Search Results (3,603)

Assessing the sustainability of urban development, including road changes, is increasing from year to year and requires clear indicators for robust decision-making tools to gain knowledge across regions. This study conducts the selection of transportation routes over a longer period as an example to evaluate the sustainability of historical official routes in achieving economically cost-efficient operation and maintenance. Official ways in the Qiantang River Basin connected the Jiangnan region, the economic center of China, with surrounding provinces were assessed. During the past six hundred years, the official road network in this area gradually simplified, evolving from valley roads to river banks, which covered longer distances. However, this transformation lacks a systematic explanation. By applying the analytic hierarchy process (AHP) with geographic information system (GIS), quantitative analysis was gained and the results are as follows: (1) Among the influencing factors, the weights of transportation cost and population related to economic needs are 39.54% and 29.52% respectively, with a combined total of 69.06%. (2) The official road network is often designed for governing the people, but in places such as the Qiantang River Basin, economic logic superseded political imperatives, becoming the dominant factor in reshaping the official ways. (3) In the pre-industrial era characterized by limited technological capacity, the physical environment had a greater impact on economic costs, ultimately reshaping the spatial configuration of official route networks. Overall, the evolution of official routes reflects the decline in their military-political function, driven by sustained peace and long-term decline in strategic position. The evolution of the official ways in the Qiantang River Basin reveals the importance of economic benefits in road selection. Full article

As global super-large open-pit mines expand in scale and extraction depth, conventional single-stage planning cannot meet the combined demands of productivity and resource recovery, making the shift to underground block caving inevitable. This study outlines the systemic challenges of block-scale extraction and the rationale for adopting multi-level spatial design decision-making. Four core model categories are briefly proposed: ultimate pit limit optimization, gravity flow simulation for draw strategy, long-term production scheduling for large-scale computation, and probabilistic frameworks addressing geological and market uncertainty. A Bayesian network-based block decision model is then proposed and decoupled into three physical decision tiers. The first tier incorporates energy prices, transport costs, and ore prices to establish an economic boundary rating robust to market volatility. The second tier aggregates mining units with discrete-event perturbations to produce a reliability-oriented production rating. The third tier integrates rock mechanics parameters with in situ monitoring data to derive a physics-informed safety rating. The three ratings are synthesized via Bayesian inference and evaluated within a multi-attribute utility function encompassing net present value, safety index, downside risk, and information risk. A feedback module quantifies the economic benefit of uncertainty reduction, yielding a closed-loop intelligent system spanning macroeconomic boundary definition to operational safety alerting. Finally, the main conclusion of this study is that integrating macro-economic volatility with rock mechanics through a dynamic Bayesian framework is essential for managing the open-pit to underground transition. The results indicate that leveraging the Value of Information for real-time risk diagnosis significantly reduces conservative design losses, providing a quantifiable and robust decision-making paradigm for super-large mining systems. Full article

Accurate forecasting of high-dimensional meteorological fields remains challenging due to the complex spatio-temporal dynamics of atmospheric systems and the presence of heterogeneous training difficulty across space and lead time. Existing deep forecasting approaches usually optimize all prediction units uniformly, which may overemphasize low-benefit or weakly generalizable supervision signals. To address this issue, we propose Spatio-Temporal Selective Learning (ST-SL), an online training framework that estimates the learnability of each prediction unit by comparing the main model with a frozen reference model and computes the loss only over selected high-benefit spatio-temporal units. To provide an effective forecasting backbone, we further introduce VASTFormer, a variable-aware spatio-temporal Transformer that models cross-variable dependencies, incorporates physics-enhanced Solar Positional Encoding, and captures atmospheric trajectories with an efficient temporal translator. Experiments on the ERA5 reanalysis dataset show that VASTFormer outperforms representative spatio-temporal baselines, while ST-SL further improves accuracy without adding inference-time parameters or computational cost. Compared with the strongest baseline, VASTFormer+ST-SL reduces MSE, MAE, and RMSE by 8.84%, 6.70%, and 4.54%, respectively. Meteorological skill evaluation further shows an average ACC of 0.9801 and RMSESS of 0.8104, and percentile-based extreme-condition evaluations confirm consistent improvements across standard and high-impact forecasting scenarios. These results indicate that selective supervision can improve generalization in dense meteorological forecasting. Full article

Model predictive control enhances building energy performance; however, its reliability is highly dependent on the robustness of internal prediction models under severe operating conditions. To address this, a risk-aware model-then-control (RAMC) training framework is proposed in this study. This approach augments conventional prediction loss with a conditional value-at-risk (CVaR) penalty on operational costs under perturbed inputs, embedding tail-risk awareness directly into the prediction model. The framework is trained via standard backpropagation, avoiding the computational burden of differentiating through the controller. The proposed methodology is evaluated on a simulated commercial building equipped with a hydronic heating system under three weather scenarios. Compared to a standard fidelity-trained baseline, the strongest risk-aware configuration reduced occupied cold degree-hours by 22–26% and peak cold violations by 14–27%, demonstrating the greatest benefit under forecast bias. These comfort improvements were achieved alongside a 17–31% increase in weekly heating energy consumption. The results indicate that embedding tail-risk awareness into model training improves closed-loop comfort robustness relative to standard accuracy-based training. An ablation study attributes this improvement directly to the CVaR tail term, while the risk weight formalizes a tunable energy–comfort trade-off dictated by operational priorities. revtwogreenIn this case study, a fixed setpoint-margin baseline reached comparable cold protection at lower energy; the distinct contribution of RAMC is that it relocates a tunable tail-risk preference into the prediction model itself, leaving the downstream controller unchanged. Full article

Reliable and scalable landmine detection technologies are essential for humanitarian mine action (HMA), yet standardized benchmarks for Unmanned Aerial Vehicle (UAV)-based sensing in operationally relevant environments remain limited. This study presents a comprehensive evaluation of 34 multimodal datasets acquired over a standardized seeded test site for landmine and unexploded ordnance detection. Nine sensing modalities, including RGB, thermal, multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar (SAR), are evaluated using the Anomaly, Identifiable Anomaly, Unique Identifiable Anomaly (AIU) index to establish a unified framework for quantifying detection fidelity. Results indicate that RGB imagery achieves the highest surface detection rate (94.8%), with 45.4% of targets classified as uniquely identifiable, reducing false-positive risk. For sub-surface detection, handheld electromagnetic induction (EMI) and magnetometry exceed 95% detection for ferrous items but fall below 10% for plastic ordnance. Ground-penetrating radar (GPR) is the only modality capable of detecting buried plastic targets (55.6% for cart-based systems), whereas UAV-mounted GPR remains limited (18.2%) at current operational flight heights. Based on the comparative analysis, we discuss the gaps in current detection capabilities, compare false-positive rates across modalities, and perform a cost–benefit analysis fitting contamination scenarios with the most cost-effective detection method. All datasets are publicly released, along with an interactive web-map, to support reproducible benchmarking and cross-modality comparison in UAV-enabled explosive hazard detection. Full article

Used engine oil (UEO) is a hazardous waste stream that poses significant environmental risks when improperly managed. However, its high heating value makes it a promising candidate for energy recovery. In Kuwait, rising vehicle ownership has led to increasing quantities of UEO, while the power sector remains heavily dependent on conventional fossil fuels. Although extensive research has examined UEO treatment methods and combustion characteristics, limited attention has been given to its integration into utility-scale power-generation systems. This study presents a national-scale techno-economic and environmental assessment of using UEO as a supplementary fuel for electricity generation in Kuwait. East Doha Power Station was selected as a representative case study to evaluate fuel-substitution potential and the practicality of integrating UEO into existing power-generation infrastructure. Historical vehicle-registration data were used to estimate UEO generation, and future availability was projected through 2035 based on vehicle-growth trends. The corresponding thermal energy potential, equivalent electricity generation, fuel-displacement capacity, economic benefits, and environmental impacts were subsequently evaluated. The results indicate that annual UEO generation is projected to increase from approximately 181,800 tonnes/year in 2024 to 303,300 tonnes/year in 2035. This quantity corresponds to about 12,126 TJ/year of recoverable thermal energy and an equivalent electricity-generation potential of approximately 1.1 TWh/year (4000 TJ/year), assuming a power-plant efficiency of 33%. The recovered UEO could displace approximately 311,000 tonnes/year of heavy oil or 287,000 tonnes/year of crude oil, with estimated net annual fuel-cost savings of approximately 28–30 million KD. Based on literature-reported emission factors, UEO utilization could reduce combustion-related CO₂ emissions by up to 19.0% and NO_x emissions by up to 45.5% compared with heavy oil. Sensitivity analysis further confirmed the robustness of the findings under a range of recovery and operating conditions. To the best of the authors’ knowledge, this study represents the first comprehensive national-scale assessment of the potential use of UEO for utility-scale power generation in Kuwait. The findings indicate that UEO has the potential to serve as a strategic secondary energy resource that supports waste reduction, fuel conservation, economic savings, and circular-economy objectives. However, practical implementation will require appropriate collection and treatment infrastructure together with further technical validation, pilot-scale demonstration, and regulatory evaluation. Full article

Background. Orthotic and prosthetic devices for general-use and activity-specific function can provide critical preventive health benefits for individuals with limb loss, limb difference, and mobility impairments, and yet coverage remains inconsistent across U.S. states. Objective. To evaluate the fiscal impact of clarifying insurance coverage for orthotic and prosthetic devices across 23 states lacking comprehensive coverage. Methods. A cost consequence analysis was conducted using data from the U.S. Census Bureau, Kaiser Family Foundation, Government Accountability Office, and a recent actuarial analysis informing baseline cost, coverage, and prevalence assumptions. Per-member-per-month (PMPM) cost increases were compared against device enabled preventive health savings to estimate net fiscal impact. Sensitivity analyses modeled three scenarios based upon a combination of uptake (% eligible individuals accessing device) and physical activity equivalent annual cost saving, respectively: conservative (25% uptake, $1000), moderate (50% uptake, $2500), and high-impact (75% uptake, $5000). Return on investment (ROI) was calculated for the moderate scenario as the ratio of annual savings to implementation cost. Results. Under the assumptions of the moderate scenario, projected ROI remained positive across all states, ranging from approximately 1.5× in Florida to over 114× in Vermont, with 78% of states (18 of 23 states) demonstrating returns greater than 4×. Moderate scenario annual net savings ranged from approximately $10.8 million in Vermont to $437.0 million in California, with substantial projected savings also observed in Florida ($235.5 million), New York ($225.2 million), and Virginia ($143.8 million). PMPM cost increases for 70% of states range between $0.03 and $0.43, with all modeled states remaining below $1.46. Discussion. In our healthcare system dominated by high-cost and reactive care, the ROI obtained by this cost-consequence analysis (CCA) using evidence-based assumptions supports orthotic and prosthetic coverage clarification as preventive interventions to restore function. Full article

The plastics industry sector is a massive contributor to greenhouse gas emissions. In this context, it is important to find alternatives to valorise plastic polymer waste, since 63.0% of the plastics produced between 1950 and 2015 were incinerated or disposed of in landfills. This study aims to evaluate the environmental and economic performance of a polymeric control panel for a domestic boiler. The environmental assessment was conducted using the Life Cycle Assessment (LCA) methodology from a cradle-to-grave perspective, allowing the identification of the hotspots of the panel under analysis in two scenarios: virgin panel (VP) and recycled panel (RP). The economic evaluation was performed through a techno-economic analysis (TEA) considering both operating expenditures (OpEx) and annualised capital expenditures (CapEx) allocated to the functional unit. The VP scenario used 100.0% virgin polymer, while the RP scenario used 70.0% virgin polymer and 30.0% internal recycled polymer. The analysis shows a clear synergy: substituting a portion of virgin polymer with recycled PC/ABS reduces both environmental impacts and production costs, while also increasing the sustainability. The results support internal recycling as a practical circularity strategy that can improve environmental performance. The RP scenario is both the environmentally preferable and the economically better option. Additionally, the consistency of results across both LCA and TEA indicates that the identified hotspots represent leverage points for future interventions to amplify benefits to further improve sustainability. For instance, further decarbonization of the Portuguese electricity grid or increased reliance on on-site PV electricity would strengthen the environmental profile of both scenarios. At the same time, continued optimisation of recycling processes could enhance cost savings. Full article

The Milano Cortina 2026 Winter Olympic Games represent a significant departure from traditional mega-event models due to their markedly polycentric territorial structure. This study investigates the sustainability of this “decentralized” model by analyzing the environmental impact of mobility flows across a vast geographic area. Adopting a methodological approach, the research integrates historical attendance data from previous Winter Games with official projections and travel time simulations to model the event’s carbon footprint. Specifically, the framework quantifies gas emissions by categorizing mobility flows into external international travel and internal inter-cluster transit. The analysis highlights a significant discrepancy between the stated sustainability objectives and the actual implementation of the infrastructural plan. Findings reveal that the total carbon debt is heavily driven by international travel, yet the localized impact on Alpine clusters remains critical due to a persistent reliance on road infrastructure over rail systems. The results suggest a “paradox of decentralized sustainability”, where the benefits of reusing existing sporting venues are offset by the environmental costs of connecting geographically fragmented sites. We conclude that without a robust and efficient public transport network, territorial dispersion acts as a catalyst for widespread anthropogenic pressure on fragile mountain ecosystems, challenging the long-term ecological legacy of the event. By empirically exposing these dynamics, this study offers a novel evaluative framework for assessing the true sustainability of distributed governance in future mega-events. Full article

Background/Objectives: Middle-income countries (MICs) in the Middle East and North Africa (MENA) face financial and health system barriers when introducing new vaccines. The Gavi MICs approach has supported the introduction of pneumococcal conjugate (PCV), human papillomavirus (HPV), and rotavirus (RV) vaccines; however, economic evidence from the region remains limited. This systematic review assessed the quantity, characteristics, and quality of economic evaluations of these vaccines in MENA MICs published between 2015 and 2025 and synthesised economic evidence to inform policy decisions in Algeria, Egypt, Iran, Jordan, Lebanon, Morocco, Palestine, and Tunisia. Methods: Relevant databases and registries were searched for cost–effectiveness, cost–utility, cost–benefit, and budget impact analyses of PCV, HPV, and RV vaccination strategies. Two reviewers independently screened studies, extracted data, and assessed methodological quality. Results: Twenty-six studies met the inclusion criteria, including 12 on HPV, nine on RV, and five on PCV. Vaccine introduction was the most commonly evaluated intervention (n = 23), and most studies were cost–effectiveness or cost–utility analyses adopting payer, health system, societal, or mixed perspectives. PCV and RV introduction were consistently found to be cost-effective or cost-saving. HPV introduction showed mixed results, particularly in Iran, but was generally cost-effective in Tunisia and Morocco. Reporting of vaccine coverage, delivery costs, and programmatic constraints was limited, and overall methodological quality varied. Conclusions: Available evidence supports the economic value of PCV and RV introduction in MENA MICs, while HPV’s cost-effectiveness is context dependent. Future evaluations should incorporate dynamic modelling, implementation costs, and affordability considerations to better inform sustainable vaccine introduction. Full article

This theory-oriented, methodological and conceptual survey explores the application of Multi-Criteria Decision-Making (MCDM) methods in vehicle routing, transportation and robot navigation. Multi-criteria decision aid brings significant added value in applications where robots and autonomous agents execute missions with multiple objectives in multi-cost and multi-criteria environments. Logistic operations and multi-modal transportation tasks, where multiple factors influence mission success, also benefit from multi-criteria decision aid. Decisions are inherently complex, and MCDM methods capture certain aspects of the decision contexts as they inherently encode different contextual priorities. An evaluation on the effectiveness of MCDM methods may prove to be uninformative without information on contextual preferences. Direct MCDM method comparison may fail to reveal insight over method effectiveness without contextual information. The survey presents an interpretive synthesis over decision modeling constructs and introduces a new context-oriented analytical synthesizing perspective to establish a meaningful base for comparison. Through conceptual abstraction over decision roles, latent decision structures and recurring decision patterns, two stable explanatory constructs emerged: ‘governing aspects’and ‘application attitudes’. Governing aspects characterize decision influences, whereas application attitudes characterize decision architectures. We analyze attitudes pertaining to different application domains from the perspective of responsiveness and computation demand, and discuss some key governing aspects, such as robust decision-making and behavior elicitation. We aim to provide a rich landscape of multi-criteria decision scenarios, and identify future research areas based on our findings. The outlined synthesizing framework functions both as a methodological taxonomy and a conceptual compass and case repository for navigating MCDM applications. Full article

Integrating energy storage and hydrogen energy into wind–solar–hydrogen storage microgrids can effectively mitigate the intermittency, volatility, and unpredictability of renewable energy. Aiming at tackling excessively idealized traditional models, this paper establishes a system model for wind–solar–hydrogen storage microgrids by adopting real-world environmental parameters such as wind speed and solar irradiance. To improve the operational performance of the system, an optimal microgrid scheduling method is proposed, which adopts the minimum daily comprehensive operating cost as its core objective. The wind and solar curtailment penalty terms are embedded into the objective function to quantify energy efficiency requirements, and hard constraints including power balance, ramp rate of hydrogen power production, and state of charge (SOC) of energy storage are introduced to guarantee system stability. An improved sparrow search algorithm (ISSA) incorporating a sine function and a golden ratio coefficient is adopted to optimize the position updating process. The objective function is solved so that it satisfies all of the system’s constraints, and the algorithm’s performance is evaluated based on typical daily simulation data. The results demonstrate that the proposed algorithm can effectively optimize the scheduling process and improve the economic efficiency, stability, and overall performance of the system. The total economic benefit generated by the system is increased by 8.48%, which constitutes a remarkable enhancement of the system’s economic efficiency. Full article

E-commerce platforms increasingly rely on recommendation systems whose internal logic is often opaque, making explanation design important for consumer evaluation. Drawing on procedural justice theory, this study examines whether process-based explanations function as procedural justice cues in e-commerce recommendations and how they relate to algorithmic trust and continuance intention. In a between-subjects online experiment with 394 Chinese consumers (197 per condition), participants received either an outcome-based recommendation or a process-disclosure package that disclosed data inputs and reasoning and therefore bundled procedural content with greater specificity and informational richness. Relative to outcome-based explanations, this package increased perceived procedural justice and was associated with higher trust in the algorithm and greater continuance intention. Perceived procedural justice and trust formed a theoretically ordered indirect pathway, but this ordering should be read as theory-grounded rather than causally established because the mediators and outcome were measured contemporaneously. Exploratory moderation analyses suggested that responsiveness to process-based explanations reflected broader self-reported digital interpretive capacity rather than algorithm-specific literacy alone. Robustness checks further indicated that the procedural justice pathway was not eliminated by explanation clarity, cognitive load, scenario realism, product attractiveness, or privacy intrusiveness. The findings position process-disclosure packages as practical transparency tools while cautioning that their benefits depend on consumers’ interpretive capacity and processing costs. Full article

With the growing adoption of electric vehicles, the proper siting of electric vehicle charging stations (EVCSs) has become a critical issue in urban transportation planning. This study addresses the EVCS siting problem in Kuwait through a spatial decision-making approach. A total of 25 spatial criteria covering transportation, land use, environmental conditions, and energy infrastructure were evaluated. Criterion weights were calculated from expert judgments using the Fuzzy SIWEC and SWARA methods. The results showed a high level of consistency between the two weighting methods, with a Spearman rank correlation coefficient of ρ = 0.9090 and a Pearson correlation coefficient of r = 0.8376. The final weights indicated that tourism, culture, and entertainment areas (C2.3, 0.05046), parking areas (C1.3, 0.04910), road accessibility (C1.4, 0.04813), and retail and dining areas (C2.6 to C2.7, 0.04708 to 0.04757) were the most influential factors in EVCS planning. All criteria were spatially represented in a geographic information systems environment, normalized to the [0–1] range according to their benefit and cost directions, and integrated through weighted overlay analysis to produce a continuous EVCS suitability map. Based on this suitability surface, 133 candidate EVCS alternatives were assigned to areas with relatively high suitability values and active urban land-use characteristics. The extracted raster suitability values of these candidate alternatives ranged approximately between 0.640 and 0.860, indicating that the assigned points were concentrated in spatially favorable areas rather than being randomly distributed. The ranking results obtained from TOPSIS and VIKOR showed that the top six alternatives were identical in both methods, and alternative A123 ranked first with a VIKOR value of 0.007548 and a TOPSIS value of 0.884213. Sensitivity analysis showed that changes in criterion weights affected suitability values and transition zones, while the overall spatial pattern of highly suitable areas remained stable. The findings suggest that the proposed GIS-MCDM framework provides a practical preliminary decision-support basis for spatial screening and investment prioritization in EVCS planning, particularly in hot-climate cities. Full article

This study investigates the combined use of fly ash (FA) and plastic waste (PW) as partial replacements for cement and coarse aggregates in the production of paver blocks. Experimental mixes were developed with a substitution level of FA (10% to 30%) and PW (3% to 15%). The performance of the modified concrete block was evaluated in terms of compressive strength (CS), flexural strength (FS), ultrasonic pulse velocity (UPV), water absorption (WA), Cantabro abrasion resistance (CAR), and rapid chloride permeability test (RCPT). Experimental results revealed that the optimal mixture, containing 25% FA and 12% PW (M4), exhibited superior performance. Compared with the control mix, the 56-day compressive and flexural strengths increased by 14.1% and 15.3%, respectively. The UPV value increased to 5.1 km/s, indicating improved concrete quality and matrix densification. Durability performance was significantly enhanced, with water absorption reduced by 25.4%, Cantabro abrasion mass loss decreased by 23.7%, and chloride ion penetrability reduced by 50.0% at 56 days. Statistical analysis using two-way ANOVA confirmed that FA and PW contents significantly influenced paver block performance (p < 0.05). The economic assessment further demonstrated cost savings of up to 3.0% compared with conventional concrete paver blocks. The study demonstrates that FA and PW can be effectively valorized in paver block production, offering both economic and environmental benefits. This green approach supports sustainable construction practices and promotes efficient waste management. Full article