Search Results (5,573)

Prior studies have commonly examined organizational structure, managerial processes, construction logistics, productivity, and project complexity through separate research streams, leaving limited empirical evidence on how these domains are positioned within a single multilevel construction management framework. This study develops an integrated analysis of their relationships using data from construction firms operating in Cochabamba, Bolivia, and the Metropolitan Region of Curitiba, Brazil. A quantitative, non-experimental, cross-sectional design was adopted, and 108 valid survey responses were analyzed through descriptive statistics, correlation analysis, Exploratory Factor Analysis, Principal Component Analysis, reliability assessment, regional comparison, and Relative Importance Index ranking. The results showed that formal organizational characteristics displayed comparatively limited direct associations with performance-related variables, whereas stronger relationships were concentrated among managerial, strategic–logistical, and operational processes. The project complexity analysis retained three dimensions—technical, process-stage, and planning-related complexity—which jointly explained 61.271% of the rotated variance. At the operational level, equipment and tool maintenance, schedule adherence, and material storage received the highest Relative Importance Index values, with RII = 0.776 for each item. The findings indicate that construction performance is more closely associated with the dynamic processes through which information, decisions, responsibilities, and resources are coordinated than with formal structure considered independently. The study contributes a multilevel conceptual framework in which project complexity is interpreted as increasing the coordination requirements linking organizational arrangements, managerial processes, logistics, and operational reliability. Full article

Cost control in engineering, procurement, and construction (EPC) public works is shaped by interacting drivers, nonlinear feedback, and qualitative judgments. Existing studies usually apply the three relevant method families separately: DEMATEL-ISM maps causal structure but does not propagate hesitation-aware expert judgments into cost trajectories; fuzzy systems represent uncertainty but commonly lack a verified causal hierarchy; and system dynamics (SDs) capture dynamic accumulation but often rely on crisp inputs. The resulting absence of a traceable causal screening to uncertainty to dynamic cost link is the specific gap addressed in this study. We therefore develop a transparent three-stage pipeline combining the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL-ISM) method, triangular intuitionistic fuzzy reasoning (TIFR), and SDs. DEMATEL-ISM identifies the causal hierarchy; TIFR represents membership, non-membership, and hesitation in design complexity and human–technology synergy judgments; and SDs evaluate stage-specific cost trajectories. Recalculation from the supplied 17 × 17 direct influence matrix produced a six-level hierarchy in which senior management decision-making capability and the level of integration occupy the two deepest driving levels. For the Urumqi Civic Center case, the baseline terminal cost absolute percentage error was 0.492%. A coordinated intervention scenario shifted the simulated terminal cost by CNY 12.1243 million (6.1%) relative to the baseline; this is a model-based scenario difference, not an observed project saving. Integration had the largest simulated effects on design and transportation costs, whereas senior management decision-making capability had the largest effects on procurement and construction costs. Security cost curves showed a complementary pattern between managerial capability and workers’ professional competence, but no statistical interaction effect is claimed. The framework is intended for within-case scenario comparison and intervention prioritization; multi-project and time-series validation remains necessary. Full article

The rapid integration of solar photovoltaic (PV) generation, distributed energy resources, and advanced communication infrastructures is transforming conventional power systems into highly interconnected cyber–physical smart grids. Although this transition improves sustainability and operational flexibility, it also increases grid-management complexity and introduces cyber–physical vulnerabilities, including false data injection attacks, communication failures, equipment degradation, and renewable-induced operational instabilities. This paper presents the Digital-Twin-Enabled Resilience Framework (DTERF), a conceptual reference architecture for anomaly detection in high-PV cyber–physical smart grids. DTERF integrates heterogeneous cyber–physical data acquisition, Digital Twin-based contextual representation, machine-learning analytics, explainable decision support, adaptive operational response, continuous learning, and self-healing capabilities within a unified resilience cycle. The framework is grounded in a structured review and comparative assessment of contemporary machine-learning approaches and recent integrated smart-grid research. Its architecture is conceptually evaluated through requirements-to-architecture traceability, examining functional coverage and internal consistency across the complete operational cycle. The analysis shows that DTERF provides explicit architectural mechanisms addressing the principal requirements identified in the literature, including contextual anomaly analysis, interpretability, cybersecurity robustness, resilience support, and operational integration. Rather than proposing a new anomaly detection algorithm or claiming empirical performance superiority, DTERF provides a technology-agnostic architectural foundation for coordinating complementary capabilities required for resilient anomaly management. Future work should empirically validate the framework using Digital Twin simulation environments, representative high-PV distribution systems, cyber–physical anomaly scenarios, and real or utility-derived operational data. Full article

Electric vehicles are rapidly reshaping global transportation, emerging as a central pillar of efforts to cut greenhouse gas emissions and end dependence on fossil fuels. This review provides a critical and integrative synthesis of recent advances in electric vehicle technologies, focusing on three interconnected domains: battery innovations, charging strategies, and grid integration. Progress in high-energy-density lithium-ion chemistries, emerging solid-state and sodium-ion batteries, and advanced battery management systems is examined with respect to their implications for driving range, safety, and lifecycle sustainability. Charging infrastructure developments, including fast and ultra-fast charging, wireless charging, and battery-swapping networks, are evaluated in terms of technical feasibility, grid impact, and user adoption. The evolving role of EVs in enhancing energy system flexibility is further analyzed through vehicle-to-grid (V2G) and smart grid interactions, with emphasis on control algorithms, grid stability, and renewable energy integration. By critically analyzing recent literature, this review identifies key technological, infrastructural, and system-level challenges, as well as emerging research directions that require coordinated optimization across domains. The insights presented aim to guide future research, technology development, and policy design toward the realization of a resilient, efficient, and scalable electric mobility ecosystem. Full article

Urban Internet-of-Things (IoT) infrastructures increasingly host distributed artificial intelligence workloads across heterogeneous edge environments, supporting applications such as urban monitoring, resource optimization, and large-scale sensing analytics. Despite the rapid adoption of edge AI, current systems lack a unified architectural layer responsible for orchestrating distributed intelligence across dynamic urban infrastructures. Existing orchestration mechanisms are typically rule-based and rely on low-level telemetry signals such as latency, node availability, or network conditions, limiting their ability to interpret the contextual meaning of system behavior and environmental events. This paper introduces Urban AI OS, a trustworthy LLM-based semantic control layer for distributed urban edge intelligence. The proposed architecture transforms heterogeneous telemetry and environmental signals into high-level semantic events through large language model reasoning, enabling context-aware orchestration decisions including adaptive topology management, workload coordination, and node role assignment. To address the reliability risks associated with LLM-assisted control, Urban AI OS employs confidence-gated execution with deterministic fallback policies, post-action monitoring, rollback, and auditable decision logging to constrain the operational impact of uncertain or erroneous LLM recommendations. By decoupling semantic reasoning from the data plane execution of AI workloads, Urban AI OS provides a model-agnostic framework for managing large-scale urban edge intelligence infrastructures. Experimental evaluation on a real-world urban IoT deployment with 50+ edge devices demonstrates that the proposed semantic operating layer improves system responsiveness by 35%, reduces unnecessary topology changes by 42%, and improves communication efficiency compared to conventional rule-based and adaptive threshold baselines, while maintaining confidence calibration through formal fallback mechanisms. Full article

The building sector faces an urgent challenge in balancing carbon neutrality goals with natural resource conservation. This study constructs a three-dimensional dynamic coupling model integrating Life Cycle Assessment, carbon footprint, and emergy analysis to evaluate the sustainability of a hydrogen–solar–geothermal hybrid energy system for an ecological office building in China’s hot summer and cold winter climate zone over a twenty-year horizon. The model incorporates dynamic factors including grid decarbonization, equipment efficiency degradation, and replacement cycles to overcome the systematic bias inherent in static LCA. Results reveal a significant trade-off: the hybrid system achieves a 29.8% reduction in global warming potential with a seven-year carbon payback period, yet non-renewable resource consumption doubles and resource scarcity damage increases by 173%. The carbon payback trajectory exhibits non-monotonic fluctuation, with electrolyzer replacement in year ten generating 360 tonnes of additional emissions that nearly reset the cumulative net value to zero. Multi-objective optimization identifies photovoltaic capacity as the system baseline (170–210 kW) and electrolyzer capacity as the primary regulating variable (35–62 kW), with the TOPSIS-recommended compromise solution of 200 kW photovoltaic, 50 kW electrolyzer, 30 kW fuel cell, and 32 m³ hydrogen storage achieving annual carbon emissions of 280 tonnes and a 33.3% reduction. Carbon pricing exhibits a nonlinear leverage effect with an incentive threshold of 200 RMB per tonne, substantially above China’s current 60–80 RMB per tonne level. This study concludes that while hydrogen–solar–geothermal hybrid systems offer substantial climate benefits, their comprehensive sustainability depends on proactive management of material scarcity costs, precise planning of equipment replacement cycles, and coordinated multi-level policy instruments. The findings provide methodological foundations for transitioning building carbon neutrality assessment from static LCA to dynamic coupling frameworks and from single carbon metrics to integrated carbon-resource-cost evaluations. All quantitative results presented herein are derived from this specific case study under the stated assumptions and parameter values; generalization to other building types or climate zones requires recalibration. Full article

Beyond 5G and future 6G services require radio access networks to support heterogeneous applications with diverse latency, reliability, throughput, mobility, and computing requirements. These challenges are particularly pronounced in vehicle-to-network (V2N) communications because of high mobility, dynamic channel conditions, frequent handovers, and heterogeneous service requirements. Conventional traffic-steering methods primarily rely on radio-side indicators, while computing-resource availability and traffic-specific computation demands are often considered separately. To address this limitation, this paper proposes a coordinated communication and computing resource management framework for O-RAN-based V2N communications. The framework integrates a traffic-management rApp (TM-rApp) in the non-real-time RIC with a traffic-steering xApp (TS-xApp) in the near-real-time RIC to enable policy-based closed-loop control. Candidate cells are ranked using communication quality, computing-resource capability and availability, predicted throughput, mobility characteristics, and traffic-class priority. As a proof-of-concept supporting component, proactive throughput forecasting is evaluated using standalone LSTM and stacked ensemble (S-LSTM) models based on lagged radio, mobility, load, and throughput features. The S-LSTM provides an adaptive mechanism for combining base learners but does not achieve a statistically significant improvement over the standalone LSTM; moreover, the forecasting evaluation uses fixed, non-optimized hyperparameters and a single chronological train–test split without cross-validation. Accordingly, the prediction results are interpreted as preliminary evidence of forecasting feasibility rather than as a definitive predictive-performance contribution. The framework further incorporates O-RAN-compatible traffic-steering policies, a minimum dwell-time constraint, and priority-aware resource allocation. Evaluation using a real-world corridor based on Al Haramain Expressway Road in Jeddah and a synthetic straight-highway scenario shows that the proposed method improves SLA compliance over RSS and HHAARC, achieves the highest computing-resource satisfaction, and reduces handovers relative to RSS. The results demonstrate a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions. Full article

Wound healing is a dynamic and highly coordinated process that involves inflammation, cell proliferation, angiogenesis, re-epithelialization, extracellular matrix remodeling, and tissue maturation. The altered expression of important signaling pathways, such as transforming growth factor-β (TGF-β)/Smad, nuclear factor-κB (NF-κB), phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), mitogen-activated protein kinase (MAPK), and Wnt/β-catenin, may be responsible for slower wound healing, chronic inflammation, excessive fibrosis, and impaired tissue regeneration. Multifunctional nanomaterials are a promising strategy for tuning these highly coordinated processes due to their tunable physicochemical properties, high surface area, and the ability to deliver cargo, as well as the integration of antimicrobial, antioxidant, anti-inflammatory, and pro-angiogenic properties. The aim of current review is to summarize the potential of multifunctional nanomaterials to promote wound healing, with a focus on mechanisms of action and modulation of key cellular signaling pathways. A systematic review of the literature was conducted using PubMed, Scopus, Web of Science, and Google Scholar, searching for publications from 1996 to June 2026, and representative experimental, mechanistic, preclinical, and translational studies were critically evaluated. In this review, the authors discuss the role of nanomaterial properties, therapeutic payload, molecular targets, modulation of cellular signaling pathways, and regenerative effects. These platforms have been shown in in vitro and animal studies to influence inflammatory signaling, oxidative stress, angiogenesis, collagen remodeling, re-epithelialization, cellular proliferation, and migration. However, the modulation of these pathways are dose-responsive, time-dependent, and cell- and wound-stage-specific. Despite the promising therapeutic potential of nanomaterial-based wound care strategies, the available evidence remains predominantly preclinical, with relatively limited clinical data supporting their use in humans. Concerns regarding long-term toxicity, biodistribution, batch-to-batch reproducibility, sterilization, scalable manufacturing, regulatory approval, and commercial feasibility further challenge translation into clinical practice. Multifunctional nanomaterials may offer a promising approach for pathway-specific and multimodal wound management; however, comprehensive mechanistic studies, long-term safety and biodistribution assessments, and well-designed clinically relevant investigations are required to establish their efficacy, safety, and true translational potential. Full article

Noise mitigation on urban rail transit lines in service is often constrained by the need to maintain uninterrupted operation and avoid large-scale demolition or reconstruction. Using Kunming Metro Line 4 as an engineering case study, this study combined onboard field measurements with data-driven analysis to characterize passenger-compartment noise and identify its principal determinants. A green noise-mitigation framework integrating source control, transmission-path control, and green operational management was developed, and its benefits were quantified across four dimensions: acoustic-environment improvement, human-health protection, low-carbon energy savings, and enhanced operation and maintenance efficiency. Passenger-compartment noise levels predominantly ranged from 78 to 82 dB(A). SHapley Additive exPlanations (SHAP) analysis identified train operating speed and track gradient as the two dominant factors, with relative importance values of 35.03% and 27.56%, respectively. Engineering implementation of the framework reduced peak noise levels in curved sections by 3–8 dB(A). The results further showed that noise-pollution mitigation and carbon-emission reduction share common sources and intervention pathways. The scenario-based assessment indicated that, under the specified parameter assumptions, the relevant measures could collectively achieve an estimated annual carbon emission reduction of approximately 3353.20 tCO₂. These findings provide a practical and scientific basis for green noise mitigation and coordinated pollution and carbon reduction on urban rail transit lines in service. Full article

Distributed generation (DG) is reshaping distribution networks through bidirectional power flows, operational variability, and dependence on coordinated regulation, pricing, and control. This paper examines how regulatory architecture, grid-code requirements, tariff design, and Distributed Energy Resource Management Systems (DERMS) influence DG integration, emphasizing Honduras. A structured mixed-source review is applied, combining Scopus-based bibliometric analysis of 2438 records (2000–2026) with targeted synthesis of technical, regulatory, tariff-related, and institutional sources. The bibliometric results show sustained growth and a thematic shift from conventional voltage-control studies toward active distribution networks, DER coordination, storage, demand response, tariff reform, and digital energy management. The analytical synthesis shows that effective DG integration requires more than interconnection compliance: it depends on grid-support functions, cost-reflective and equitable tariffs, and operational tools capable of managing voltage deviations, reverse power flow, congestion, protection coordination, and limited visibility. DERMS is an enabling layer for voltage control, active and reactive power management, congestion mitigation, adaptive protection, and predictive operation. For Honduras, current regulatory progress should be complemented by phased modernization focused on observability, smart metering, data infrastructure, local flexibility, and progressive DERMS deployment. The study provides an integrated framework for aligning regulatory, economic, and operational dimensions of DG integration in emerging distribution systems. Full article

Foot-and-mouth disease (FMD) causes significant losses for livestock farmers in affected countries and regions, including Southeast Asia, where several countries are progressing through different stages of the Progressive Control Pathway (PCP) under diverse resource settings. The PCP, developed by the World Organization for Animal Health and the Food and Agriculture Organization of the United Nations, is an outcome-oriented strategy designed to support progress toward the elimination of FMD. In this review paper, we propose an evidence-based framework linking epidemiological tools to each PCP stage requirements based on studies conducted in eight Southeast Asian countries through peer-reviewed research and gray literature. The findings indicate that Stage 1 progression is supported through risk-factor analyses, seroprevalence studies, and participatory epidemiology, while Stage 2 relies on spatial analyses and evaluations of control strategies. Progression to Stage 3 requires evidence of active surveillance systems for rapid detection of new viral incursions, whereas later stages depend on risk assessment, sustained surveillance, and restricting transboundary animal movement. Regional challenges include underreporting, surveillance-system deficiencies, and inconsistent vaccination monitoring. The framework proposed herein provides a practical approach for aligning the generation of evidence with PCP requirements. Ultimately, strengthened regional collaboration that coordinates transboundary risk management is crucial for progress toward sustainable FMD control in Southeast Asia. Full article

High-voltage electric-vehicle (EV) fast charging requires accurate coordination between the off-board charger and the battery management system during voltage and current negotiation. This study evaluates a Hybrid Particle Swarm Optimization-Whale Optimization Algorithm (HPWOA) schedule for training a dual-output feedforward neural network that directly estimates ChargePower_kW and ChargeCurrent_A. Ten protocol-state and battery-condition variables were used as inputs. The 158-dimensional neural-weight vector was optimized using 75 Particle Swarm Optimization (PSO) iterations, followed by 75 Whale Optimization Algorithm (WOA) iterations. Using the supplied 500-record dataset, a reproducible 30-seed sample-level evaluation was conducted with a common 3775 fitness-function-evaluation budget for PSO, the WOA, the SFSA, and the HPWOA. The Stochastic Fractal Search Algorithm (SFSA), therefore, used 30 iterations because it evaluates five diffusion candidates per individual. The reported HPWOA mean ± standard deviation (SD) was RMSE = 4.658 ± 0.986 kW and R² = 0.843 ± 0.071 for power, and RMSE = 12.686 ± 2.687 A and R² = 0.858 ± 0.059 for current. The HPWOA outperformed the WOA and SFSA, but not standalone PSO. A conventional mini-batch Adam-trained dual-output neural network produced RMSE = 1.704 ± 0.121 kW and 4.946 ± 0.273 A, and R² = 0.980 ± 0.003 and 0.979 ± 0.002, respectively. Charger-grouped five-fold validation gave the HPWOA R² = 0.857 ± 0.045 (power) and 0.873 ± 0.048 (current). An analytical P = V × I reconstruction was physically consistent in construction and did not show a statistically significant power–RMSE difference from direct HPWOA outputs. The results, therefore, position the two-phase schedule as a reproducible comparative baseline rather than as a demonstrated replacement for gradient-based training or a physically constrained reconstruction. Full article

This research assesses the practical synthesis of Building Information Modeling (BIM), Autodesk Forma and Dalux to support LEED-oriented sustainable design for a higher education building. Autodesk Revit 2025 functioned as the central BIM platform, while Autodesk Forma enabled early-stage simulations of solar exposure, daylight potential, wind conditions, microclimate, noise and solar-energy potential. Dalux supported model coordination and information management in accordance with ISO 19650 principles. The workflow links simulation outputs to BIM elements through project-defined parameters, allowing performance evidence to inform design refinement. Quantitative indicators were consolidated for daylight exposure, wind comfort, outdoor thermal stress, acoustic exposure and photovoltaic potential. The solar-energy analysis considered an area of approximately 970 m², an annual potential of 1030 kWh/m² and a theoretical yield of approximately 999,100 kWh/year. Assuming 70% roof coverage and 18% panel efficiency, the estimated photovoltaic the expected output is approximately 125,113 kWh/year. These findings demonstrate the value of combining BIM, cloud-based analysis and CDE-based coordination for early-stage sustainable design, while LEED certification, operational energy modelling and lifecycle assessment require additional specialist validation. The proposed workflow provides a consistent approach for aligning design development with sustainability objectives and can be applied to similar building types. Full article

The use of helicopters demands very accurate management and the correct use of assets. This is crucial in achieving good performance and avoiding tasks that could be inconducive to extinguishing the fire or even cause serious accidents. In the field of civil protection, very few studies present this or a similar type of analysis for the use of helicopters. This study proposes a structured approach designed to improve the safety and operational efficiency of firefighting helicopters by integrating lessons learned from real accident case studies, supported by maintenance key performance indicators (KPI), to improve the management and use of these resources. The analyzed case studies identified recurrent operational risks associated with helicopter downwash, low-altitude operations, inadequate coordination between aerial and ground crews, and insufficient post-maintenance validation. The maintenance analysis demonstrated that an increased mean time to repair (MTTR) significantly reduces fleet availability and consequently increases reserve fleet requirements. These findings highlight the importance of integrating operational safety, maintenance management, and fleet planning to improve the reliability and readiness of aerial firefighting operations. Full article

Climate change and variability pose serious threats to natural and human systems. The Mediterranean and North Africa (MNA) are among the world’s climate change hotspots. An in-depth understanding of the decadal climate variability in this region is critical to support planning and management, as well as adaptation in important sectors such as water resources. Therefore, in this study, fifth-generation ECMWF atmospheric reanalysis (ERA5) precipitation data and the outputs of the Coordinated Regional Downscaling Experiment-COmmon Regional Experiment (CORDEX-CORE) regional models were used to characterize the decadal precipitation variability in MNA and its sub-regions (Western North Africa: WNA, Sahara: SAH, southern Mediterranean: SMED, and northern Mediterranean: NMED). The models showed overestimation in most areas and underestimation in a few areas relative to the ERA5 data, with the magnitude varying by region and season. The positive biases obtained from the regional climate models (RCMs) were higher than the positive biases obtained from the general circulation models (GCMs). The wet biases were dominant during the annual, summer, and autumn seasons over MNA and its sub-regions. Negative biases were mostly associated with GCMs, mainly HadGEM2-ES and/or NorESM1-M; meanwhile, they were linked with RCMs such as CCLM5-0-15 and/or RegCM4_v7 and were mostly obtained in winter and spring. The multi-model mean (MME) was better at reproducing the decadal precipitation patterns over MNA, SMED, and NMED at all time scales, while REMO2015-NorESM1-M and the MME performed better than the remaining models at the annual time scale over WNA and SAH. These findings are useful for improving climate modeling, the water resources management and related sectors, and climate adaptation strategies in the region, especially in North Africa. The short-period coverage of the simulated data available for this study constitutes a limitation to the findings. Full article