Search Results (12,478)

Pinching-antenna systems (PASSs) offer a low-complexity and reconfigurable solution for near-field downlink communications by deploying multiple radiating elements along a single waveguide. Existing studies mainly assume simplified propagation conditions or focus on spectral efficiency, while the impact of environment-dependent interference patterns arising from user-specific blockage conditions on energy-efficient design remains unclear. An energy-efficient downlink design for single-waveguide PASS based on environment-division multiple access (EDMA) is investigated. Under a given propagation environment, EDMA exploits user-dependent blockage and visibility differences through proper pinching-antenna placement, thereby inducing different multi-user interference patterns without increasing radio-frequency hardware complexity. We examine how such blockage-dependent interference influences the relationship between spectral efficiency and energy efficiency, and develop an energy-aware EDMA framework that jointly considers pinching-antenna locations and transmit power allocation under quality-of-service constraints. The resulting coupled design problem is solved through an alternating optimization procedure. EDMA is compared with conventional time-division multiple access (TDMA) using a unified hardware and power-consumption model. Numerical results reveal clear energy-efficiency threshold behaviors with respect to blockage intensity, user population, and service requirements. The results further show that EDMA can significantly outperform TDMA in specific operating regimes. Full article

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has emerged as a promising technique to enhance spectral efficiency and coverage in fifth- and sixth-generation wireless networks. However, asymmetric indoor propagation conditions characterized by heterogeneous line-of-sight (LoS) and non-line-of-sight (NLoS) links often degrade user fairness. This paper investigates a downlink RIS-assisted NOMA system under the standardized 3GPP indoor office (InH) channel model to address fairness-oriented design under realistic link-budget constraints. We formulate an optimization problem for max–min fairness that jointly considers discrete RIS element partitioning and NOMA power allocation to achieve a symmetrical allocation of quality of service (QoS). To enable efficient computation, the non-convex problem is transformed into an epigraph form and solved using a low-complexity, bisection-based quasi-convex optimization framework combined with enumeration over RIS partitions. Numerical results demonstrate significant fairness gains; for instance, doubling the RIS array size yields a substantial improvement in the ergodic max–min rate, corresponding to approximately a 66% gain at moderate transmit power levels. Furthermore, by accounting for practical impairments such as imperfect successive interference cancellation (iSIC), imperfect channel state information (iCSI), and RIS implementation losses, the results reveal that fairness-optimal operation consistently prioritizes the far user to overcome severe indoor NLoS attenuation. The proposed framework is also compared with alternating optimization (AO)-based RIS-NOMA, conventional RIS beamforming without partition and RIS-assisted orthogonal multiple access (OMA) schemes. Simulation results confirm that the proposed framework achieves low computational complexity, making it suitable for practical indoor wireless environments. Full article

In this study, we examine an ex-service, Ni-base single-crystal blade made of alloy PWA1483, which was in service for 6000 h. Using light optical, scanning, and transmission electron microscopy, we analyzed the microstructure at the blade’s tip, middle, and root. Key focus areas included surface features, dendrite spacings, γ’-particle sizes, and dislocation densities. The findings reveal that the bulk microstructure hardly evolved. Dendrite spacings exhibited a consistent microstructure across all locations and there were no significant differences between the local alloy chemistries of dendritic and interdendritic regions, indicating high-quality processing. A bimodal γ’-particle distribution was observed. Variations in γ’-sizes and γ-channel widths were noted, with the tip showing rounded γ’-particles. Small spherical particles occurred only in the root and middle of the blade. The middle location exhibited the highest hardness. Dislocation densities were low and uniform, with the highest density correlating with the highest hardness. Full article

The adoption of information technology (IT) has become essential for improving operational efficiency and service quality in the restaurant industry. However, integrating IT into organizational structures and daily work processes often requires employees to acquire new competencies and adapt to revised workflows, which may also generate technology-related stress. To address this issue, this study examines the relationship between IT adoption and organizational performance while considering technostressors and computer self-efficacy as moderating variables. A questionnaire-based survey was administered to restaurant employees, and hierarchical regression analysis was employed to test the proposed hypotheses. The empirical results reveal that IT adoption has a significant positive effect on organizational performance. However, this relationship is contingent upon individual-level factors: technostressors weaken, whereas computer self-efficacy strengthens, the positive impact of IT adoption on organizational performance. These findings suggest that organizations seeking to enhance performance through digital transformation should not only invest in IT systems but also address employees’ psychological and competency-related factors. Full article

The energy transition and electrification are increasing the use of power electronics in low-voltage networks, increasing losses and reducing service availability when harmonic currents are concentrated in the neutral. This study statistically evaluates power quality in a campus-type microgrid with a high proportion of nonlinear loads. The novelty of the work lies in combining field measurements, percentile-based neutral-current severity analysis, and standards-based comparative mitigation assessment in a low-voltage 3P4W campus microgrid. A campaign was carried out using a Fluke 1775 analyzer, recording trends, frequency, and events. Approximately 1900 events were recorded, mainly waveform deviations, interruptions, and rapid voltage changes. Voltage distortion was moderate, with a 95th percentile between 3.6% and 3.8%, while the neutral conductor concentrated the highest severity: neutral-current THD exceeded 220% in the 95th percentile and reached maximums above 700%, with 16.78 A in the 95th percentile at the measurement point. Based on IEC 61000-2-2 and IEEE 519, four mitigation measures were evaluated in DIgSILENT PowerFactory 2024 to estimate and reduce losses and heating: load balancing, detuned compensation, passive filtering, and active filtering. Active mitigation reduced the neutral harmonic component by 80% and the combined strategy decreased the neutral current at the measuring point by 78% (16.78 A to 3.69 A), with an estimated reduction in resistive losses of close to 95%. These results suggest sustainability benefits by reducing energy wasted as heat, extending the useful life of the infrastructure and improving operational resilience. Full article

Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article

Demand-responsive transit (DRT) requires real-time vehicle assignment under dynamically arriving requests, where each decision may alter multi-stop routes and affect both onboard and newly arriving passengers. However, DRT simulations often face three key limitations: rapidly increasing computational complexity as fleet size and demand grow, insufficient integration of traffic congestion into routing decisions, and limited consideration of passenger-oriented service quality in final vehicle assignment. To address these issues, this study proposes an integrated DRT simulation incorporating three core algorithms: Fréchet Distance-based Candidate Vehicle Selection (FD-CVS), Congestion-Aware Path Planning (CA-PP), and Satisfaction-Aware Vehicle Assignment (SA-VA). FD-CVS reduces computational burden by filtering candidate vehicles based on route similarity. CA-PP extends conventional path planning by incorporating congestion-adjusted travel costs derived from public transportation data. SA-VA determines the final vehicle assignment by jointly evaluating passenger waiting time, in-vehicle travel time, and capacity constraints. The algorithms are implemented within a discrete-event simulation environment using real-world data. Experimental results demonstrate that FD-CVS significantly reduces execution time under high-demand conditions, while SA-VA improves passenger waiting time and acceptance rates. Overall, the proposed three-algorithm framework enables more realistic and computationally efficient DRT system evaluation. Full article

Background/Objectives: Nurses’ turnover intentions are strong predictors of actual turnover, which increases costs, reduces care quality, and destabilies the workforce. This study aimed to identify the key predictors of nurses’ turnover intentions using advanced machine learning methods and to explore how demographic, well-being, and work environment factors contribute to these intentions. Methods: Cross-sectional data were collected from 2459 nurses employed across various healthcare institutions. We used multichannel invitation and snowball sampling. An artificial neural network regression model was applied, combined with iterative feature selection and SHAP analysis, to identify the most important predictors of turnover intentions and to examine nonlinear and context-dependent relationships among variables. Results: Seven predictors explained 49.8% of the variance in turnover intentions, outperforming traditional linear models. Age was the strongest predictor, with younger nurses demonstrating a substantially higher likelihood of intending to leave; this association was nonlinear, with intentions decreasing more sharply at older ages. Job satisfaction and burnout were also strong predictors, particularly among younger nurses. Four work environment factors further contributed to turnover intentions: managerial support functioned as a protective factor, interpersonal conflict increased intentions to leave, limited professional development opportunities were strongly associated with higher turnover intentions, and role conflict showed heterogeneous effects. Conclusions: Machine learning approaches enhance understanding of complex workforce dynamics and enable more precise identification of high-risk groups. The findings support age-sensitive retention strategies, proactive monitoring of nurse well-being, and organizational interventions to strengthen managerial support and professional development, ensuring workforce stability and sustainable healthcare service delivery. Full article

HM Inspectorate of Probation is committed to building and utilising the evidence base for high-quality youth justice services, and to promoting excellence and having a positive impact upon those inspected and the wider sector. Research evidence and inspection findings are used to inform understanding of what helps and what hinders services and to consider system-wide change. In this article, the latest inspection and research findings in relation to the high-profile areas of serious youth violence and criminal exploitation are highlighted. The article encompasses insights from core and thematic inspections, including those from recent joint targeted area inspections (JTAIs) undertaken with other inspectorates. Alongside the JTAIs which examined multi-agency responses to serious youth violence, research was commissioned to hear directly from children and families about their experiences. Other research commissioned and published by the Inspectorate has emphasised the importance of implementing relational, child-centred and trauma-informed approaches and to optimising collaborative/partnership working across agencies and sectors. Reports have also drawn attention to the value of paying attention to the socio-ecological framework, systemic resilience, adultification biases, and both contextual and transitional safeguarding. Full article

Jordan is among the most water-stressed countries globally, with renewable freshwater availability falling below 100 m³ per capita per year. The Jordan Valley (JV), the country’s primary irrigated agricultural corridor, faces interconnected pressures across water, energy, food, and ecosystem (WEFE) systems under intensifying climatic and demographic stressors. This study evaluates the integrated performance of the WEFE nexus in the Jordan Valley using updated evidence (2018–2023) to quantify cross-sector interactions, performance gaps, and intervention priorities. A mixed-methods empirical assessment integrated quantitative sectoral data on water supply–demand and quality, electricity supply–demand and renewable deployment, agricultural productivity, and ecosystem pressure indicators, complemented by Living Lab–based stakeholder interviews. Sectoral indices were calculated based on supply–demand adequacy and aggregated into an overall WEFE Nexus Index. Results indicate persistent water scarcity, with a domestic supply of 23.48 MCM yr⁻¹ versus demand of 26.00 MCM yr⁻¹ (deficit −2.52 MCM yr⁻¹) and irrigation supply of 206 MCM yr⁻¹ relative to approximately 400 MCM yr⁻¹ demand (deficit −194 MCM yr⁻¹). Water services account for 14% of national electricity consumption, while solar pumping provides approximately 40% of daytime irrigation energy. Agricultural productivity is constrained by salinity and water quality, resulting in yield gaps (e.g., greenhouse vegetables: 4.7 vs. 10.0 t/dunum). Sectoral performance is uneven (Water 0.71; Energy 1.00; Food 0.45; Ecosystem 0.50), yielding an overall WEFE Nexus Index of 0.63 (0.50 after efficiency adjustment). Climate projections indicate continued warming (+1.8 °C) and declining precipitation (−11%) by 2060. Water harvesting, integrated renewable-powered water services, wastewater reuse, salinity management, climate-smart agriculture, and ecosystem restoration are critical to enhancing climate-resilient resource security in the Jordan Valley. The WEFE index developed here offers a tool for integrated planning and underscores that achieving climate-resilient resource security in the Jordan Valley will require strategic, cross-sector interventions and adaptive governance rather than sector-specific fixes. Full article

The Jordan Valley, a critical agro-ecosystem in Jordan, faces escalating challenges from chronic water scarcity compounded by environmental and socio-economic pressures, necessitating a systems perspective to understand cross-sector interactions beyond isolated sectoral issues. This study interprets socio-ecological interactions influencing sustainability outcomes in the region and identifies key feedback loops and adaptive responses under water scarcity through an integrated Socio-Ecological Systems (SES) and Water–Energy–Food–Ecosystems (WEFE) framework. Employing a qualitative document analysis (QDA) design, a purposive collection of peer-reviewed studies and institutional publications (n = 50) published between 2002 and 2025 was assembled and systematically coded using a structured deductive–inductive strategy grounded in SES components and WEFE domain interactions. Results reveal seven interconnected themes: water scarcity as a structural constraint, agricultural intensification and resource pressures, climate change as a stress multiplier, ecosystem degradation and service loss, pollution and environmental quality challenges, socio-economic vulnerability and livelihood constraints, and fragmented governance with coordination gaps. These themes highlight reinforcing loops where scarcity promotes groundwater reliance and non-conventional water use, intensification heightens salinity and contamination risks, climate variability escalates irrigation demands, and ecological degradation diminishes buffering capacity, while socio-economic limitations hinder adaptation and governance fragmentation impairs integrated planning and enforcement. While prior studies have examined water scarcity, agricultural intensification, or climate impacts in isolation, this study advances the literature by synthesizing these dynamics through an integrated SES–WEFE analytical lens, revealing reinforcing system feedbacks and governance constraints that are not visible within single-sector or descriptive syntheses. Full article

The issue of emergency facility location is a long-term strategic issue, and the complexity and diversity of the decision-making environment force decision-makers to focus on multiple objectives when making location decisions. We develop a multi-objective optimization system centered on cost-effectiveness, service balance, and fairness, targeting three core objectives: minimizing total costs, minimizing differences in service quality among demand points, and minimizing material shortage gaps between demand points. To address the issue of limited facility service capacity induced by material shortages, we establish a multi-objective optimization model for the reliable location of emergency facilities. By combining the model’s characteristics with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and an elite retention strategy, the Pareto frontier solution set of the multi-objective model is obtained, and the model’s feasibility is verified through various examples of different scales. Finally, sensitivity analysis was conducted on the reliability location model of emergency facilities under different disruption risks using the control variable method, and the topology structure of the reliability location allocation network for emergency facilities under different disruption situations is obtained. The research findings provide decision-makers with actionable references and technical support for selecting reliable locations for emergency facilities amid disruption risks. Full article

Urban rail transit (URT) train-door failures are safety-critical and can cause cascading service disruptions, yet existing emergency operation schemes (EOSs) are often static, difficult to adapt to evolving fault patterns, and hard to verify against updated regulations. This study proposes a retrieval-augmented large language model (LLM) framework for executable and evidence-traceable EOS generation. Multi-source heterogeneous incident evidence (structured work orders, operational impact records, and unstructured maintenance/dispatch narratives) is normalized into a structured incident representation, and a hybrid retriever (dense + BM25) with cross-encoder reranking selects compact regulatory clauses and historical cases under a fixed context budget. The generator is fine-tuned with structured objectives to enforce schema compliance, role assignment, and citation grounding. Experiments on 776 passenger-door incidents from Shanghai URT (2019–2024) show that Hybrid + rerank achieves the best retrieval quality (Recall@5 = 0.78; Coverage@B = 0.71; FirstHit/B = 0.46). For generation, the full setting improves operational usability, reaching SchemaPass = 0.88, RoleAcc = 0.91, CiteCov = 0.73, and UsableAns = 0.83, compared with 0.15 UsableAns for a pure LLM baseline and 0.26 for prompting with RAG only. These results indicate that combining high-utility retrieval with structure- and citation-aware fine-tuning substantially improves the executability and verifiability of safety-critical operation schemes. Full article

Urban living streets are core venues for promoting public health; however, existing studies often lack a multidimensional quantitative evaluation system that integrates physical, psychological, and social health dimensions. To address this gap, this study constructs a space quality evaluation model comprising 15 indicators across three health dimensions, integrating multi-source data (including Street View Imagery, POI data, and field measurements). Taking six typical living streets in the central urban area of Changsha as a case study, we applied the Analytic Hierarchy Process to determine indicator weights and evaluate space quality. The results reveal significant spatial heterogeneity: (1) The comprehensive quality scores vary markedly, with Cai’e South Road ranking highest (66.62) and Zengjiawan Lane lowest (28.37); (2) key factor analysis indicates that seven indicators—including Street Width, Motorization Level, and POI Functional Diversity—are significantly associated with space quality, among which Sidewalk Width and Relative Sidewalk Width are identified as critical determinants; (3) addressing identified deficits in slow-traffic spaces and service amenities, this study proposes health-oriented micro-renewal strategies. This study provides a transferable analytical framework and practical decision support for the assessment and improvement of urban living street space quality. Full article