Search Results (3,779)

Risk-based management of water quality in drinking water supply systems requires decision-support tools that extend beyond parameter-level hazard assessment and enable prioritization at the level of physical system objects. In this context, hazard assessment refers specifically to drinking water quality parameters and their possible operational and health-related implications, particularly in facilities serving sensitive user groups. This study proposes a class-based extension of the FPOR (Fuzzy Priority of Objects at Risk) framework to support object-level operational prioritization under conditions of limited data availability. Hazard importance is adopted from prior hazard prioritization using the Fuzzy Priority Index (FPI), while priority premises (PP) are represented as object classes reflecting typical functional and operational characteristics. Class-based profiles of local hazard relevance and object vulnerability are defined using expert-informed fuzzy representations and aggregated into FPOR scores to produce a relative ranking of priority premises classes. The results demonstrate how hazard prioritization can be systematically propagated to object-level decision units without reliance on site-specific monitoring data. The proposed framework provides a transparent and scalable basis for early-stage risk-based planning and supports the operational implementation of object-oriented management strategies in drinking water systems, while maintaining a clear conceptual separation from health risk assessment addressed in subsequent studies. Full article

Parabolic trough collectors (PTCs) are highly sensitive to receiver positioning accuracy; however, most existing studies report optical efficiency degradation without formally defining alignment tolerance limits. This study proposes a tolerance-based thermo-optical risk framework to quantify allowable receiver misalignment envelopes for reliable PTC operation. A Monte Carlo Ray Tracing (MCRT) methodology is employed to evaluate the impact of angular receiver misalignment on optical efficiency and circumferential heat flux redistribution. Beyond conventional efficiency metrics, normalized flux-based thermal non-uniformity indicators are introduced to assess thermo-mechanical risk without requiring full thermo-fluid modeling. The results reveal a nonlinear decoupling between optical acceptability and thermal safety. While optical efficiency remains above 0.80 up to approximately ±6°, pronounced flux localization and rapid growth of thermal stress indicators occur beyond ±4°, marking the onset of thermally critical behavior. The identified ±4° threshold corresponds to approximately twice the collector half-acceptance angle (θ_₍crit₎/δ ≈ 2), demonstrating geometry-dependent scaling characteristics. The proposed framework formalizes the optical–thermal decoupling phenomenon and transforms conventional efficiency-based evaluation into a reliability-informed alignment tolerance assessment tool applicable to manufacturing precision, installation control, and operational quality management in CSP systems. Full article

High-altitude and high-speed aircraft generate substantial aerodynamic heat during flight, creating a harsh thermal environment in the engine compartment that risks overheating and burnout of control components and fuel and lubricating oil accessories. Consequently, the thermal protection system (TPS) design for engine accessories has become one of the key technologies in hypersonic vehicle design. Based on certain TBCC, this paper uses a modular active-passive integrated TPS design and employs the quality management experimental design tool to optimize the design and decouple the method proposed on the modular design boundaries. This paper is the first to combine modular design with design of experiments (DOE) tools and apply them to the TPS of high-altitude and high-speed combined power accessories. The design scheme is optimized by identifying the main influencing factors. The optimized TPS scheme decreases the performance loss by 10% and increases cooling efficiency by 22–26%. The proposed engineering method shortens the development cycle significantly and improves efficiency by 78%. The modular design method for accessory TPS provided in this paper has good engineering applicability and can be widely used in the early stages of thermal protection scheme design, scheme optimization, scheme selection, and overall thermal management of hypersonic combined power systems. 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

Effective water resource management requires understanding the causal mechanisms driving water quality dynamics, yet extracting actionable insights from complex multivariate monitoring data remains a persistent challenge. This study presents CEcBaN (CCM-ECCM-Bayesian Networks), a decision-support tool that integrates Convergent Cross Mapping (CCM) for detecting dynamical coupling, Extended CCM (ECCM) for identifying temporal lags and causal directionality, and Bayesian network (BN) modeling for probabilistic scenario-based inference. The tool was designed to enable managers and researchers without programming expertise to reconstruct causal networks from routine monitoring data, distinguish direct from indirect effects, and evaluate intervention scenarios. CEcBaN was validated using four synthetic datasets with known causal structures, achieving superior specificity (0.83) and edge count accuracy (25% error) compared to Transfer Entropy (0.47 specificity, 139% error), Granger causality (0.82, 39% error), and the PC algorithm (0.83, 46% error). Application to Lake Kinneret, Israel, demonstrated the tool’s utility across three water quality challenges: (1) nitrogen cycling, where the nitrification pathway was reconstructed and seasonal stratification was identified as a key modulator (accuracy 0.931); (2) thermal dynamics, where a transition from atmosphere-driven to internally regulated heat transfer during stratification was revealed (2.1-fold increase in coupling strength); and (3) cyanobacterial bloom prediction, where prior phytoplankton community composition provided a 4–6-week early warning window (accuracy 0.846). CEcBaN advances causal inference in water resource management by making these analytical methods accessible through an intuitive interface. Full article

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

Sediment yield remains a major challenge in erosion-prone watersheds because it affects reservoir capacity, water quality, hydraulic infrastructure, and ecological stability. Although numerous studies have examined sediment yield estimation and sediment control, these topics are often treated separately, limiting the development of integrated watershed management strategies. Unlike many existing sediment yield review papers that focus primarily on predictive models, erosion processes, or management measures in isolation, this study provides an integrated synthesis of sediment yield estimation methods and sediment control strategies within a single watershed management framework for erosion-prone environments. The review covers empirical models, traditional sampling, physically based models, and emerging data-driven tools such as artificial intelligence, machine learning, remote sensing, and sensor-based monitoring, alongside structural, vegetative, and adaptive sediment control measures. The reviewed literature indicates three major trends: increasing integration of GIS and remote sensing with conventional models, wider use of process-based models for scenario analysis, and rapid growth of AI-based methods for real-time and nonlinear prediction. The findings further show that no single estimation or control strategy is universally applicable; performance depends strongly on watershed scale, sediment connectivity, land use, climatic regime, and data availability. Overall, the review highlights the need for integrated, adaptive, and site-specific sediment management frameworks that combine predictive modeling, monitoring technologies, and practical control interventions to improve long-term watershed resilience. Full article

Background and Objectives: Headaches have become one of the global public health burdens in the 21st century. Although findings on the presence of headaches in general and older adult populations have been well-documented, little evidence has been observed for university students. Moreover, their level of quality of life seems to be impaired due to stressful events and the inability to cope with them. However, the mutual relations between headaches and quality of life in this population remain unknown. Therefore, the main purpose of the study was to examine the associations between lifetime headaches and the presence of headaches in the last 12 months with quality of life. Materials and Methods: In total, 1350 university students (age = 22.9 ± 2.3 years; 81.3% female) were recruited. Each participant was instructed to fulfill the Headache-Attributed Restriction, Disability, Social Handicap, and Impaired Participation (HARDSHIP) questionnaire, a reliable and valid tool to assess headache and quality-of-life characteristics. Headache characteristics, headache-related disability (HALT-90), and quality-of-life domains were analyzed using Spearman’s correlation analyses and structural equation modeling. Results: Participants with migraine reported more frequent and more intense headaches and substantially greater headache-related disability compared with those with tension-type or undifferentiated headache. The mean number of lost days in the previous 90 days (HALT-90) was 14.3 (SD 23.1) in the migraine group compared with 4.53 (SD 12.0) in the tension-type headache group and 5.77 (SD 10.9) in the undifferentiated headache group. Across most WHOQOL domains, students with migraine reported lower quality-of-life scores compared with other headache groups. The WHOQOL-8 total score averaged 30.9 (SD 4.79) in the migraine group and 33.6 (SD 3.93) among participants without headache. Greater headache burden was consistently associated with poorer quality of life, with headache-related disability showing the strongest correlation with energy for everyday life (r = −0.345, p < 0.001). Conclusions: These findings suggest that greater headache burden, particularly migraine and headache-related disability, is associated with poorer quality of life among university students. The results highlight the need for targeted prevention programs aimed at helping students manage stress more effectively and improve their quality of life. Full article

Evaluating the aquatic ecological and environmental consequences of dredging projects with precision is essential for reconciling engineering objectives with the long-term health of aquatic ecosystems. This study establishes an evaluation system for the aquatic ecological and environmental impacts of dredging engineering based on the Pressure–State–Response (PSR) analytical framework, and constructs a comprehensive assessment system through Velocity Pausing Particle Swarm Optimization–Projection Pursuit (VPPSO-PP) coupled with fuzzy pattern recognition. Taking the Pinglu Canal project as a case study, the objective weights of indicators are obtained via the VPPSO-PP method, and the impact levels are determined by combining the fuzzy pattern recognition model. Case studies show that the quality of discharged residual water is the most critical factor affecting the aquatic ecological environment, ranking highest with a weight of 0.0839, followed by the proportion of aquatic ecological restoration investment at 0.0685. Among the five typical dredging sections of the Pinglu Canal, the Shaping River section and the Offshore Estuary Section were rated as having a “mild impact.” In contrast, the Main Stream of Qinjiang River section, the Watershed section, and the Qinzhou urban section were rated as having a “moderate impact.” These evaluation results are consistent with the actual engineering conditions. The model developed in this study enables a quantitative and objective assessment of the aquatic ecological impacts of dredging projects. It provides a scientific basis and a practical tool for ecological management and decision-making in dredging operations. Full article

Data-driven approaches are increasingly required to optimize biofertilization strategies in forage systems. Machine learning (ML) provides an efficient tool for identifying functional drivers in complex plant–soil–microbe systems, offering important perspectives for precision data-driven agriculture. However, despite its potential, ML remains data-driven in studies involving diazotrophic inoculation using biochar as a pelletizing material, particularly in forage grasses. This study applied ML to predict the key drivers controlling Brachiaria brizantha performance and soil quality under biochar-pelletized diazotrophic bacteria (DB). Five isolates were inoculated with or without biochar, and plant traits and soil attributes, including pH, potassium, phosphorus, sodium, and urease activity were evaluated. These data were integrated into multivariate analyses and ML algorithms, including Linear Discriminant Analysis, Random Forest, and Support Vector Machine, to identify the functional drivers that best discriminate treatment performance and uncover mechanistic functional drivers. All isolates increased soil potassium content, with the highest values in the biochar amended treatments, and a 39% increase. Soil pH and urease activity were significantly modulated by isolate identity, while biomass allocation patterns differed among treatments. Overall, the results highlight that biochar pelletization can enhance the effectiveness of DB inoculants. ML revealed that dry foliar biomass, soil pH, and fresh root weight were the most predictive variables, highlighting consistent signatures explaining plant–soil responses to biochar-pelletized DB. These findings demonstrate that interpretable ML can disentangle complex plant–soil–microbe interactions, support precision biofertilization design, and serve as an efficient decision-support tool for sustainable pasture management. Beyond the present system, this study establishes a transferable and scalable analytical framework for precision biofertilization strategies in forage systems and other biochar-mediated agroecosystems, advancing predictive and data-driven approaches in sustainable agricultural engineering. Full article

Urban blue–green infrastructure (BGI), comprising vegetation and aquatic elements, is fundamental to city resilience and climate adaptation. Effective BGI management necessitates high-resolution, spatially accurate data for which Unmanned Aerial Vehicles (UAVs) have emerged as versatile monitoring tools. This study provides a critical synthesis and analytical evaluation of UAV-based technologies for BGI management from 2018 to 2025. Following a PRISMA-guided methodology, the review evaluates dominant research themes, sensor technologies (RGB, multispectral, thermal, LiDAR, and water and air quality sensors), and analytical methods. Departing from traditional descriptive reviews, this study appraises the operational maturity of these technologies using an adapted Technology Readiness Level (TRL) framework. The analysis identifies a significant “maturity gap” between standardized structural mapping (TRL 9) and experimental functional assessments of environmental conditions (TRL 4–6). Notably, the article includes a detailed analysis of specific UAV platforms and sensors, providing specifications of technological capabilities. By identifying critical technical, regulatory, and economic bottlenecks, this review provides a robust, evidence-based foundation for the deployment of drones in enhancing urban resilience and sustainable environmental governance. Full article

With the acceleration of global climate change and urbanization, air pollution, particularly ozone pollution, has become a critical environmental issue, especially in the Beijing–Tianjin–Hebei region of China. This study investigates the spatiotemporal distribution of ozone pollution and its precursors, focusing on formaldehyde as a key indicator of volatile organic compounds. Utilizing high-resolution remote sensing data from the China High-Resolution Air Pollutants dataset and TROPOMI HCHO observations from 2013 to 2022, we employed advanced techniques such as the Kolmogorov–Zurbenko filter and high-value area identification to analyze ozone pollution trends, meteorological influences, and the spatial distribution of HCHO concentrations. Our findings reveal a significant increase in ozone concentrations across BTH, with an annual growth rate of 2.51 μg/m³, peaking during the summer months. The KZ filter decomposition highlighted that short-term and seasonal variations dominate ozone fluctuations, driven by meteorological factors such as solar radiation and temperature. Furthermore, the identification of HCHO HVAs demonstrated that urban agglomeration and expansion zones exhibit higher HCHO concentrations, with VOCs-limited zones showing the most pronounced HCHO levels. The study also introduced the PHV (Percentage Higher than Vicinity) index to quantify anomalous HCHO emissions, providing a robust tool for pinpointing pollution hotspots. Based on these insights, we propose targeted emission control strategies for key regions, including urban expansion zones in Zhangjiakou and non-urban zones in Qinhuangdao, to mitigate ozone pollution effectively. This research offers valuable scientific support for regional air quality management and the formulation of precise pollution control measures in the Beijing–Tianjin–Hebei region. Full article

Construction projects are prone to conflicts and disputes due to differing stakeholder interests, which can adversely affect their successful completion in terms of time, cost, and quality. Thus, implementing effective conflict management methods is essential to reduce negative outcomes and capitalize on the positive outcomes of conflicts. However, there is still limited understanding of the status and trends of stakeholder conflicts, and critical conflict causes and management strategies identified by previous studies. Thus, a systematic literature review was conducted, complemented by a scientometric analysis using the VOSviewer bibliographic tool and Pareto analysis to systematically identify critical factors within literature. A total of 63 studies published between 1993 and 2025 were analyzed. Findings indicate that most recent studies have focused more on human, contractual, and technological aspects of conflict. Overall, this study identified 46 conflict causes and 58 management strategies, which were categorized into different groups based on their characteristics. Among these, 23 and 31 were identified as most critical causes and management strategies based on Pareto analysis, with most factors linked to stakeholder relationships. The study offers a systematic understanding of the status quo and emerging themes in stakeholder conflicts research in construction industry. The findings of this study will be beneficial for researchers in identifying future research directions and project stakeholders to understand the most common conflicts and effective management methods for handling conflicts in construction projects. Full article

Structural symmetry appears as a natural feature in both optimal solution landscapes and hospital scheduling behaviors, representing an inherent balance that can be deliberately leveraged to improve how quickly algorithms converge and how reliably systems perform in intricate healthcare optimization contexts. Managing hospital resources is a multifaceted challenge that requires simultaneously addressing several competing goals, such as reducing costs, improving patient experiences, making the most of available resources, distributing staff workload fairly, and strengthening readiness for emergencies. Traditional optimization approaches frequently struggle to cope with the complexity and ever-changing nature of modern healthcare environments. To address this gap, this study introduces a novel Multi-Objective Dream Optimization Algorithm (MO-DOA) tailored for smart healthcare resource management, which adapts a biologically inspired optimization framework to meet the specific demands of healthcare settings. The MO-DOA is built around three core mechanisms: a foundational memory component that retains high-quality solutions, a forgetting-supplementation component that maintains a productive balance between exploration and exploitation, and a dream-sharing component that promotes diversity among candidate solutions. Rigorous testing across realistic hospital environments confirms MO-DOA’s outstanding effectiveness, with results showing a 21.86% gain in resource utilization, a 30.95% decrease in patient waiting times, a 19.06% boost in patient satisfaction, and a 29.56% improvement in how evenly staff workloads are distributed. The algorithm’s emergency response capabilities are especially noteworthy, achieving bed assignments within 4.23 min and an equipment deployment success rate of 94.56%. Computationally, the algorithm proves highly efficient, with an average response time of 18.87 s and strong scalability across different operational scales. Collectively, these findings position MO-DOA as a powerful and practical tool for optimizing hospital operations in real time. Full article