Search Results (4,415)

Indoor evacuation under emergency conditions remains a challenging problem due to dynamic hazards, uncertain infrastructure availability, and variability in human behavior. Traditional evacuation systems rely heavily on centralized architectures, making them vulnerable to communication failures and delayed global decision making. To address these limitations, this paper proposes a novel framework termed Federated Edge-Semantic Learning for Decentralized Resilient Evacuation (FESL-DRE). The proposed framework distributes evacuation intelligence across edge nodes, enabling autonomous decision making without dependence on a central controller. It integrates semantic reasoning to transform raw sensor data into interpretable environmental states, federated learning to model behavioral patterns in a privacy-preserving manner, and a gossip-based coordination mechanism to propagate hazard information across neighboring nodes. An adaptive routing strategy is developed to account for hazard levels, crowd density, and human behavioral variability. The framework is evaluated using a simulation-based environment under dynamic hazard conditions and varying levels of node failure. Experimental results demonstrate that FESL-DRE achieves superior performance compared to classical and centralized adaptive methods, with improvements in evacuation success rate, reduced blocked movement attempts, and enhanced resilience under moderate infrastructure degradation. Furthermore, the proposed approach maintains low communication overhead and demonstrates promising scalability characteristics within the evaluated simulation environment. The results highlight the potential of decentralized intelligence for evacuation support and provide a foundation for future validation in realistic smart building and IoT-enabled environments. Full article

This paper examines whether the persistent difficulty in addressing the eco-social crisis may partly stem from an inadequate representation of human decision-making within mainstream economic models. Although pro-environmental behaviors (PEBs) and sustainable consumption are increasingly recognized as essential for sustainability transitions, neoclassical economics still largely relies on the homo oeconomicus paradigm, which assumes fully rational and utility-maximizing decision-making. Building on contributions from psychology, behavioral economics, neuroscience, and sustainability studies, this integrative narrative review examines how cognitive biases challenge the foundational assumptions of homo oeconomicus and explores the potential role of emotional intelligence in sustainability-related decision-making. Adopting the integrative narrative review approach, this paper integrates literature on (1) cognitive biases and bounded rationality; (2) emotional intelligence and judgment bias; and (3) emotional intelligence, pro-environmental behaviors, and sustainable consumption. The evidence reviewed suggests that sustainability-related decisions are strongly shaped by cognitive and emotional processes operating under uncertainty and socially embedded consumption patterns. Within this framework, EI may represent a psychological resource capable of influence of cognitive biases by supporting emotional regulation, impulse control, self-awareness, and long-term orientation. Overall, the paper proposes a conceptual framework linking cognitive biases, emotional intelligence, and sustainable behavior beyond the traditional homo oeconomicus paradigm. Full article

Uncertainty quantification of well-log interpretation is essential to derisking subsurface exploration and development decision-making by providing possible scenarios for reservoir property distribution, fluid flow behaviors, and hydrocarbon potential. Well-log interpretation offers crucial insights into permeability variations, reservoir compartmentalization, mineral composition, and fluid mobility. Inherent uncertainties, such as those arising from geological heterogeneity, limited sampling, and non-uniform distribution of rock properties, can lead to inaccuracies that compromise petrophysical interpretation and formation evaluation. However, traditional data-driven well-log interpretation methods, which map well logs to formation properties based on core measurements, are primarily deterministic and fail to quantify uncertainty accurately. By leveraging deep learning and generative models, we introduce a probabilistic approach that significantly improves permeability estimation and uncertainty quantification. Our methodology integrates co-kriging techniques with Conditional Generative Adversarial Networks (cGANs) and Conditional Variational Autoencoders (cVAEs), establishing a quantitative relationship between kriged core, well-log data and permeability. Our approach enhances petrophysical property uncertainty estimations based on geostatistics by establishing a quantitative relationship between kriged estimates and flow-related properties. Training features are constructed using collocated co-kriging, capturing the cross-correlation between well logs (input features) and core data (output formation properties). Core bulk density, calculated from grain density, is kriged to well-log resolution to enable porosity estimation, while permeability is similarly kriged. A low-pass filter is then applied to smooth the kriged core bulk density, permeability, and estimated porosity, ensuring more accurate interpretations. The results reveal that cGANs and cVAEs consistently produce lower uncertainty estimates compared to traditional machine learning models. High-permeability zones exhibit lower uncertainty (approximately 3–5%), while low-permeability zones show higher uncertainty (10–15%). Traditional deep learning models tend to overestimate uncertainty, whereas generative models provide more reliable estimates. Additionally, applying kriged permeability data improves uncertainty estimations, further reducing uncertainty to 3% in high-permeability zones and 10% in low-permeability zones. To ensure broad applicability, the methods were tested on datasets from both carbonate and clastic reservoirs. In carbonate formations, prior classification steps are necessary to achieve accurate permeability predictions. The interpretation workflow improves permeability estimation accuracy and enhances uncertainty quantification across conventional and unconventional reservoirs. Additionally, this method is adaptable for CO₂ injection and H₂ storage wells, demonstrating versatility across various reservoir types. Full article

We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. The proposed influence comparison method simplifies expert elicitation by reducing the cognitive load of direct weight estimation, while the genetic algorithm ensures alignment of forecasts with observed or expert-derived data. A forecasting algorithm based on incremental changes in concept levels enhances the sensitivity of the output variable to factor variations. To illustrate the applicability of the framework, we construct a decision-support model for predicting weight-loss success under diverse psychological, behavioral, and environmental conditions. Simulation results demonstrate how factor ranking, scenario modeling, and paired influence analysis provide actionable insights for decision-making. Beyond the weight-loss domain, the approach is generalizable to a wide range of knowledge-based systems requiring robust integration of expert judgment, fuzzy reasoning, and evolutionary optimization. Full article

Objective: Delays in prehospital stroke care are often influenced by the actions of first witnesses (e.g., family, bystanders). However, evidence on what shapes their responses remains fragmented. This scoping review synthesizes these factors and maps them onto the Capability–Opportunity–Motivation–Behavior (COM-B) model. Methods: Following the PRISMA-ScR guidelines, eight international and Chinese databases were searched for studies published between 2019 and 2024. Two reviewers independently screened records and charted data. Because no eligible study directly recruited first witnesses, all included studies focused on stroke patients or the general public. We therefore adopted a conservative interpretive approach: we extracted factors occurring at the stroke scene before professional contact and, where a logical low-inference link existed, interpreted their potential relevance to first-witness behavior. These interpreted determinants were then mapped onto the COM-B framework. Results: Forty-eight studies involving approximately 257,000 participants from more than 20 countries were included. Factors identified across the literature covered all domains of the COM-B framework. Physical opportunity was the most frequently coded domain (33/134 codings, 24.6%), followed by psychological capability (26/134, 19.4%) and reflective motivation (22/134, 16.4%). Key barriers included insufficient stroke knowledge, limited access to emergency services, and delayed decision-making due to weak urgency perception. Conclusions: This scoping review identified behavioral factors potentially relevant to first-witness response, as interpreted from patient and public evidence. The findings suggest that prehospital stroke delays may be associated with the co-occurrence of limited capability, constrained opportunity, and insufficient motivation. These barriers often coexist and may interact with one another, highlighting the potential value of behavior-informed strategies for improving prehospital stroke response and reducing delay. Full article

Land Use and Land Cover (LULC) change is widely acknowledged as a pivotal driver of environmental change, exerting an escalating influence on surface hydrological processes. The accelerating pace of LULC alterations in response to burgeoning human populations underscores the pressing need for a comprehensive evaluation of their ramifications on surface runoff dynamics. This study investigates the impacts of LULC changes on flood behavior in a Mediterranean watershed in Crete, Greece (Geropotamos watershed). LULC data spanning the years 1990, 2006, and 2018 were procured from the European CORINE Land Cover database at a refined spatial resolution. The HEC-HMS hydrological model is employed to simulate peak discharge and associated hydrograph characteristics under varying recurrence intervals. Subsequently, selected river segments within the studied catchments undergo hydrodynamic flood modelling using the HEC-RAS hydraulic model. Flood depth maps are generated to illustrate the evolution of inundated areas relative to LULC change. The overarching objective of this research is to furnish a comprehensive understanding of how spatiotemporal variations in land use and land cover in-fluence flood characteristics, thereby facilitating informed decision making for sustainable planning. Full article

This paper introduces a methodology for evaluating the voltage stability margin in high-voltage direct-current (HVDC) systems, which analyzes the singularity of the power flow Jacobian matrix—computed via the Newton—Raphson method—and identifies saddle-node bifurcations. The continuation power flow method is employed to model progressive load increases, enabling the continuous tracking of power flow solutions and the determination of voltage collapse points. Within this framework, the system’s behavior is analyzed under contingency conditions, particularly transmission line outages, assessing its capability to maintain secure operating conditions under increasing demand scenarios. The main objective is to identify the most critical line in the system, defined as that which leads to the greatest reduction in loadability when unavailable, prior to voltage collapse. This approach allows for the early identification of structural vulnerabilities, supporting decision-making processes aimed at risk mitigation and operating cost optimization. The proposed methodology is validated using two systems: the six-terminal CIGRE-B4 HVDC system and an 11-node HVDC test feeder. Full article

In carbon sequestration in saline aquifers, many factors affect multiphase fluid migration and reservoir pressure change. This study developed a high-resolution three-dimensional numerical model to investigate large-scale CO₂ geological storage in the Ordovician carbonate aquifer of the Tarim Basin, China. This study focuses on the quantitative prediction of CO₂ plume migration, multiphase flow interactions between supercritical CO₂ and brine, and formation pressure evolution under coupled injection operations. Injection strategies were compared by constant rate (CR) and variable rate (VR) injection, and two caprock morphology-type selection by placing wells into monocline traps (wells 1/3/5) and anticline traps (wells 2/4) with varying limb dip angles and closure depths. The results demonstrate that both injection speed and caprock morphology strongly control CO₂ trapping evolution and storage security. At the end of the 500-year simulation, the dissolved-CO₂ migration distance followed the order CR > VR, indicating that, under the studied conditions, VR injection most effectively limited the lateral spread of dissolved CO₂ and thereby enhanced dissolved-CO₂ immobilization. In addition, CR and VR injection schedules have a subtle impact on long-term pressure change; Across all cases, formation pressure remained below the caprock breakthrough pressure. CR injection promotes the fastest CO₂ dissolution and pressure dissipation but yields the weakest long-term immobilization, whereas VR injection trades early dissolution rate for more effective plume containment. This result indicates that injection-strategy selection should be matched to dominant site controlled near-term pressure management versus long-term containment and to the trapping behavior imposed by caprock morphology. This study provides a mechanistically grounded optimization framework linking injection-speed control and caprock morphology to the coupled evolution of pressure-buildup safety and long-term CO₂ immobilization, supporting CCUS decision-making in the Tarim Basin. Full article

Achieving rapid and accurate obstacle avoidance in complex and dynamic environments remains a significant challenge for robots. To enhance the adaptability and flexibility of humanoid arms for obstacle avoidance, a brain-inspired multi-pathway motion decision-making method is proposed to modulate rational planning and habitual actions of humanoid arms. Firstly, a novel framework integrating both a slow and a fast pathway is designed for motion decision-making tasks. Imitating the rational planning function of the prefrontal cortex, the slow pathway employs an improved planning approach based on Real-Time Rapidly exploring Random Tree Star (RT-RRT${}^{*}$) to execute deliberate decisions, along with an improvement in planning via the Smart technique and the high-efficiency neighbor searching method. Meanwhile, mimicking the habitual responses governed by the striatum, the fast pathway utilizes an action model trained by Soft Actor-Critic to make quick and habitual motions. The model in the fast pathway is also used to guide the sampling strategy in the slow pathway. Moreover, to facilitate the integration and smooth transition between the two pathways, an emotional neural network is designed as the modulation module with inspiration from the structure and function of the amygdala. Based on body and obstacle information, the network generates emotional signals to modulate the involvement degree of the two pathways before each decision-making process. Experimental results demonstrate that the proposed multi-pathway framework achieves a higher obstacle-avoidance success rate than existing methods while generating motion characteristics that are consistent with certain aspects of human obstacle-avoidance behavior. Full article

Background: Micromanagement is an extensively prevalent yet relatively under-theorized management process in healthcare organizations. This narrative review synthesizes the literature on micromanagement and related leadership practices in healthcare, focusing on its antecedents, manifestations, consequences, and mitigation strategies. Methods: A structured literature search was conducted on 10 May 2024 across eight electronic databases. Eligible studies included qualitative, quantitative, mixed-methods, and applied studies published between 2003 and 2024. The main outcomes were the underlying causes and behavioral measures of micromanagement, examined directly, or closely related constructs such as excessive supervision, reduced autonomy, authoritarian leadership, toxic leadership, and controlling managerial behavior. The secondary outcomes involved organizational and patient-related effects and their respective interventions. Results: A total of twelve studies were selected. The identified antecedents of micromanagement were authoritarian leadership styles, autocratic and toxic leadership personality traits, overly intrusive supervisory practices, poor employee empowerment, complicated regulation, unclear definition of professional roles, and inherent structural challenges. Micromanagement behavior was seen in authoritative decision-making, transactional supervision, systematic reduction in employee autonomy, and institutionalized distrust. The consequences recorded include high levels of occupational stress, poor organizational productivity, poor quality of healthcare services, high employee turnover rates, and psychological problems. Conclusions: This review represents a preliminary conceptual synthesis of the literature that addresses micromanagement in healthcare. The evidence base is inconsistent, with many studies focusing on constructs that relate to micromanagement while not studying it directly. In future research, validated tools to assess micromanagement should be designed, as well as leadership interventions that benefit both workplace and patient outcomes. Full article

This review examines hydrogen-assisted fatigue crack growth rate (HA-FCGR) in pipeline steels with a focus on implications for integrity assessment of hydrogen transport systems. Existing natural gas pipelines offer a cost-effective pathway for hydrogen transmission; however, hydrogen embrittlement (HE) significantly alters fatigue behavior. This paper integrates scientometric analysis with a systematic review to evaluate the influence of material microstructure, welds, loading conditions, hydrogen pressure, and environmental variables on fatigue crack growth rates (FCGR). The synthesis confirms that HA-FCGR is most pronounced in the Paris region and is strongly governed by hydrogen pressure and loading frequency, while the role of material strength is less definitive than traditionally assumed. Recent advances in machine learning demonstrate strong predictive capability for FCGR; however, their integration into risk-based inspection and pipeline integrity frameworks remains limited. To bridge the gap between laboratory-scale understanding and field implementation, the concept of a near-real-world hydrogen pipeline testbed is introduced, enabling synchronized measurement of pressure cycling, material degradation, and system-level response. The review identifies critical research needs, including weld-focused fatigue datasets, realistic pressure-cycle validation, uncertainty-aware modeling, and integration of physics-based and data-driven approaches for decision-making. These findings provide a pathway toward reliable and scalable integrity assessment for hydrogen transport in existing pipeline infrastructure. Full article

Low-Earth-orbit (LEO) megaconstellations are transforming satellite communications from sparse, ground-controlled infrastructures into dense, dynamic, and increasingly autonomous space networks, while their global coverage capability is fundamentally enabled by large-scale symmetric orbital structures distributed across multiple planes and shells. As these systems expand to tens of thousands of satellites, maintaining such orbital symmetry under continuous perturbations, changing communication topologies, and varying onboard resources becomes a fundamental operational challenge. Future space systems must therefore manage, coordinate, and sustain large constellations for which their orbital configurations, communication topologies, and onboard resources vary continuously. Here, we review the management and configuration-maintenance problems of megaconstellations through a Starlink-informed perspective. We first summarize the multi-shell deployment architecture, satellite platform evolution, and dominant orbital perturbations that shape constellation behavior. We then examine hierarchical and cluster-based management strategies designed to reduce the burden on ground control and improve scalability. We further discuss in- and out-of-plane configuration maintenance. Finally, we identify open challenges in distributed autonomy, multi-shell coordination, dynamic topology management, and intelligent orbit control. This review highlights that the long-term viability of megaconstellations will depend not only on launch capacity and satellite manufacturing but also on scalable decision-making, autonomous coordination, and sustainable orbital operations. Full article

Gaussian process-based Bayesian optimization (BO) is a sample-efficient sequential strategy for optimizing expensive black-box functions. The Gaussian process provides a probabilistic approximation of the unknown function, while an acquisition function balances exploration and exploitation to select the next evaluation point. Despite significant research efforts, no master acquisition function has been identified. This paper proposes a novel adaptive acquisition function that dynamically adjusts the exploration–exploitation trade-off based on the evolution of the optimization process, rather than using fixed or random scheduling. While implemented here within a GP-based BO framework, the core switching mechanism is surrogate-agnostic: the exploitative component requires only a surrogate point prediction, and the explorative component is entirely model-free. Unlike traditional approaches, where mechanisms like UCB/LCB lean toward exploration over iterations, or fixed strategies that switch from exploratory (EI) to exploitative (PI) behavior at predetermined points, the proposed method makes purely exploitative decisions using only the GP’s prediction. However, it discards these decisions when they have low potential for significant improvement, instead focusing on uncertainty reduction. Notably, this approach uses inverse distance weighting for uncertainty quantification rather than the GP’s predictive uncertainty, avoiding bias from the GP’s predictions. Testing on benchmark functions demonstrates that the proposed acquisition function is almost always Pareto optimal, offering the most balanced trade-off between convergence to the global optimum and exploration capability compared to state-of-the-art alternatives. Full article

This study examines how venture capital investors in Kazakhstan evaluate women-led enterprises within a frontier-emerging market context characterized by institutional transition and evolving entrepreneurial ecosystems. Addressing a gap in the literature on gendered investment behavior beyond mature markets, the research adopts an exploratory mixed-methods design combining survey data (n = 21) with open-ended qualitative responses from VC professionals. Descriptive and bivariate analyses are used to examine associations between investor characteristics and evaluation criteria, while exploratory factor analysis is employed as a heuristic tool to assess whether survey items cluster around broadly interpretable evaluative orientations shaping investment judgments. The findings suggest the presence of two indicative evaluative orientations—market orientation and impact orientation—that appear to structure how respondents in this sample prioritize dimensions such as scalability, innovation, teamwork, and social value. These orientations are interpreted as context-specific and exploratory rather than statistically generalizable. The study contributes to entrepreneurial finance and institutional theory by developing an exploratory evaluative framework that captures the coexistence of commercial and developmental considerations in venture investment decision-making within a transitional economy. The findings further highlight how gendered investment dynamics are shaped by both market criteria and institutional environments, offering implications for policymakers, investors, and scholars seeking to understand capital allocation processes in underexplored venture ecosystems. Full article

This study develops mixed-integer programming (MIP) models for workforce allocation and delivery service design under delivery-date commitment requirements in third-party logistics (3PL) systems facing demand surge conditions. Existing research on delivery commitments has largely focused on customer behavior or simplified operational settings, with limited attention to integrated optimization frameworks that jointly consider workforce assignment, service scheduling, and service differentiation in realistic logistics environments. To address this gap, two MIP models are proposed for free and fee-based delivery services, respectively, incorporating customer delivery-date preferences, workforce heterogeneity, multi-skilled labor allocation, and capacity constraints within a unified decision-making framework. A real-world case study from a Chinese 3PL provider is used to evaluate the models. Computational results show that the fee-based service design improves delivery commitment reliability, workforce utilization, and profitability compared with the free-delivery setting, particularly under high-demand and capacity-constrained conditions. The findings highlight the operational value of service differentiation and workforce flexibility, and provide a prescriptive analytics framework to support integrated delivery planning in modern logistics systems. Full article