Search Results (411)

Coastal lakes require monitoring approaches that capture spatial and temporal variability beyond the limits of conventional in situ measurements. In this study, a SIGMaL framework (Satellite–In situ–GIS-multicriteria decision analysis (MCDA)–Machine Learning (ML)) was developed, a unified methodology that integrates in situ monitoring, GIS MCDA-derived water quality index (WQI), satellite imagery, and ML models for comprehensive coastal lake water quality assessment. A WQI, derived from a 12-month series of in situ measurements and environmental parameters, was used alongside four physicochemical parameters measured by a multiparameter probe. First, satellite reflectance from each sensor was used to train a set of nine regression models for modelling electrical conductivity (EC), turbidity, water temperature (WT), and dissolved oxygen (DO). Second, convolutional neural networks (CNNs) with spectral and temporal inputs were trained to classify WQI classes, enabling a cross-sensor evaluation of their suitability for lake water quality monitoring. Third, the trained CNNs were applied to generate WQI maps for a subsequent 12-month period without in situ data. Across all analyses, WQI-based models provided more stable and accurate models than those trained on raw parameters. Sentinel-2 achieved the most consistent WQI performance (AUC ≈ 1.00, R² ≈ 0.84), PlanetScope captured fine-scale spatial detail (R² ≈ 0.77), while Landsat 8–9 was most effective for WT but less reliable for multi-class WQI discrimination. Sentinel-2 is recommended as the primary satellite sensor for WQI mapping within the SIGMaL framework. These findings demonstrate the advantages of WQI-based modelling and highlight the potential of ML–remote sensing integration to support coastal lake water quality monitoring. Full article

Greywater reuse presents a promising strategy for reducing potable water demand and supporting the irrigation of urban green infrastructure, yet its implementation in early planning phases remains limited by fragmented regulations, data gaps, and the absence of practical decision support tools. This study develops a comprehensive evaluation matrix based on Multi-Criteria Decision Analysis (MCDA) to assess the feasibility of greywater reuse in residential district development. The framework integrates eight domains (legal, technical, infrastructural, ecological, economic, and social factors) and is complemented by automated supporting worksheets for water balance, ecological indicators, and economic parameters. Application of the matrix to two contrasting residential case studies demonstrated its diagnostic value: the new-build district in Dortmund showed a high reuse potential, strongly influenced by favourable infrastructure conditions and ecological indicators, whereas the existing building in Weimar yielded a moderate potential due to infrastructural constraints and lower greywater availability. Sensitivity analyses further revealed that local water tariffs, intended-use scenarios, and stakeholder weightings substantially affect outcomes. Overall, the results show that the matrix supports transparent early-stage decision-making, identifies critical bottlenecks, and strengthens governance-oriented integration of greywater reuse in sustainable urban development. Full article

Lavender is a perennial Mediterranean plant that has been cultivated throughout history for medicinal, aromatic, and cosmetic purposes. Due to its high economic and commercial value, it has become an important agricultural product worldwide. The low production cost, adaptability to environmental conditions, and demand for its versatile use in the global market make it a significant potential source of income for developing Mediterranean countries. This study aims to identify commercially suitable cultivation sites for Lavandula angustifolia Mill. using remote sensing (RS) and geographic information systems (GIS) technologies to support rural development. Within this scope, suitable cultivation habitat parameters for the species in open fields and natural conditions were determined; these parameters were weighted according to their importance using multi-criteria decision analysis (MCDA), and thematic maps were created for each parameter. The created maps were combined using weighted overlay analysis, and a final map was generated according to the suitability class. The results indicate that within the study area, 75,679.45 ha is mostly suitable, 388,832.71 ha is moderately suitable, 24,068.43 ha is marginally suitable, and 229,327.20 ha is not suitable. As a result, it has been observed that Lavandula angustifolia Mill., which is currently cultivated on approximately 4045 ha of land and contributes 429 tons of product to the regional economy, covers only a relatively small portion of the suitable cultivation sites identified in the study and is not utilized to its full potential. It is understood that the expansion of lavender cultivation in determined suitable sites has significant potential to substantially develop the region and its rural population in terms of both yield and production volume, and to involve women and youth entrepreneurs in agricultural employment. Full article

This study investigates how strategic foresight can enhance FinTech governance and policy resilience in emerging economies, using Kuwait as an illustrative case. It aims to identify which foresight interventions should be prioritized across alternative futures to strengthen innovation, security, and institutional adaptability within the digital finance ecosystem. A scenario-based Multi-Criteria Decision Analysis (MCDA) framework is applied, combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert evaluations were conducted to assess five foresight interventions against eight policy and performance criteria across three plausible scenarios: Optimistic Growth, Status Quo, and Crisis and Contraction. Sensitivity analyses were performed to validate the stability of intervention rankings. The results reveal distinct priorities under each scenario: SME-oriented digital finance platforms and talent development dominate under growth and stability, while cybersecurity investment becomes paramount during crisis conditions. Regulatory fast-tracking maintains a consistent, moderate influence across all contexts. These outcomes underscore the need for adaptive, context-sensitive policy design that accommodates uncertainty. The framework provides policymakers with a structured approach to align FinTech strategies with long-term national visions such as Kuwait’s Vision 2035, while offering transferable insights for other emerging economies. The study’s originality lies in integrating strategic foresight and MCDA for FinTech governance—a methodological and practical contribution to foresight-informed policymaking. Full article

This study assesses four hydrogen production pathways (electrolysis, ammonia cracking, steam biomethane reforming, and methane pyrolysis) for an inland refinery under European Renewable Energy Directive III (RED III) goals. Using multicriteria decision analysis (MCDA), economic, environmental, technological, and implementation factors were evaluated. The results show that biomethane reforming offers the lowest cost, while electrolysis provides the best environmental and technological performance. Sensitivity analysis highlights electricity price as the key factor. The MCDA model proved to be effective for systematic comparison and informed strategic decision making. However, RED III regulatory requirements may favor ammonia or electrolysis for renewable fuel of non-biological origin production, emphasizing the need for long-term strategic planning to maintain competitiveness. Full article

Flooding is a significant natural hazard in arid regions, particularly in Saudi Arabia, where intense rainfall events pose serious risks to both infrastructure and public safety. Bahrah City, situated between Jeddah and Makkah, has experienced recurrent flooding owing to its topography, rapid urbanization, and inadequate drainage systems. This study aims to develop a comprehensive flood hazard mapping approach for Bahrah City by integrating remote sensing data, Geographic Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA). Key input factors included the Digital Elevation Model (DEM), slope, distance from streams, and land use/land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to assign relative weights to these factors, which were then combined with fuzzy membership values through fuzzy overlay analysis to generate a flood susceptibility map categorized into five levels. According to the AHP analysis, the high-susceptibility zone covers 2.2 km², indicating areas highly vulnerable to flooding, whereas the moderate-susceptibility zone spans 26.1 km², representing areas prone to occasional flooding, but with lower severity. The low-susceptibility zone, covering the largest area (44.7 km ²), corresponds to regions with a lower likelihood of significant flooding. Additionally, hydraulic simulations using the rain-on-grid (RoG) method in HEC-RAS were conducted to validate the hazard assessment by identifying inundation depths. Both the AHP analysis and the RoG flood hazard maps consistently identify the western part of Bahrah City as the high-susceptibility zone, reinforcing the reliability and complementarity of both models. These findings provide critical insights for urban planners and policymakers to improve flood hazard mitigation and strengthen resilience to future flood events. Full article

The global shift toward renewable energy and circular economy models requires industrial systems that minimize waste and recover value across entire life cycles. This review synthesizes recent advances in by-product recovery technologies supporting renewable energy and circular industrial processes. Thermal, biological, chemical/electrochemical, and biotechnological routes are analyzed across battery and e-waste recycling, bioenergy, wastewater, and agri-food sectors, with emphasis on integration through Life Cycle Assessment (LCA), techno-economic analysis (TEA), and multi-criteria decision analysis (MCDA) coupled to process simulation, digital twins, and artificial intelligence tools. Policy and economic frameworks, including the European Green Deal and the Critical Raw Materials Act, are examined in relation to technology readiness and environmental performance. Hybrid recovery systems, such as pyro-hydro-bio configurations, enable higher resource efficiency and reduced environmental impact compared with stand-alone routes. Across all technologies, major hotspots include electricity demand, reagent use, gas handling, and concentrate management, while process integration, heat recovery, and realistic substitution credits significantly improve life cycle outcomes. Harmonized LCA-TEA-MCDA frameworks and digitalized optimization emerge as essential tools for scaling sustainable, resource-efficient, and low-impact industrial ecosystems consistent with circular economy and renewable energy objectives. Full article

This study proposes GSM, an interpretable and operational GAM-SHAP-MCDA framework for stroke risk stratification by integrating generalized additive models (GAMs), a point-based clinical scoring system, SHAP-based explainability, and multi-criteria decision analysis (MCDA). Using a publicly available dataset of $n=5110$ individuals ($4.87\%$ stroke prevalence), a GAM was fitted to capture nonlinear effects of key physiological predictors, including age, average blood glucose level, and body mass index (BMI), together with linear effects for hypertension, heart disease, and categorical covariates. The estimated smooth functions revealed strong age-related risk acceleration beyond 60 years, threshold behavior for glucose levels above approximately $180\mathrm{mg}/\mathrm{dL}$, and a non-monotonic BMI association with peak risk at moderate BMI ranges. In a comparative evaluation, the GAM achieved superior discrimination and calibration relative to classical logistic regression, with a mean AUC of $0.846$ versus $0.812$ and a lower Brier score ($0.045$ vs. $0.051$). A calibration analysis yielded an intercept of $-0.04$ and a slope of $1.03$, indicating near-ideal agreement between the predicted and observed risks. While high-capacity ensemble models such as XGBoost achieved slightly higher AUC values ($0.862$), the GAM attained near-upper-bound performance while retaining full interpretability. To enhance clinical usability, the GAM smooth effects were discretized into clinically interpretable bands and converted into an additive point-based risk score ranging from 0 to 42, which was subsequently calibrated to absolute stroke probability. The calibrated probabilities were incorporated into the TOPSIS and VIKOR MCDA frameworks, producing transparent and robust patient prioritization rankings. A SHAP analysis confirmed age, glucose, and cardiometabolic factors as dominant global contributors, aligning with the learned GAM structure. Overall, the proposed GAM–SHAP–MCDA framework demonstrates that near-state-of-the-art predictive performance can be achieved alongside transparency, calibration, and decision-oriented interpretability, supporting ethical and practical deployment of medical artificial intelligence for stroke risk assessment. Full article

Background: Despite advances in monitoring and forecasting systems, natural disasters continue to cause significant human losses. During the response phase, fast decisions are required to allocate limited resources, particularly rescue helicopters, which play a key role in reaching inaccessible areas. However, helicopter allocation involves trade-offs between efficiency and operational safety under uncertain conditions. Methods: This study proposes a decision-support methodology based on Multi-Criteria Decision Analysis (MCDA) for allocating rescue helicopters during disaster response. The approach integrates Value-Focused Thinking (VFT) and Multi-Attribute Value Theory (MAVT) to structure objectives, assign weights, and evaluate alternatives using criteria related to mission safety, response time, and expected number of rescued victims. The method is illustrated through a simulated flood response scenario in a Brazilian regional context. Results: The results show that the model allows decision-makers to compare allocation scenarios and to make explicit the trade-offs between operational efficiency and safety. The application indicates that small reductions in efficiency may lead to relevant gains in operational safety, particularly under adverse weather conditions. Conclusions: The proposed approach provides a transparent and traceable structure for supporting helicopter allocation decisions during disaster response. It contributes to more consistent decision-making in critical operations, especially in contexts characterized by uncertainty and time pressure. Full article

This study investigates the cyclic behavior of reinforced concrete beam–column joints strengthened with hybrid GFRP–steel reinforcement using nonlinear finite element analysis. Six hybrid configurations—defined by varying the percentage of the total longitudinal steel reinforcement area, in the beam, replaced with GFRP bars (0%, 20%, 25%, 33%, 50%, and 100%)—were evaluated in terms of load–displacement hysteresis, stiffness degradation, dissipated energy, and crack development. A multi-criteria decision analysis (MCDA) was employed to quantitatively compare the six configurations. The findings demonstrate the potential of partial GFRP substitution to enhance the seismic performance of reinforced concrete beam–column joints. Full article

The Arctic is warming three to four times faster than the global average. This is transforming global maritime routes, thereby increasing shipping and resource extraction in Alaska. This surge requires sustainable energy solutions as policy trends towards stricter emissions standards. This article assesses the potential of Geothermal-to-X (GtX) technologies in establishing clean refueling infrastructure across Alaska, using its untapped geothermal resources. GtX uses electrolysis to split water into hydrogen and oxygen, a process powered by geothermal energy. Hydrogen and its X products, such as green methane or green ammonia, can be stored as fuels and are largely recognized as the key to a carbon-free future to address the growing energy demand. This study assesses the technical, economic, strategic, and geological feasibility of GtX refueling hubs in Alaska. Five locations were denoted as potential candidates and beckon future research. This study concludes that Unalaska is the most viable initial GtX hub given the highest Multi Criteria Decision Analysis (MCDA) score from its combination of a high-quality geothermal resource, an existing and accessible deepwater port, and a sizable local energy demand. The goal of this study is to provide an accessible and comprehensive resource for stakeholders and policymakers, outlining an energy future with sustainable maritime development, powered by affordable and secure energy. Full article

Despite significant advances in the medical sciences, out-of-pocket (OOP) healthcare costs have remained a concern, especially for lower-middle-class and poor people. The current study aims to investigate the critical factors that notably contribute to catastrophic healthcare expenses (CHCEs). To this end, the ongoing research is conducted through two phases. The first phase aims to identify the key determinants of CHCEs through expert and household evaluations. A multi-criteria decision analysis (MCDA) framework using the FullEX method is developed to analyze expert and household opinions. In the second phase, the experts investigate the hierarchical relationships among key determinants. Interpretive structural modeling (ISM) and MICMAC analysis are carried out to examine the structural relationships among the determinants. The findings of the FullEX analysis reveal that experts and households are in consensus. It is found that low-income level, number of dependable members, frequent birth rate, high child mortality, and lack of job security and risk pooling mechanisms notably contribute to the higher CHCEs. The ISM analysis indicates the strong driving power of income, education, and job security, leading to disparities in rural economic conditions, reflecting the need for holistic development. The MICMAC analysis confirms the hierarchical relationships among the key determinants of CHCEs. The findings necessitate formulating an inclusive strategy to reduce financial distress and improve the healthcare outlook for rural households, leading to sustainable development. Full article

The growing demand for sustainable pavement materials has increased interest in using recycled concrete aggregate (RCA) as a substitute for natural aggregates. However, the mechanical, durability, and environmental performance of roller-compacted concrete pavement (RCCP) incorporating very high RCA contents (≥75%) remains poorly understood, particularly when combined with hybrid steel fiber reinforcement. This knowledge gap limits the practical adoption of high-RCA RCCP in infrastructure applications. To address this gap, this study investigates the eco-efficiency of RCCP produced with 75% RCA and different steel fiber systems, including industrial (ISF), recycled (RSF), and hybrid (HSF) combinations. Mechanical performance was evaluated through compressive, tensile, and flexural testing, while freeze–thaw durability was assessed under extended cyclic exposure. Environmental impacts were quantified through a cradle-to-gate life cycle assessment (LCA), and a multi-criteria decision analysis (MCDA) was applied to integrate mechanical, durability, and environmental indicators. The findings show that although high-RCA mixtures exhibit reduced mechanical performance due to weaker interfacial bonding, HSF reinforcement effectively mitigates these drawbacks, enhancing toughness and improving freeze–thaw resistance. The LCA results indicate that replacing natural aggregates and industrial fibers with RCA and RSF substantially reduces environmental burdens. MCDA rankings further identify HSF-reinforced high-RCA mixtures as the most balanced and eco-efficient configurations. Overall, the study demonstrates that hybrid steel fibers enable the development of durable, low-carbon, and high-RCA RCCP, providing a viable pathway toward circular and sustainable pavement construction. Full article

The article discusses the issue of improving the management efficiency of spare parts at railway stations to reduce operational costs and enhance the effectiveness of rolling stock maintenance. A comparative analysis was conducted between traditional inventory storage strategies and models involving the redistribution of surplus spare parts between stations. For the quantitative assessment of the proposed approach, a mathematical model based on a multi-criteria decision analysis (MCDA) methodology was developed that includes the calculation of storage, transportation, and redistribution costs. The results show that redistributing spare parts reduces storage costs by 25%, while implementing the proposed strategy shortens the average downtime of railway wagons by 12.5%, confirming its economic feasibility. Moderate savings were achieved at the Zharyk and Zhanaaul stations, where excessive stockpiling of spare parts was reduced, and the main logistics hub—Karaganda Sortirovochnaya—helped minimize the risk of downtime. At the same time, the analysis revealed certain limitations, such as increased transportation costs and reduced network stability when redistribution volumes are high. The practical implications of the study lie in the potential implementation of the developed inventory management improvement model, not only for railway transport but also for other industries such as aviation and automotive logistics. Spare parts redistribution reduces the financial losses associated with frozen capital, increases the capital turnover, and decreases the risk of depreciation of stored components. The proposed approach ensures a more balanced distribution of resources between stations, facilitating an increase in the profitability of railway enterprises. Full article

This paper develops and applies an ex-ante methodological framework to assess the societal optimisation of eBRT innovations within the Horizon Europe eBRT2030 project, using Multi-Criteria Decision Analysis (MCDA) and the PROMETHEE method. The study evaluates 11 eBRT innovations to be deployed in five demonstration sites in Europe and one in Colombia. Twenty social parameters, including 10 risks and 10 benefits, were weighted and scored through expert and stakeholder engagement, to calculate the Societal Optimisation Index (SOI). Positive SOI values indicate that societal benefits outweigh risks, and negative values indicate the opposite, while close-to-zero values indicate socially neutral or ambiguous options requiring case-specific judgement. The results indicate that innovations such as Adaptive Fleet Scheduling and Planning, Intelligent Driver Support Systems, and IoT Monitoring Platforms provide strong societal benefits with manageable risks, while charging-related innovations are associated with social concerns. The study emphasises the importance of social impact assessment prior to implementing innovations, to enable inclusive decision-making for policymakers and transport planners and enable the development of socially optimised eBRT systems. Embedding experts’ perspectives and social criteria ensures that technological innovations are aligned with societal needs, assisting the transition towards more equitable, low-carbon transport systems. Full article