Search Results (318)

This study develops and applies an adapted New Urbanism–Transit-Oriented Development (NU–TOD) framework to evaluate spatial, functional, and climatic performance in Al Malqa District, Riyadh. Integrating NU–TOD principles with hot-arid microclimate research, Saudi sociocultural norms, and Vision 2030 governance, the study employs a convergent mixed-methods design that combines Geographic Information Systems (GIS)-based land-use and network analyses, shading and microclimate indicators, and policy review. Results show that Al Malqa benefits from strong civic anchors and proximity to a Line 1 metro station but exhibits limited walkability, low station-area densities, constrained mixed-use intensity, and insufficient shading, which together reduce effective walking catchments and transit viability. A phased intervention strategy is proposed: short-term shading and feeder-mobility improvements; medium-term TOD zoning, parking reform, and public-realm upgrades; and long-term mixed-use redevelopment and district-scale green infrastructure. A reproducible Multi-criteria Decision Analysis (MCDA) feasibility assessment (impact, regulatory ease, cost, timeline) prioritises shaded pedestrian corridors and feeder mobility as the most implementable early actions. The study contributes a replicable evaluation model for hot-arid cities and offers actionable guidance for Vision 2030, aligned with suburban intensification in Riyadh. Full article

This study aims to build a spatial model for selecting the optimal site for a radio astronomy observatory in southern Jordan. Geographic Information Systems (GISs) and Multi-Criteria Decision Analysis (MCDA)-based methodology were used in this study to develop a spatial model for choosing the best location for a radio astronomy observatory in southern Jordan. The criteria were weighted using a hybrid framework that combined the Analytic Hierarchy Process (AHP) and the entropy method to account for the actual spatial diversity of the data, in addition to expert judgment. The study assesses site suitability by considering several environmental and logistical factors that mitigate radio frequency interference (RFI), including elevation, cloud cover, artificial light pollution, and accessibility. A final map highlighting the optimal areas for radio astronomy observatories in southern Jordan has been created. The study methodology started with MCDA, and was followed by several stages, including visual evaluation, overlay analysis, establishment of 500 m buffer zones, extraction of the “Very High Suitability” class, and conversion to a transparent vector layer that is free from urban overlap and electromagnetic interference. The results show that the majority of large observatories (10 km²; equivalent to ≥10,000,000 m²) are located in Aqaba and Ma’an, which offer natural isolation and wide expanses ideal for global projects. Medium observatories (0.5–10 km²; equivalent to 500,000–10,000,000 m²) were generally identified at a reasonable cost in Ma’an and Aqaba, with the possibility of radio surveillance and infrastructure expansion. Many small observatories (0.01–0.5 km²; equivalent to 10,000–500,000 m²) were constructed near academic institutions, providing viable, easily accessible places for university research with little regulatory restraints. This research contributes to national astronomy infrastructure planning and serves as a model for other countries experiencing dry or semi-arid climates. It also offers decision-makers a useful spatial database. Full article

Regenerating fragile historical contexts requires choices of repurposing that combine heritage protection, continuity of use and managerial feasibility, in the presence of multiple objectives and stakeholders with different preferences. This study develops and tests an MCDA-based decision-support framework for the ex-ante selection of adaptive reuse scenario applied to Craco (Italy) and Palazzo Carbone-Rigirone. Craco and Palazzo Carbone-Rigirone were selected as a critical case because they combine heritage abandonment, geomorphological fragility, cultural visibility, weak local services and the need for a feasible management model. The methodology involves: (i) defining four adaptive reuse scenarios; (ii) constructing nine criteria that integrate socio-economic impacts, safety/security, cultural attractiveness, compatibility with the property and economic–financial feasibility; (iii) elicitation of weights using a hybrid approach, combining the decision-maker’s macro priorities and the social quota derived from questionnaires using normalised indicators of satisfaction/dissatisfaction and priorities for improvement; (iv) classification using the Weighted Sum Model and TOPSIS under two normalizations (distributive and ideal) and two variants (relative and absolute). The results show convergence between methods and stability of the ranking, with a preference for the multifunctional scenario oriented towards cultural services and socialising. In the case of Craco, adaptive reuse offers advantages compared with purely conservative, passive musealization or tourism-only strategies. The study concludes that MCDA is useful as a transparent pre-selection tool and supports the alignment of local needs and institutional priorities; its robustness can be strengthened with sensitivity analyses and policy scenarios. Full article

Reliable battery health assessment is essential to accelerate battery electric vehicle (BEV) adoption, yet most existing in-vehicle methods do not capture the complex processes driving ageing. Electrochemical impedance spectroscopy (EIS) offers deeper diagnostic insight but faces significant architectural and integration constraints. This study establishes a rigorous system-level framework for practicable onboard EIS implementation, focusing on the integration within Battery Management System (BMS) and powertrain architectures. Various integration topologies for cell-, module- and pack-level EIS are evaluated, highlighting their key trade-offs. The viability of the presented architectures is assessed through an application-specific Multi-Criteria Decision Analysis (MCDA) for mass-market, high-performance and circular economy use-cases. This study confirms the feasibility of onboard EIS while providing industry and scientific stakeholders with practical guidance to advance battery diagnostics for next-generation BEVs. Full article

Industrial Symbiosis (IS) is a key Circular Economy strategy that promotes resource efficiency through collaboration among companies. While previous research has largely focused on established IS business models, increasing attention has been given to sector-specific implementation and the contextual factors that influence its success. This study develops a Multi-Criteria Decision Analysis (MCDA) framework based on the Deck of Cards Method (DCM) to support the sectoral implementation of IS. A key innovation of this study is the incorporation of IS enablers and barriers into a sector-specific MCDA framework, providing a structured approach to support decision-making and implementation. By incorporating stakeholder preferences and prioritizing implementation opportunities, the framework provides a structured basis for decision-making, being replicated and adaptable to other industrial sectors. The framework was applied to the Portuguese cement sector through consultations with experts representing five stakeholder groups, furtherer allowing its validation. The analysis combines the importance assigned by stakeholders to the criteria and the performance of the sector across those criteria. Results for the sector perspective indicate that policy, economic, and technological criteria are perceived as the most important for advancing IS, whereas geographical, social, and management-related aspects receive lower priority. In Portugal, this sector demonstrates stronger performance in economic (17.49–3.04), technological (13.80–5.99), and environmental (14.58–3.19) criteria, while challenges remain in geographical coordination (1.16–7.95), social engagement social (0.79–6.81), and intermediary support (1.15–8.44). These findings highlight the importance of aligning policy, technological development, and organizational mechanisms to facilitate industrial collaboration and resource exchange. The study demonstrates the potential of MCDA as a practical and effective decision-support tool for IS implementation and provides insights for designing targeted strategies to strengthen sectoral Industrial Symbiosis. Full article

The development of sustainable tourism requires the use of planning methods that combine environmental protection with inclusive access to nature-based destinations. This article presents a macro-level spatial decision-support framework for planning service infrastructure for specialized off-road electric wheelchairs in mountain destinations. The proposed framework combines predefined static vehicle-related constraints, Geographic Information System (GIS) analysis using QGIS and OpenStreetMap data, and Multi-Criteria Decision Analysis (MCDA). The spatial filtering stage evaluates terrain feasibility using an adopted maximum longitudinal slope threshold and minimum path-width requirement. The location–allocation stage combines Simple Additive Weighting (SAW) with a spatial-dispersion procedure to identify service hubs that are both suitable and regionally distributed. The method is not a dynamic engineering model of vehicle performance, but a GIS-MCDA planning tool for preliminary regional infrastructure siting under predefined operational constraints. Full article

Background: Multi-criteria decision analysis (MCDA) has become a dominant approach for glacier hazard susceptibility mapping, widely used to support risk management and climate adaptation planning. However, despite its widespread adoption, the role of MCDA outputs remains conceptually ambiguous: hazard classifications are often interpreted as predictive representations of risk, even though they are derived from preference-dependent decision models. This raises a critical but underexamined question regarding the reliability of MCDA-based glacier hazard assessments. This issue becomes particularly relevant in the current transition toward data-driven and artificial intelligence (AI)-based approaches for hazard modelling, where similar challenges of interpretability, validation, and reliability arise. Methods: To address this issue, we conducted a systematic literature review following the PRISMA 2020 protocol, analysing peer-reviewed studies published between 2015 and 2025. After screening 571 records, 60 studies were included. Data were extracted using a structured framework and synthesised through quantitative descriptive analysis and qualitative assessment of modelling practices, including method selection, criteria weighting, uncertainty treatment, validation, and geographical distribution. This study conducts a structured methodological audit—not a catalogue—of multi-criteria decision analysis (MCDA) applications in glacier hazard susceptibility mapping. Results: The analysis reveals a consistent methodological pattern. The Analytic Hierarchy Process (AHP) dominates current practice (36/60 studies, 60%), typically implemented through GIS-based weighted overlay with expert-derived weights. Critically, 80% of studies (48/60) derive criteria weights exclusively from expert judgement, with no data-driven calibration or sensitivity testing of subjective inputs. This epistemic reliance on unstructured or semi-structured expert elicitation, presented without robustness analysis, forms a central concern of this review. Moreover, empirical validation is limited: only 21/60 studies (35.0%) report quantitative performance metrics. Uncertainty and robustness analyses are rarely conducted, and most studies rely on single-model configurations without comparative evaluation. Despite these limitations, the resulting hazard maps are frequently presented as objective spatial predictions. The evidence base is also geographically concentrated, with 48/60 studies (80.0%) located in High Mountain Asia. Conclusions: The findings indicate a systematic mismatch between how MCDA-based hazard maps are constructed and how they are interpreted. In most cases, MCDA functions as a decision-structuring framework rather than a validated predictive model, yet its outputs are commonly treated as predictive evidence. This gap has important implications for the use of such models in risk management and climate adaptation, particularly in the emerging context of AI-driven hazard modelling, where issues of model validation, interpretability, and reliability become even more critical. Advancing the field requires explicit validation against observed events, systematic robustness and sensitivity analysis, transparent uncertainty modelling, and comparative evaluation of alternative or hybrid decision frameworks. Full article

Dryland watersheds are governed by tightly coupled source–sink dynamics, in which expanding bare soil and declining vegetated patches amplify runoff, sediment transport, and land degradation. Identifying suitable locations for water harvesting structures remains challenging due to the limited scalability of field assessments and the inability of coarse DEM-based GIS methods to capture critical microtopographic features. This study evaluates whether high-resolution (0.44 m) topographic data, integrated with multi-criteria decision analysis (MCDA), can identify suitable locations for water harvesting structures in dryland watersheds and compares the model discrimination of the Analytical Hierarchy Process (AHP) and the Fuzzy AHP (FAHP). Eight geomorphic and ecological indicators were evaluated and validated using 565 practitioner-identified restoration practice locations across two watersheds in southern New Mexico. The results show that 78% (East Control) and 94% (West Restoration) of validation sites occur within the top two predicted suitability classes, with moderate to good model discrimination (AUC: 0.671–0.723) and strong ranking performance (Boyce Index: 0.945–0.983). AHP and FAHP produced nearly identical outputs (ΔAUC < 1%; ΔBoyce ≤ 0.005). These findings demonstrate that high-resolution topography, coupled with MCDA, provides a robust and transferable framework for the landscape-scale prioritization of nature-based water harvesting structures to support ecohydrological restoration in dryland watersheds. Full article

This paper introduces CMA-YOLO, a network that integrates multi-scale features with dual attention mechanisms to address weak feature representation, low detection accuracy, and loss of fine-grained details in deep networks for wind turbine blade surface defect detection. First, we construct the C2MSA module by designing a Multi-scale Feature-enhanced Attention Convolution Mix (MS-ACmix) based on ACmix and embedding it into the C2PSA block. This lets the network capture local and global contextual features, strengthening multi-scale target recognition and lowering missed detections. Second, we devise a Monte Carlo Dual Attention (MCDA) mechanism combining random sampling with dual attention. This approach retains the regularization benefits of the Monte Carlo method while leveraging dual attention selection, enabling improved detection accuracy with low computational cost. Finally, we substitute the original downsampling layers in the backbone and neck with the ADown module. This lightweight design, together with efficient feature extraction and fusion, reduces fine-grained detail loss and improves defect detection capability. Quantitative results reveal that, compared to YOLO11n, CMA-YOLO yields improvements of 3.4% in mAP@0.5, 6.1% in mAP@0.5:0.95, and 8.8% in recall, with a 0.7 GFLOPs reduction in computational cost, thus validating the proposed algorithm. Overall, CMA-YOLO provides a lightweight and effective approach for inspecting blade surface defects on wind turbines operating in resource-limited settings. Full article

Objective: To investigate whether gestational diabetes mellitus (GDM), including insulin-treated GDM, affects cerebroplacental ratio (CPR) values in monochorionic diamniotic (MCDA) twin pregnancies. Methods: This retrospective single-center cohort study included a total of 262 MCDA twin pregnancies managed at a tertiary referral center, comprising pregnancies without GDM (n = 120), with diet-controlled GDM (n = 80), and with insulin-treated GDM (n = 62). Doppler ultrasound examinations were performed at three gestational time points between 24 and 36 weeks of gestation. CPR, umbilical artery pulsatility index (UA-PI), and middle cerebral artery pulsatility index (MCA-PI) were compared longitudinally between groups. Doppler indices were compared between groups without adjustment for baseline differences such as BMI and parity Results: Maternal body mass index was significantly higher in pregnancies complicated by GDM, particularly in those requiring insulin therapy (p < 0.001). Estimated fetal weight was higher in the insulin-treated GDM group at mid-gestation (28–32 weeks; p = 0.01). However, CPR values remained within normal ranges throughout all screening points across all three groups. No relevant differences in UA-PI, MCA-PI, gestational age at delivery, Apgar scores, or umbilical cord pH were observed between groups. Conclusions: In MCDA twin pregnancies, gestational diabetes—regardless of insulin treatment—does not appear to significantly influence cerebroplacental ratio values throughout gestation. No statistically significant differences in CPR values were observed between groups. No statistically significant differences in CPR values were detected between groups. However, given the exploratory design and lack of adjustment for confounders, subtle effects cannot be excluded. The clinical utility of CPR in this population requires further investigation. Full article

Due to climate change and its increased variability, as well as the extreme weather events, flooding is becoming a major natural threat causing profound economic, social, and ecological impact. This paper systematically reviews 89 peer-reviewed articles published between 2010 and 2024 on flood risk assessment approaches, including geospatial techniques and methods for flooding, using the PRISMA framework and the ScienceDirect and Web of Science databases. GIS and remote sensing are the most popular tools for flood hazard mapping, and hydrodynamic models such as HEC-RAS and MIKE FLOOD dominate flood simulation. Machine learning algorithms, multi-criteria decision analysis (MCDA), and climate scenario analysis have also emerged as increasingly prominent methodological contributions to flood risk frameworks. This review makes a novel contribution by providing the first systematic synthesis of geospatial flood risk assessment methods, explicitly quantifying both the urban–rural research imbalance and the degree of hazard, vulnerability, and exposure integration across the literature. Specifically, only 13 (2.7%) of all eligible articles addressed rural flooding, despite the profound socioeconomic impacts that disproportionately affect these communities, and only 16% of included studies integrated any combination of hazard, vulnerability, and exposure components within current assessment approaches. This review highlights the importance of interdisciplinary collaboration and sensitivity to rural contexts in cultivating resilience and fostering equitable flood risk management. Full article

This paper assesses the challenges and policy responses for the adoption of electric vehicles (EVs) in Africa. We applied a decision support system framework comprising a new integration of the RANking COMparison Method (RANCOM) and Root Assessment Method (RAM) for the first time in the literature to address the multi-criteria decision analysis (MCDA) problems based on expert opinions. Six experts evaluated five criteria along with ten policy responses. While the weights of criteria are computed via the RANCOM method, the RAM approach ranks the policy responses. Moreover, the Compromise Fuzzy Ranking (CFR) method defines the consensus rankings via both positional ranks and preference scores. Furthermore, a three-stage comparative analysis is carried out for criteria weighting, policy responses ranking, and alternative consensus ranking. A sensitivity analysis is carried out including the consideration of experts’ significance according to their experience and their omission. The findings indicated the most critical challenges were the scarcity in charging infrastructure and the affordability and accessibility issues. The resilient charging infrastructure is the most appropriate policy response. The findings direct planners and EVs policymakers across the continent toward a policy that will ensure a clean and sustainable transportation system. Full article

Infrastructure drainage projects play a critical role in urban development but are increasingly exposed to environmental, operational, and climate-related risks that challenge their long-term sustainability. Despite this, decision-makers continue to lack risk-informed, structured methods to assess sustainability performance in an uncertain environment. In order to facilitate evidence-based decision-making and sustainable risk management, this study suggests a risk-informed sustainability index for infrastructure drainage projects. The study first points out a weakness in the methods currently used for sustainability assessments, specifically the lack of risk-sensitive, standardized frameworks designed for drainage infrastructure systems. Altogether, 28 sustainability indicators are identified, with 22 indicators retained after the application of fuzzy set theory criteria. The sustainability index is developed by normalizing, weighting, and combining these indicators using a multi-criteria decision analysis (MCDA) method. To show the usefulness and practicality of the suggested approach in assessing sustainability performance and pinpointing risk-critical improvement areas, it is used for a long-term infrastructure drainage project. In order to improve infrastructure resilience, the findings emphasize the significance of early integration of sustainability and risk considerations, stakeholder engagement, and ongoing performance monitoring. The suggested approach offers a flexible and transferable framework for risk-informed decision-making, assisting engineers, project managers, and policymakers in enhancing the resilience and sustainability of infrastructure drainage systems. Full article

Compressed air leakages represent a major source of energy waste and financial loss in industrial facilities. However, accurately detecting and quantifying these leaks remains challenging due to the wide variation in the accuracy, cost, usability, and practical applicability of available methods. This paper presents a structured review and evaluation of leakage localization and quantification methods for compressed air systems (CASs), categorized into hardware-, software-, and non-technical-based approaches. Based on expert interviews and a comprehensive literature review, a set of evaluation criteria was defined and applied within a multi-criteria decision analysis (MCDA) framework. The Analytic Hierarchy Process (AHP) was used to derive criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the alternatives separately for localization and quantification tasks. To enhance practical relevance, five expert interviews were conducted with industrial stakeholders from diverse professional backgrounds, including maintenance engineers and energy managers. A questionnaire was also distributed to assess the methods. The results provide illustrative insights into the relative suitability of different methods. Within the scope of this exploratory study, from a practical industrial perspective, the compressor duty cycle method and non-intrusive load monitoring (NILM) appear to be promising approaches to leakage quantification, while ultrasonic detection is preferred for localization. Notably, discrepancies between questionnaire-based rankings and expert interview insights highlight the limitations of purely survey-driven evaluations. The proposed framework supports industrial decision-makers in selecting leakage detection and quantification methods by balancing technical performance, implementation effort, and operational constraints, thereby contributing to reduced energy losses and improved system efficiency. Full article

Adaptive reuse has emerged to become a tool for implementing the understanding of sustainability in the domain of architectural conservation, as it encourages the continued usage of old buildings as means of reducing environmental impact, as well as preserving socio-cultural capital while generating economic income. However, in its practice, the decisions regarding granting meanings, interpretation, and preserving memories within adaptation processes are dominated by expert-driven approaches that inadequately incorporate stakeholder values or intangible heritage dimensions. To this end, this study aims to contribute to the current debate by adopting a participatory co-evaluation framework that integrates both authenticity perspectives and sustainability dimensions using Multi-Criteria Decision Analysis (MCDA) for evaluating adaptive reuse alternatives for an abandoned prefabricated wooden heritage building. Stakeholder priorities were drawn through a workshop and transformed into normalized weights using the Simos technique. Four design alternative typologies—namely, Continuity, Cultivation, Differential, and Optimization—were assessed and compared against 20 performance indicators across heritage, social, ecological, and economic criteria using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Indicator-level analyses and sensitivity tests (±10% and ±20% weight variations) were applied to confirm the robustness of rankings. The results from the best-performing alternative demonstrated the trade-offs between heritage authenticity and sustainability objectives, as well as demonstrating how combining participatory methods with quantitative evaluation can support evidence-based decision-making for adaptive reuse. The applied integrated framework helps bridge the gap between heritage theory and practice by combining authenticity, participation, and sustainability in one analytical approach, supporting evidence-based decisions for adaptive reuse. Full article