through which institutional effects are transmitted. By integrating institutional theory with technology acceptance research, this study contributes a governance perspective for understanding sustainable digital transformation in healthcare systems and provides practical insights for designing interoperable, compliant, and supportive digital health infrastructures to enhance hospital operational efficiency and quality of care. Full article

This study investigates the educational potential of Large Language Models (LLMs) for automating rule-checking tasks in Building Information Modeling (BIM) instruction. A quasi-experimental classroom implementation was conducted over two consecutive semesters with 55 graduate students in a Construction Management program. In Fall 2024, students were taught manual rule-checking techniques, whereas in Spring 2025, students received additional instruction in LLM-based prompting and Python code generation for automated compliance checking. A mixed-methods evaluation was conducted using surveys, NASA Task Load Index ratings, assignment-based learning outcomes, and structured interviews. Compared with the manual-only cohort, the LLM-assisted cohort reported significantly lower mental, temporal, and frustration demands, as well as higher perceived time efficiency and overall effectiveness. The LLM-assisted group also achieved significantly higher performance in violation detection and method accuracy, although no significant differences were observed in code interpretation or reflective analysis. Qualitative findings further revealed both the efficiency benefits of AI-assisted automation and persistent challenges related to prompt refinement, debugging, and output validation. These findings suggest that LLMs can enhance BIM instruction when paired with structured pedagogical scaffolding to support critical oversight and novice learners. Full article

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

This study reviews and summarizes existing research on how small and medium-sized construction enterprises adopt Building Information Modeling (BIM), while also highlighting potential areas for future investigation. The analyses aimed to address two research questions: RQ1: What research areas are explored in scientific publications on the use of BIM in small and medium-sized enterprises? RQ2: What future research directions should be pursued regarding the implementation and development of BIM in SMEs? A bibliometric analysis and science-mapping analysis was conducted on 162 Scopus-indexed publications (2007–2025) using Excel, VOSviewer and Biblioshiny, complemented by a state-of-the-art review of 69 recent studies (2022–2025). Keyword analyses revealed five thematic clusters: implementation and adaptation, collaboration and integration, construction industry digitalization, project management, and information systems. Within the identified areas, a state-of-the-art review was conducted to indicate the main research domains and directions for future research. Emerging topics include Industry 4.0-enabled digitalization, common data environments, interoperability, decision-making, human resource management, and safety and risk assessment. Future studies should examine managerial competencies, behavioral drivers of adoption and value creation in resource-constrained contexts. Policymakers and professional bodies should combine capacity building, incentives and lightweight interoperable tools to lower entry barriers for SMEs. Integrating bibliometric mapping with qualitative synthesis, this paper offers an evidence-based research agenda and guidance to support BIM diffusion in SMEs. Full article

Background: Healthcare inventory management is critical for ensuring timely access to supplies and reducing stockouts. As supply chains grow more complex, algorithms, AI, and analytics techniques have emerged as tools for forecasting, tracking, classification, and procurement. Yet empirical validation across diverse contexts remains inadequate, and existing reviews treat these approaches as separate streams rather than an integrated system. Methods: To evaluate these capabilities, a systematic review of 64 peer-reviewed articles published between 2011 and 2025 was conducted using a descriptive and content analysis approach on the use of Triple A (Analytics, AI, and Algorithms) techniques in inventory frameworks across various healthcare contexts, such as hospitals, pharmaceutical supply chains, and humanitarian supply chains. Results: Integrating multiple Triple A approaches consistently outperforms single-method strategies, particularly with RFID and IoT tools. Key findings often overlooked are: emergency procurement and classification, which remain neglected despite the highest patient safety stakes, and key procurement drivers—organizational conditions, supplier reliability, and team capacity. Data quality, interoperability, and cybersecurity further constrain generalizability. Conclusions: Bridging these gaps requires integrated Triple A approaches rather than single methods. Phased implementation, cloud-based platforms, and privacy-by-design offer practical pathways for building resilience under real-world constraints. Full article

This review explores the hypothesis that schizophrenic symptoms may be understood not as isolated deficits, but as interconnected manifestations of a structural reorganization of consciousness. The premises of this work are grounded in a comparative matrix that suggests an underlying “consanguinity” between the philosopher’s voluntary epoché—the suspension of the natural attitude performed to study the inner workings of consciousness—and the involuntary “unworlding” passively experienced in schizophrenia. By exploring this shared ontological ground, the text suggests how specific phenomenological shifts, such as the collapse of the “vital drive,” may manifest as clinical markers; this process may eventually lead to an involuntary “transcendental reduction” where the mind’s internal machinery becomes an object of forced awareness. Building on these premises, the review tentatively outlines several key achievements. It addresses the substrate-subjectivity gap by linking biological sensory-binding failures to the onset of involuntary hyper-reflexivity. Regarding structural loss and gain of function, it suggests that the psychotic transition involves a simultaneous erosion of common-sense coherence and an intensified receptivity to unfiltered perceptual fragments, which may trigger a search for metaphysical meanings. In terms of a therapeutic synthesis, it proposes exploring the conversion of “artless decentering” into a manageable, strategic distance through mindfulness and person-centered position-taking. Finally, it discusses a potential nosographic evolution, advocating for future diagnostic classifications that prioritize the experiencing self and qualitative insights to support a more translational and empathetic approach to psychiatry. Full article

The increasing adoption of electric vehicles in residential buildings creates challenges for charging infrastructure management, particularly in pricing services to balance revenue, user satisfaction, and grid stability. Traditional pricing methods, such as fixed rates and time-of-use tariffs, cannot adapt to the dynamic nature of charging demand. We propose a reinforcement learning framework for dynamic pricing of residential EV charging stations. The framework formulates the pricing problem as a Markov decision process and employs proximal policy optimization to learn a pricing policy based on real-time conditions. The state representation includes ten features covering temporal indicators, charging loads, grid status, traffic, and weather. A multi-objective reward function balances revenue, station utilization, grid stability, and user satisfaction. The system is trained on 6878 charging sessions from a residential complex in Trondheim, Norway. Compared with fixed pricing and time-of-use pricing, the proposed method achieves an overall score of 0.569, representing improvements of 32.9% and 48.9%, respectively. Sensitivity analysis confirms that the model remains robust across different demand response assumptions. The main contributions include a custom reinforcement learning environment for residential EV charging and empirical evidence that learned policies outperform traditional pricing approaches. Full article

Accurately quantifying the driving mechanisms of land surface temperature (LST) is fundamental to developing climate-resilient urban strategies. However, traditional linear models often fail to capture the complex nonlinear interactions and spatial non-stationarity inherent in urban thermal environments, especially when hindered by multicollinearity among morphological indicators. This study proposes a multi-scale spatial explainability attribution framework by integrating an XGBoost machine learning model with SHAP (SHapley Additive Explanations) to decipher the thermal dynamics of Changchun, a representative cold-region city in China. Utilizing a 500 m grid-based dataset, we incorporated 3D urban morphology (BVD), land cover (NDVI, NDWI), and socioeconomic factors. The results indicate that the XGBoost model achieves superior predictive performance (R² = 0.694) compared to traditional OLS models. SHAP global attribution identified Building Volume Density (BVD) as the primary warming driver, as its three-dimensional volume creates “thermal traps” through radiation trapping and reduced ventilation. Notably, NDVI exhibits a significant nonlinear “cooling threshold effect” at 0.3, beyond which its mitigation efficiency stagnates or even reverses due to vegetation fragmentation and heat-induced physiological stress. Furthermore, spatial mapping reveals a distinct “sign reversal” in NDWI’s impact, reflecting the dualistic thermal regulation of water bodies across different urban–rural gradients. These findings suggest that urban thermal management strategies should shift from merely restricting 2D surface occupancy (e.g., Building Density) to a more sophisticated approach focused on precisely controlling 3D volume intensity (BVD). This study provides a “point-to-area” diagnostic tool supporting a transition to spatially targeted urban planning interventions. Full article

The traditional methods of field-based phenotypic data collection for crop germplasm resources are often inefficient and highly subjective. As the foundation for breeding innovation, these resources require precise identification of phenotypic traits for effective evaluation and utilization. Therefore, efficient and standardized management of germplasm data is critical during the breeding process. To address this, we have developed an intelligent recognition and management system focused on the crop’s organ characteristics. The system consists of a web client for overall project management and data download, and a WeChat Mini Program for data collection and uploading. Both components are integrated with image analysis models. Using a soybean variety screening experiment as a case study, we have constructed multiple high-definition datasets for soybean phenotypic traits, and employed YOLOv11 series models for object detection, image classification, instance segmentation, and pose estimation to build analytical models for each of these traits. All models achieved a mean average precision (mAP@0.5) exceeding 94%, along with a top1_accuracy of 0.999. In practical evaluations, all models took between 0.71 and 3.03 s to make predictions for 100 images, achieving an accuracy rate of over 98%. This system delivers a comprehensive solution for field phenotypic identification of crop germplasm resources, substantially enhancing the efficiency and objectivity of data collection and analysis. It serves as a valuable decision-support tool for precision breeding and digital agriculture. Full article

Faults in aero-engine rotating components account for more than 60% of total failures, and their early features are easily masked by noise under complex conditions. Traditional single-sensor diagnosis suffers from low feature utilization, poor interpretability, and weak cross-condition generalization. This paper proposes a multi-source fault diagnosis method for aero-engines based on an explainable boosted tree, integrating spatiotemporal attention (STA) and adaptive feature selection (AFS). We collect multi-domain data from four standard core sensors widely used in existing engine health management systems and extract multi-dimensional features to build a heterogeneous feature set. Adaptive feature selection is implemented using mutual information and a variance inflation factor. A spatiotemporal attention mechanism is introduced to weight and fuse features effectively. The fused features are used to train an XGBoost classifier, and SHAP values are adopted to quantify feature contributions and improve model interpretability. Uncertainty sources and sensitivity boundaries are quantitatively analyzed to support engineering acceptance. The method achieves high sensitivity to early weak faults and stable uncertainty under complex operating conditions. Tests on a fault simulation test rig show that the proposed method achieves 99.2% diagnosis accuracy and 97.5% cross-condition generalization accuracy, outperforming conventional models. It can identify early weak fault signatures, clarify key fault indicators, and provide a quantitative basis for fault tracing and maintenance decision-making. The method employs a standard sensor suite without additional hardware costs, features lightweight computation and low inference overhead, and delivers clear economic benefits by reducing false alarms, avoiding unplanned downtime, and optimizing maintenance resources. It offers a reliable, cost-effective solution for aero-engine fault diagnosis under complex operating conditions. Full article

Accurate estimation of residential building energy consumption and associated CO₂ emissions is essential for refined urban carbon management. This study develops a hybrid framework that integrates physics-based simulation and machine learning to estimate residential building energy use and energy-related CO₂ emissions in Shenzhen in 2020. Representative building archetypes were first simulated and then used to train machine-learning models for large-scale applications. Building-level energy estimates were further combined with a bottom-up inventory to generate high-spatiotemporal-resolution maps of residential CO₂ emissions. The results show that: (1) the selected model achieved good accuracy and temporal robustness, with strong agreement between estimated and reference energy use at daily, monthly, and annual scales; (2) residential energy use was primarily driven by meteorological conditions, especially daily mean temperature and the duration of high-temperature conditions, and exhibited clear weekly and seasonal patterns, with higher values on weekends and in summer; (3) residential CO₂ emissions in Shenzhen reflected the combined effects of scale and intensity, with Longgang and Bao’an contributing the largest total emissions, Self-built residential buildings contributing the largest aggregate emissions, and Old residential buildings showing the highest average emissions per building; (4) emissions were highly concentrated in a small number of high-emission buildings, which were more frequently distributed along road-adjacent block perimeters. Overall, the proposed framework improves the fine-scale characterization of residential building CO₂ emissions and provides a useful basis for hotspot identification and targeted mitigation. Full article