Search Results (5,069)

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

The decarbonization of maritime transportation requires shipboard energy systems to coordinate conventional generators, renewable energy sources, energy storage devices, and thermal energy units under voyage-dependent operating constraints. This paper develops a configurable hybrid multienergy ship system for coordinated electrical and thermal energy scheduling. The proposed framework functionally separates the propulsion subsystem from the service and thermal subsystem while retaining system-level coordination among photovoltaic generation, wind generation, diesel generators, micro gas turbines, energy storage batteries, and thermal energy units. A convolutional neural network is employed to provide short-term photovoltaic power forecasts for day-ahead scheduling. The resulting scheduling problem simultaneously considers voyage completion, power balance, equipment operating limits, ramp-rate constraints, battery charging and discharging restrictions, operating costs, and pollutant emission treatment costs. The nonlinear operating logic is reformulated as a mixed-integer optimization problem and solved using CPLEX. A representative coastal voyage case study is used to evaluate the proposed framework. The results demonstrate that the method can coordinate multiple shipboard energy sources, satisfy the prescribed electrical and thermal demands, and provide a set of Pareto-optimal solutions describing the trade-off between operating cost and emission-related cost. The proposed framework provides a system-level scheduling approach for supporting the economic and low-carbon operation of hybrid multienergy ships under increasingly stringent maritime emission reduction requirements. Full article

Microtransit services fill gaps within public transportation systems across the United States (U.S.), but there are questions about whether they complement and compete with fixed-route services. The successful integration of microtransit is important for sustainability because it has the potential to improve the ridership and viability of public transit, which has implications for reducing emissions and increasing vehicle occupancy. Moreover, microtransit may serve as a more cost-effective way to provide transit service in low-density regions, relative to fixed-route services. We analyzed survey and trip activity data from three microtransit operations in California, including the Silicon Valley Hopper (N = 457), Richmond Moves (N = 131), and the Via West Sac (N = 224). For the Bay Area systems, surveys were deployed in November 2024, while activity data spanned June 2022 to November 2024. Via West Sac is one of the oldest microtransit systems in the U.S., and data from a May 2019 survey was integrated into the analysis. The survey showed that 35% of Richmond Moves, 31% of Silicon Valley Hopper, and 17% of Via West Sac respondents connected to and/or from public transit during their most recent microtransit trip. We estimated travel and wait times were lower on microtransit than public transit for 53% of Richmond Moves, 83% of Silicon Valley Hopper, and 85% of Via West Sac trips. We also found that 27% of Richmond Moves, 37% of Silicon Valley Hopper, and 52% of Via West Sac trips had no viable fixed-route transit alternative. Insights from these findings and expert interviews were used to define planning and design recommendations to improve complementarity. Key recommendations include providing comparative travel time information between microtransit and public transit options, highlighting faster fixed-route alternatives to requested trips, and offering transfer credits for microtransit trips that connect to transit. Full article

Coupling coordination degree (CCD) between persistent late-night radiance and population density may vary nonlinearly with urban functional density, yet linear and single-city analyses cannot distinguish shared from city-specific patterns. A method-controlled dual-city XGBoost-SHAP framework was applied to 14,215 H3 cells in Shanghai and 31,067 in Beijing using six point-of-interest density factors. XGBoost outperformed OLS, with random hold-out R² values of 0.899 and 0.918 vs. 0.617 and 0.626. Public service (X2) ranked first in both cities, accounting for 39.77% and 58.73% of total mean absolute SHAP magnitude. The secondary hierarchy diverged as follows: commercial finance (X3) ranked second in Shanghai at 27.91% and formed the strongest interaction with X2, whereas transport infrastructure (X6) ranked second in Beijing at 18.63% and formed the strongest interaction with X2. Nonlinear analysis identified reproducible negative-to-positive crossings for X2, X3, and X6, peak-type responses for X4 and X5, and no stable second saturation threshold. Spatial OOF, grid, rank, LOWESS, and residual checks supported the leading-factor contrast while showing scale sensitivity and residual spatial dependence. The results identify a common leading attribution alongside city-specific secondary, nonlinear, and spatial patterns within the two observed megacities. Full article

Digital twins are becoming vital decision-making infrastructures across critical infrastructure sectors such as smart city urban services, transportation, energy, and healthcare. As digital twins become autonomous and gain real-time intervention capabilities, their governance becomes increasingly essential. Yet existing governance mechanisms remain largely procedural: they emphasize compliance without operationalizing the cognitive practices required to question assumptions, detect algorithmic harms, or support legitimate multi-actor deliberation. Drawing on a systematic scoping review, this study synthesizes the literature on digital twin autonomy, algorithmic risks, epistemic foundations, and governance mechanisms. The review reveals a fundamental gap: current governance mechanisms lack institutionalized cognitive capacities for continuous validation, proactive detection of emerging harms, and structured multi-stakeholder deliberation. This gap is corroborated by a limited but growing body of empirical studies on governance in deployed DT settings. To address this gap, the paper proposes the skeptical intelligence framework, developed through design science research. The framework integrates three cognitive functions: validation, detection, and deliberation supported by operational principles, governance artifacts, and distributed accountability roles. The framework advances digital twin governance beyond compliance toward a model rooted in critical epistemology, reflexivity, transparency, and democratic legitimacy. Consistent with design science research, the framework is delivered and evaluated at design time; empirical implementation and outcome evaluation are planned across multi-actor digital twin infrastructure contexts, including smart city governance, port logistics, and energy networks, where DT-mediated decisions redistribute opportunities and risks across heterogeneous stakeholders. Empirical validation in an operational setting is planned as the next phase of this research. Full article

Extreme weather couples spatially correlated component damage with photovoltaic (PV) derating, changing electric-vehicle (EV) demand, repair delay, and time-varying network topology. This paper develops a coordinated restoration architecture for multi-area feeders containing PV, battery energy storage, and charging stations. Its weather-facing layer constructs joint outage-risk, renewable-error, charging-demand, and voltage-vulnerability descriptors. Those descriptors parameterize a two-stage mixed-integer second-order-cone program with a finite-support optimal-transport ambiguity set that remains well defined for discontinuous mixed-integer recourse. Regional actor–critic agents propose five-minute corrections around the hourly robust schedule; constrained projection, non-linear AC power-flow screening, emergency fallback, and margin-tightened re-optimization retain the authority to accept or reject each proposal. The evaluation uses public 33-node and 123-node feeders together with synthetic 240-node and 850-node stress networks. A pre-fit manifest allocates 240 records to training, 80 to validation, and 320 to final testing, while aggregate operational outcomes cover 50 random streams. Within this controlled benchmark, accepted schedules restore 93.6% of critical-load energy (SD 2.1 percentage points), serve 96.7% of total demand (SD 1.8 percentage points), retain 82–86% of EV service across hazard classes, and reduce the modeled 24 h objective by 25.8% relative to deterministic dispatch. The full pipeline records two to four candidate-stage voltage-limit events by hazard, and 4.9% of candidates undergo tightened re-optimization before accepted schedules reach zero reported AC voltage-limit violations. Between-method comparisons are descriptive and unpaired; the larger synthetic cases are structural stress tests rather than feeder-transfer tests. Full article

As the primary carrier for oil and gas transportation, pipelines are critical for the entire industry. Pipelines are continuously subjected to corrosion and abrasion in the oil and gas delivery process, leading to gradual wall thickness reduction, shortened service life, and deteriorated operational safety. Ultrasonic testing has been widely adopted for monitoring pipeline wall thickness. Conventional ultrasonic transducers possess rigid configurations, which hinder large-area inspection and exhibit poor adaptability to complex curved components. In contrast, flexible ultrasonic sensors show prominent advantages, with their small size, light weight, and excellent conformal contact with curved surfaces. Flexible piezoelectric thin-film sensors have been used in a wide range of fields. As one of the most representative piezoelectric polymers, poly(vinylidene fluoride–trifluoroethylene) (P(VDF-TrFE)) combines favorable piezoelectric coefficients and intrinsic flexibility, making it popular. Some research groups have investigated the influences of modified filler particles, doping ratios, and fabrication process optimization on the performance of P(VDF-TrFE)-based piezoelectric composites, while others have concentrated on the practical applications of existing flexible piezoelectric sensors. This study emphasizes a rapid customized fabrication strategy for flexible sensors instead of single-specification standardized probes; hence, it does not share the same comparison benchmark as conventional fixed-dimension sensors. Systematic research on flexible piezoelectric thin-film sensors is presented, including piezoelectric material modification, substrate design, laminated structural design, fabrication workflows, establishment of the testing platform, and the development of matched circuit systems. The material preparation and manufacturing processes are optimized, and a scalable technical route for fabricating flexible piezoelectric sensors is proposed. Using this route, flexible piezoelectric thin-film sensors can be rapidly tailored for different application scenarios to satisfy diverse engineering demands. Multiple experiments were conducted on pipeline samples with varying wall thicknesses and curvatures. The results verify that the sensor reaches a measurement precision of 0.01 mm, meeting the demands of high-precision pipeline structural health monitoring. Full article

Rapid urban expansion in arid intermediate cities often produces fragmented urban structures that exacerbate inequalities in public transport accessibility and hinder sustainable mobility. However, the relationships between urban fragmentation, transport accessibility, and travel behavior remain insufficiently explored in pre-Saharan cities. This study investigates these interactions through the case of El Bayadh, Algeria, using an integrated methodology combining urban morphological analysis, Geographic Information Systems (GIS), a household survey of 577 households, traffic counts, institutional interviews, and spatial equality assessment based on the Gini coefficient. Public transport accessibility was evaluated using a 300 m walking threshold, complemented by sensitivity analyses at 400 m and 500 m. The results indicate that approximately 65% of the population lives within 300 m of a bus stop, although coverage is strongly concentrated in central districts. A Gini coefficient of 0.41, provided by the El Bayadh Directorate of Transport and computed across 20 urban zones, indicates moderate-to-high spatial inequalities in accessibility that persist despite increasing the service threshold, suggesting that these disparities are more closely associated with urban fragmentation than with the specific walking-distance threshold used to define service coverage. The transport network comprises 11 bus routes, approximately 160 bus stops, and 90 km of routes; however, limited service quality and uneven spatial distribution contribute to a high dependence on private transport, with 45% of trips made by private car, 35% by taxi, and only 20% by bus. These findings are consistent with accessibility inequalities arising from the mismatch between fragmented urban morphology, centralized urban functions, and the configuration of the public transport network. The proposed framework provides transferable insights for planning more equitable and sustainable mobility systems in rapidly urbanizing arid intermediate cities. Full article

Background/Objectives: Structural inequalities in access to healthcare persist even within systems that have achieved near-universal coverage, reflecting the enduring influence of social determinants of health on service utilization. This study examines horizontal inequity in rehabilitation and specialist care in Türkiye and investigates whether access inequality mediates the well-documented income–depression gradient. Methods: Analyzing the nationally representative 2022 Türkiye Health Survey (adults aged ≥15; N = 22,742), we employed Latent Profile Analysis (LPA) to construct people-centered, multidimensional bodily burden profiles, and assessed need-adjusted access using survey-weighted logistic regression, Erreygers-corrected concentration-index decomposition, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), and restricted cubic splines, with measurement-invariance and classification-uncertainty sensitivity analyses. Statistical mediation was examined with natural-effect models and E-value sensitivity analysis. Results: Although the system demonstrated responsiveness to need—78.8% of the highest-burden profile accessed specialist services—only 13.7% of this same group reached dedicated physiotherapy or rehabilitation, revealing a profound structural bottleneck in care coordination for marginalized populations with the greatest functional impairment. A persistent pro-rich gradient was confirmed by an Erreygers-corrected concentration index of 0.058 (95% CI 0.043–0.072), driven additively by income and education. Access did not mediate the income–depression pathway (natural indirect effect OR 1.001, 95% CI 1.0003–1.002). Conclusions: The mental health burden of low income operates through pathways that equitable healthcare access alone cannot address. These findings call for macroeconomic and people-centered health system reforms—including direct physiotherapy access, transportation subsidies, and social protection interventions—to advance health equity in rehabilitation utilization. Full article

East Asia is both the densest subsystem of the global liner shipping network and the home basin of its signature hazard, the typhoon. This study quantifies the relationship in both directions, as a stress test of the sustainability of maritime connectivity, across Alphaliner-based reconstructions of the 2017 and 2021 networks that cover every port with at least one scheduled liner service. A Typhoon Vulnerability Index built from validated IBTrACS exposure, betweenness sensitivity, and national adaptive capacity scores 330 and 272 affected ports. The risk geography anchors in East Asia in both years and more strongly in the second, as the regional share of affected ports is 29 percent in 2017 and 44 percent in 2021, the 2017 top five are all East Asian, and the 2021 top four are all Chinese, led by Shanghai. Removing every indexed typhoon port in descending risk order destroys 47.5 and 41.3 percent of baseline efficiency. Equal-sized random removals destroy a similar share at the end, so the information of the hazard ordering lies earlier and elsewhere, in early losses that run about forty percent above the random expectation, in seven of the ten earliest failures being invisible to degree screening, and in the identity of the removed ports, while equal-depth-degree targeting destroys far more at every stage. An East Asia-only attack reproduces a third of the full attack’s damage in 2017 and half in 2021. Under one fixed baseline, East Asia carries 23.8 and 24.6 percent of global efficiency before the attack and its survivors retain 7.1 and 3.6 percent after it. On the evidence of these two years, the typhoon corridor is a jointly held systemic asset, its protection is a problem shared by the three economies, and the bidirectional accounting gives sustainable maritime transport a measurable resilience baseline under a warming climate. Full article

Mobility as a service (MaaS) has emerged as a means to promote multimodal public transport and shared mobility trips, with the “Metro+” integrated mobility approach being its primary form. However, it has recently witnessed major global providers’ bankruptcies. Therefore, this study aims to identify factors driving users’ substitution of ride-hailing by “Metro+” MaaS intermodal trips. Specifically, we took ride-hailing trips as the baseline and constructed a multinomial logit (MNL) model incorporating travel-scenario factors, including trip purpose, weather, and urgency. The results show that travel-scenario factors significantly affected “Metro+” MaaS intermodal trip adoption for urban medium–long-distance trips. Users preferred MaaS intermodal trips for long-distance trips amid clear weather and no time pressure, and they favored ride-hailing in extreme weather conditions or for time-sensitive trips. Therefore, MaaS providers should emphasize travel scenarios in their marketing messaging. Finally, marginal rate of substitution (MRS) and elasticity analyses were conducted, and several strategies were proposed: (1) increasing ride-hailing availability; (2) improving transfer facilities and conditions; and (3) implementing dynamic demand matching. The study facilitates the identification of market opportunities for MaaS instead of car usage, contributes to MaaS marketing and service strategy optimization, and promotes sustainable urban transportation system development. Full article

Amid the well-researched negative effects of road congestion and increased private car use, there is a need for more sustainable modes of transport. The literature points towards trains as being a vital part of the solution to the current problems, but their success depends on a number of variables, especially when it comes to the main railway stations in the largest cities. The first goal of this study was to identify the relationship between passenger numbers at major railway stations in Poland and selected parameters characterizing public transport services; the second was to assess the usefulness of the NOAH (Nest of Apes Heuristic) method for data analysis. In Poland, the number of major transfer hubs is limited, and there is a lack of an existing method allowing comparison of variables in such small datasets in a way that infers statistical significance. This is a research gap that the authors aimed to address using the NOAH algorithm combined with an analysis of regression. The initial dataset had been successfully expanded in a way that dependencies could be observed, with both goals being met. Passenger numbers relied most on the number of trains departing at each station daily, while walking distance during transfers impacted that number most negatively. The results point towards other variables influencing the passenger numbers, which were not considered in this study but could form the basis of further research. The utilized method could also be applied to a different group of cities, and in other countries. Additionally, the study added to the development of the NOAH algorithm itself, improving the method. Full article

With urban rail transit networks gradually entering a mature stage of development, whether newly added lines and stations can still improve local labour market outcomes remains an important question in transport planning and urban regeneration research. This paper examines light rail transit expansion in three mature UK cities: Manchester, Sheffield, and Nottingham. Using MSOA-level panel data from 2013 to 2024, this study applies a Difference-in-Differences (DID) approach to evaluate the impact of light rail transit expansion on local labour market vulnerability. MSOAs intersecting with the 500-m buffer zones of newly opened light rail stations are defined as the treatment group, while MSOAs not directly affected by light rail expansion are used as the control group. The Claimant Count is used as a proxy for welfare dependency and labour-market vulnerability. The results show that light rail transit expansion significantly reduces the Claimant Count in treated areas, with an estimated decline of approximately 7.52–7.56%. This finding remains robust after adding demographic, socio-economic, and built environment controls, applying MSOA-level clustered standard errors, implementing PSM-DID, and conducting placebo tests. Mechanism analysis further shows that light rail expansion is associated with changes in the functional structure of station areas, including increases in overall POI density and service-oriented facilities, while manufacturing and production-related facilities decline. These findings suggest that light rail expansion may reduce local unemployment-related welfare dependency by improving employment accessibility, alleviating spatial mismatch, and promoting service-sector agglomeration. This study provides new empirical evidence on the socio-economic effects of light rail expansion in mature UK cities. Full article

Background: Work-related fatigue is a preventable occupational health concern in high-intensity manufacturing environments. However, supervisors and occupational health services often lack transparent criteria to support the preventive review of overtime practices. This study developed and internally validated an explainable human-in-the-loop concurrent screening model to identify employees with currently elevated work-related fatigue using routinely collected working-hour records and occupational health examination data. Materials and Methods: De-identified administrative and occupational health data from 314 full-time manufacturing employees were linked with standardized fatigue assessment data. Currently elevated work-related fatigue was defined using the top quartile of the standardized work-related fatigue subscale. To reduce information leakage, the work-related fatigue score used to define the outcome was excluded from the predictor sets. Multivariable logistic regression and XGBoost were compared using nested stratified five-fold cross-validation, with aggregated out-of-fold predictions from the outer folds used for the performance evaluation. Model performance was assessed using discrimination, probabilistic accuracy, calibration, and classification metrics at provisional, study-specific thresholds. Results: XGBoost demonstrated greater discrimination and better overall probabilistic accuracy than logistic regression, with an AUC of 0.77 versus 0.68 and a Brier score of 0.14 versus 0.17, respectively. At the provisional study-specific threshold of τ1 = 0.28, XGBoost achieved a sensitivity of 0.78 and a specificity of 0.70. Using τ1 = 0.28 and τ2 = 0.55, 58.0%, 27.1%, and 15.0% of employees were classified into provisional low-, moderate-, and high-priority tiers, respectively. The observed prevalence of currently elevated work-related fatigue increased across these tiers from 9.3% to 28.2% and 78.7%, respectively. Calibration assessment indicated systematic miscalibration for both XGBoost (intercept α = 0.78, slope β = 1.64) and logistic regression (α = −0.49, β = 0.56). Because τ1 and τ2 were selected and evaluated using aggregated out-of-fold predictions generated from the same single-organization development dataset, their stability and transportability are unknown. Medium-term cumulative overtime, particularly during months −2 to −6, together with routinely measured indicators of vulnerability and recovery, contributed substantially to the model-estimated probability of currently elevated fatigue. Conclusions: Routinely collected working-hour and occupational health examination data may support explainable concurrent screening for currently elevated work-related fatigue and illustrate a potential framework for differentiated preventive reviews. However, the proposed thresholds and review-priority tiers are provisional and study-specific and should not be interpreted as implementation-ready decision rules for occupational health practices. Prospective temporal validation, external validation in independent organizations, and local recalibration are required before threshold-based implementation. Full article

The popularity of electric vehicles (EVs) is growing at a fast pace, creating a need for the strategic deployment of charging stations (CSs) to provide enough coverage, cost effectiveness, and compliance with grid and urban planning regulations. The deployment of large-scale infrastructure under multiple, often conflicting constraints remains a challenging engineering decision-making problem. In this paper, we propose a hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem. The proposed approach employs Q-learning and Deep Q-Network (DQN) agents to iteratively refine the initial deployment while simultaneously optimizing deployment cost, charging demand coverage, and operational utility under practical planning constraints. The constraints include grid capacity limitations, renewable energy utilization, and fairness considerations. The proposed framework is evaluated in realistic urban scenarios. The experimental results demonstrate that the reinforcement learning (RL) approach achieves superior trade-offs among competing objectives compared to baseline heuristic strategies, while maintaining computational scalability for large candidate location sets. The proposed framework demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems. The proposed methodology is scalable to other complex engineering planning and resource allocation problems characterized by multi-objective trade-offs and dynamic constraints. Beyond improving optimization performance, the proposed framework contributes to sustainable transportation planning by supporting the efficient deployment of electric vehicle charging infrastructure. Optimized charging station placement promotes greater accessibility to charging services, encourages electric vehicle adoption, reduces unnecessary travel associated with charging activities, and contributes to lower greenhouse gas emissions. Consequently, the proposed methodology provides decision-makers with a scalable and intelligent planning tool that supports the transition toward more sustainable and energy-efficient urban mobility systems. Full article