Search Results (1,758)

Cities are the main sites of production, employment, and capital accumulation in advanced economies, which places urban economic performance at the centre of economic policy and urban governance. This work develops an integrated grey-based multi-criteria approach for assessing that performance and applies it to the sixteen G7 cities covered by the Global Power City Index (GPCI). Criterion weights are obtained with Grey RANCOM (G-RANCOM), which converts the ordinal rankings of a five-member expert panel into interval weights, and the cities are ranked with Grey MUNRA (G-MUNRA), which aggregates linear, vector, and non-linear normalization. Each performance entry is an interval bounded by the minimum and the maximum annual score observed in the GPCI Economy function over 2021–2025. Market size, economic vitality, and business environment emerge as the most influential criteria, and New York, London, and Tokyo occupy the highest positions, while Osaka, Milan, and Fukuoka occupy the last three. Robustness is tested via scenario analyses on the model parameters and through a global analysis of 100,000 replications in which all of them vary jointly. New York holds the first position in 74% of the replications and the three lowest positions are unchanged in 87%, whereas cities in adjacent middle positions are not separated reliably. Rankings produced by five established grey approaches, by crisp and fuzzy counterparts, and by the published GPCI Economy rankings agree with the reported ordering, with Spearman correlations between 0.92 and 1.00. The framework offers urban policymakers a transparent benchmarking tool for evidence-based competitiveness strategies. Full article

The diversity of today’s malware and the conflicting criteria for predictive performance, computational efficiency, dependability, and interpretability have made the choice of a suitable malware detection model more complicated. Existing research focuses predominantly on predictive performance, while the multidimensional decision process required for practical model selection remains insufficiently addressed. To address this gap, this study provides an explainability-aware hybrid multi-criteria decision-making (MCDM) framework that systematically evaluates and ranks malware detection models across heterogeneous malware datasets. The methodology incorporates predictive performance, computational efficiency, false positive rate, and a composite Explainability Index into a single decision procedure. The Explainability Index integrates explanation stability, sparsity, and expert relevance, enabling interpretability to be explicitly considered in the model-selection process. The hybrid criteria weights are obtained by combining the Analytic Hierarchy Process (AHP) with the entropy weighting method, thereby integrating expert-driven criterion importance with data-driven variability. The final ranking is obtained using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Four candidate detection models, including Random Forest, XGBoost, Convolutional Neural Network, and Long Short-Term Memory, are independently used to validate the framework on the CIC-MalMem-2022 and CICMalDroid2020 datasets. Under a harmonized evaluation protocol without assuming direct cross-dataset predictive transfer, the experimental results rank XGBoost first with a TOPSIS closeness score of 0.670, followed by Random Forest with 0.624. Sensitivity and ablation analyses further show that the model ranking remains stable while changes in criterion weighting and framework components produce measurable variations in the multi-criteria preference structure. Overall, the framework provides a transparent and multidimensional alternative to conventional performance-centered malware model evaluation. Full article

Decisions made at the early stage of the design process have a substantial influence on the environmental, socio-economic, and technical performance of a building throughout its life cycle. Although an early-stage sustainability assessment is essential to achieving climate-neutral and nearly zero-energy buildings, its implementation remains challenging due to the need to integrate and evaluate large volumes of heterogeneous data originating from multiple sources and disciplines. In view of this challenge, this study presents a hybrid multi-level approach in which data fusion is combined with Building Information Modeling (BIM), web-based technologies, and multi-criteria decision-making (MCDM) methods to support the assessment of sustainable alternative design solutions for buildings. The proposed approach is implemented in the BIM4NZEB-DS web-based decision-support system and validated through a case study. In the data fusion model, BIM-derived information is combined with data from external sources within a unified environment, enabling designers to define sustainability indicators, assign relative level of importance, and evaluate design alternatives across key sustainability dimensions. Automated multi-criteria analysis enables the ranking and comparison of design solutions. The results of the case study demonstrate the capability of the proposed approach in terms of efficiently combining heterogeneous datasets and automating and facilitating systematic comparison of early-stage design alternatives. The findings indicate that a combination of BIM-based information management, web-enabled data fusion, and automated MCDM analysis enhances the transparency, consistency, and robustness of sustainability-oriented decision making. The approach described here contributes to the advancement of digital decision-support systems for sustainable building design and represents a practical tool for supporting climate-neutral building development during the most influential stages of the design process. Full article

Industrial construction projects involve complex interactions among uncertainty, risk, schedule, and cost, motivating the development of advanced analytical and decision-support methodologies. This study systematically reviews methodological approaches to risk assessment and performance management in industrial construction. Following the PRISMA 2020 guidelines, searches of Scopus and Web of Science identified 234 records, of which 56 articles published in Q1-ranked journals between 2011 and 2025 met the pre-specified eligibility criteria. The selected studies were classified according to methodological family, uncertainty representation, and the degree of time–cost–risk integration. Four dominant methodological families were identified: multicriteria and fuzzy decision-making, probabilistic and simulation-based modeling, optimization-based planning and resource allocation, and data-driven and artificial intelligence methods. Twenty-seven studies assessed risk independently of time and cost, whereas only four jointly modeled all three dimensions. Probabilistic and optimization-based methods demonstrated the highest level of integrated analysis, while most machine-learning approaches remained prediction-oriented and most existing models were static rather than adaptive. Based on this synthesis, the review proposes a unified analytical taxonomy and a research agenda for integrated decision-support frameworks that combine dynamic uncertainty updating, predictive analytics, and multi-objective optimization. The findings identify methodological gaps and provide a foundation for adaptive models supporting robust decision-making in complex industrial construction environments. Full article

Proper municipal solid waste (MSW) management is vital for mitigating environmental degradation and protecting public health. Landfill site selection remains a complex spatial decision-making challenge, balancing ecological, social, and economic parameters. This study presents a comprehensive systematic review of 175 peer-reviewed articles published between 2016 and 2026, evaluating the evolution of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) frameworks. The findings indicate that road accessibility (93.7%), surface and groundwater protection (91.4%), slope gradients (84.6%), and settlement buffer zones (78.9%) represent the most critical and universally applied siting criteria. Digital Elevation Models (81.9%) and geological maps (57.3%) serve as foundational geospatial datasets. While the Analytic Hierarchy Process (AHP) remains the dominant weighting technique (63.41%), recent trends show an increasing adoption of hybrid multi-criteria models and optimisation algorithms. Geographically, research output is led by India, Iran, and Turkey, peaking significantly in 2025. Crucially, this review exposes prominent methodological shortcomings, notably a heavy reliance on subjective expert validation (73.8%), whereas quantitative validation, sensitivity analysis, and uncertainty assessment remain critically underutilised. In contrast to earlier reviews, this review offers a thorough and critical synthesis of GIS- and MCDA-based approaches to landfill site selection by carefully evaluating methodological advancements, examining the advantages, disadvantages, and limitations of current approaches, and incorporating statistical trends with a structured methodological framework. This approach highlights important research gaps and offers evidence-based suggestions for creating more transparent, reliable, and sustainable techniques for landfill site selection by selecting, screening, and including relevant articles. To foster sustainable urban planning, future research must prioritise standardised evaluation frameworks, rigorous uncertainty quantification, and the integration of artificial intelligence and machine learning with spatial modelling. Full article

The global energy landscape is undergoing a low-carbon, diversified, and high-efficiency transition, creating an urgent need to develop intelligent operation and maintenance (O&M) technologies for critical nuclear power equipment to boost plant economic efficiency. As the core “heart” component of the primary loop, the reactor coolant pump (main pump) must meet extremely stringent reliability criteria to ensure safe and stable operation of nuclear facilities. Existing remaining useful life (RUL) prognostics for main pumps mostly output deterministic point estimates; they fail to quantify predictive uncertainties and cannot provide credible risk intervals to support maintenance decision-making. To fill this research gap, this study first performs coupled thermomechanical failure simulations for three vulnerable main pump components: the rotor shaft assembly, double-cone sealing structure, and motor shielding sleeve. Simulation results are validated via tests on a full-scale main pump prototype bench to extract sensitive degradation characteristic parameters. Accordingly, a hybrid Bayesian Transformer-LSTM prognostic framework is proposed for main pump RUL prediction with built-in uncertainty quantification. Data augmentation is utilized to expand multi-source degradation datasets of main pumps. The Mahalanobis distance is employed to build component-level health indicators (HIs), and a cloud barycenter weighted evaluation method fuses these sub-component HIs into a unified system-level comprehensive health index (CHI). Using the fused CHI as model input, the Bayesian Transformer-LSTM architecture incorporates probabilistic fully connected layers to simultaneously capture local time-series fluctuations and long-term global degradation trends, enabling joint RUL regression and uncertainty quantification. A full-scale main pump prototype from an in-service nuclear power plant is used to validate the multi-source data fusion strategy. Quantitative evaluation results show that the proposed method achieves a coefficient of determination R² = 0.997, root mean square error (RMSE) = 0.018, and prediction interval coverage probability (PICP) = 0.839. Comparative ablation experiments further confirm that the proposed model delivers outstanding fitting precision and reliable uncertainty quantification, enabling long-timescale full-lifecycle health characterization of main pumps. Full article

Linear Diophantine fuzzy sets (LDFSs) enlarge the admissible representation space for uncertain information through membership and non-membership degrees together with reference parameters. Existing LDFS aggregation studies have mainly considered weighted, ordered, power-based, and parametric operators, while partitioned aggregation operators that model possible interrelationships within criterion groups remain limited. Maclaurin symmetric mean (MSM) operators capture interactions among criteria, and reducible weighted MSM operators have been developed to preserve idempotency and reducibility properties but have not been extended to a partitioned MSM operator. This study develops an MSM-based aggregation framework for LDFSs, including the linear Diophantine fuzzy MSM (LDFMSM), the linear Diophantine fuzzy partitioned MSM (LDFPMSM), a reducible weighted partitioned MSM (RWPMSM) and its extension to LDFSs, and the linear Diophantine fuzzy reducible weighted partitioned MSM (LDFRWPMSM) operators. LDFMSM performs symmetric k-subset aggregation over the complete criterion set, LDFPMSM restricts joint aggregation terms to criteria within the same predefined partition, and LDFRWPMSM incorporates criterion weights while preserving idempotency and reducibility properties. Theoretical properties and special cases are established. The framework is demonstrated through numerical examples and an illustrative multi-attribute decision-making (MADM) application for an AI-driven precision agriculture platform selection. Sensitivity analyses examine the effects of the interaction parameter, criterion weights and partition structure and reveal systematic changes in the ranking results. Comparative analysis shows that the LDFPMSM can distinguish alternatives that remain tied under globally symmetric aggregation, while the LDFRWPMSM identifies a different best alternative from the benchmark operators, reflecting its joint treatment of criterion importance and within-partition interactions. Full article

Reusable launch vehicles (RLVs) subject avionics electronics to repeated vibration, shock, and thermal cycling, yet no standardized method exists to evaluate reusability at the device level. We integrate physics-of-failure models—Steinberg vibration fatigue, Engelmaier/Coffin–Manson solder thermal fatigue, and Miner cumulative damage—with AHP-entropy multi-criteria decision making (MCDM). A flight-heritage 6-layer FR4 printed circuit board (PCB) from a rocket data-acquisition unit (108.25 × 108.25 × 1.62 mm, 109 components) is analyzed under Falcon 9 vibration and a DLR re-entry thermal profile (f1 = 310.2 Hz), with 11 core devices extracted from ODB++. Thermal fatigue dominates vibration damage by seven orders of magnitude. The board-level average of 62.9 flights is misleading: the ceramic PGA device D10 limits the unit to 12.3 flights (a preliminary model-based estimate, pending ALT calibration), a factor-of-five discrepancy, with three ceramic families (D10 PGA, D9 LCC, D6–D8 FIFO) forming the bottleneck. The combined weighting assigns 79.5% of the decision to the damage-derived criterion; Comprehensive Reusability Index (CRI) thresholds map flights 0–3 to direct reuse, 4–7 to refurbishment, and 8+ to retirement. The results distill into a weakest-link reuse principle, a damage-threshold service model, and an inverse-square thermal-fatigue relation. Full article

China has experienced rapid growth in municipal solid waste generation, highlighting the need to improve the effectiveness of waste sorting governance in urban areas. As the implementation of MSW sorting policies relies heavily on public acceptance and sustained participation, evaluating policy implementation from the public perspective is particularly important, yet comprehensive evaluation frameworks remain limited. This study develops a bottom-up evaluation framework integrating large-scale social media discourse, questionnaire validation, and multi-criteria decision-making. It links LDA topic modeling with policy optimization, constructs 12 multi-actor policy pathways, incorporates DEMATEL-ANP network weighting, applies modified VIKOR for pathway ranking and improvement identification, and verifies robustness using DANP and CRITIC. Results reveal that the Public–Private Partnership model combined with compulsory policies and commissioned property management achieves the highest performance (Pathway P3: management outsourcing PPP + mandatory policy + commissioned property management). Public attitude and participation behavior emerged as central upstream criteria in the influence structure, while P3 remained the top-ranked pathway across DANP- and CRITIC-based VIKOR tests and v = 0.3–0.7. The findings indicate that effective waste sorting requires mandatory rules, professional commissioned property management, convenient facilities, and sustained community support, with pathway-specific improvement directed to the most salient economic, social, and environmental weaknesses. Full article

In the modern era, mass-housing production has generally prioritized efficiency and standardization at the expense of architectural diversity and contextual sensitivity. This study proposes a computational decision-support model for the early design phase of mass housing that responds to site-specific conditions while maintaining the efficiency of industrialized housing production through the integration of parametric modeling and genetic algorithms. Using the Zeytinburnu-Kazlıçeşme district of Istanbul as a case study, a parametric model integrating the multi-objective genetic algorithm engine Wallacei X was developed in Rhino–Grasshopper. The model is structured around fundamental design variables, contextual rules, and planning constraints to generate feasible and diverse massing configurations. Four fitness objectives were evaluated simultaneously: total construction area, building footprint area, open (garden) area, and total terrace area. In a single exploratory run, 200 massing alternatives were generated, from which 32 solutions satisfying the adopted FAR/BCR screening criteria, 26 Pareto-optimal solutions, and five selected alternatives at the intersection of both sets were identified. These solutions were evaluated through a decision-support workflow based on objective performance, Pareto optimization, and the adopted local density-screening criteria. The findings indicate that genetic algorithm-based generative design can support architectural diversity, contextual sensitivity, and informed decision-making in residential housing design. Full article

Digital twin (DT) technology has become a transformative engineering paradigm for metal-cutting machine tool systems and materials-processing equipment, enabling real-time synchronisation, predictive analytics, and intelligent decision-making throughout the entire production cycle. This systematic review summarises the latest advances in the engineering-oriented implementation of digital twins, with a focus on their modelling frameworks, tool condition monitoring, compensation for geometric, kinematic, thermal and dynamic errors, fault diagnosis and adaptive control, and on the application of this technology to lathes, milling machines and grinding machines. The search for and selection of literature were carried out in accordance with the PRISMA 2020 guidelines. In total, 680 records were identified in Google Scholar in May 2026, of which 197 studies met the inclusion criteria and were synthesised narratively within six thematic sections. The analysis demonstrates that modern digital twin architectures integrate multiphysics modelling, multi-sensor data fusion, and machine learning to create high-precision virtual replicas of physical assets. Predictive maintenance and fault diagnosis systems use machine learning and deep learning to detect incipient faults in feed systems, spindles, and other critical components before failure. The review also analyses existing challenges and outlines future research directions for reliable industrial digital twins. Full article

Bio-inspired architectural design increasingly relies on generative artificial intelligence to expand early-stage concept exploration, yet current workflows often suffer from vague requirement definition, subjective proposal selection, and weak connections between user expectations, visual generation, and design evaluation. This study proposes a closed-loop human–AI decision-support framework for biomimetic architectural concept generation. The framework first uses Kansei-oriented requirement analysis and the KANO model to identify and classify stakeholder expectations concerning morphology, structural rationality, environmental integration, cultural narrative, visual novelty, interactivity, and sustainability. On the basis of 282 valid questionnaire responses, the most influential requirement categories are further translated into an Analytic Hierarchy Process (AHP) hierarchy, where expert judgement from a five-member specialist panel is used to derive criterion and sub-criterion weights. These weights are then converted into structured prompts—via a formally specified weight-to-language conversion strategy—to guide a diffusion-based image-generation system toward more targeted biomimetic concepts. Finally, TOPSIS is applied to rank generated design alternatives according to the same weighted criteria, thereby creating a traceable link from requirement discovery to generation and decision-making. Case studies involving eagle-, manta ray-, and cheetah-inspired architectural concepts indicate that the framework improves the explicitness of design objectives, supports more consistent comparison among alternatives, and reduces reliance on purely intuitive aesthetic judgement. An ablation comparison confirms that each stage of the framework contributes incrementally to the quality of final outcomes. This study proposes an integrated workflow that combines requirements modeling, multi-criteria evaluation, and AI-assisted visual design. Full article

Economic evaluation of Battery Energy Storage Systems (BESSs) integrated with solar photovoltaic (PV) systems requires the consideration of financial performance, operational behavior, battery degradation, reliability, and uncertainty. This PRISMA-based systematic review synthesizes mathematical and decision-making approaches relevant to Fuzzy Soft Set-based PV–BESS evaluation. Searches in Scopus, ScienceDirect, SpringerLink, and Taylor & Francis Online identified 789 records published between 2015 and 2026, and 74 studies were included. All studies were coded by theme, method, and economic and technical criteria, while 15 representative studies were analyzed at the equation level. Six model families were identified: techno-economic valuation, multi-objective planning, operational energy management, reliability assessment, fuzzy and multi-criteria decision-making, and degradation-aware optimization or control. Most studies appeared in 2024–2026 (57/74; 77.0%), while keyword analysis highlighted decision making and battery storage. Common formulations included NPV, NPC, LCOE, IRR, payback period, market revenue, battery capacity fade, equivalent full cycles, degradation cost, LPSP, LOLE, EENS, Monte Carlo simulation, and fuzzy ranking methods. These components remain fragmented across separate frameworks. Fuzzy Soft Set is therefore positioned as a potential parameterized decision-support layer for integrating heterogeneous outputs, subject to further theoretical development and empirical validation. This direction is relevant to Sustainable Development Goal 7 (Affordable and Clean Energy). Full article

In the context of Industry 4.0, predictive maintenance increasingly relies on machine learning models to anticipate equipment failures and reduce unplanned downtime. This makes the selection of the most suitable ML model a multidimensional and complex decision problem, since models with comparable predictive performance may differ substantially in terms of failure detection, false positives (FP), false negatives (FN), and computational requirements. This paper proposes a reproducible multicriteria decision-making framework that integrates objective Entropy-based weighting with the TOPSIS method for ML model selection in predictive maintenance applications. Logistic Regression (LR), Random Forest (RF), XGBoost, and LightGBM are evaluated using two predictive-maintenance datasets: the MetroPT dataset, representing real industrial time-series data from an air production unit in a metro system, and the AI4I 2020 Predictive Maintenance dataset from the UCI Machine Learning Repository. The experimental protocol incorporates leakage-aware temporal evaluation for MetroPT, stratified repeated evaluation for AI4I, controlled hyperparameter optimization, statistical significance analysis, and separate assessment of predictive and computational performance. The resulting model rankings are further examined using the VIKOR method and a systematic weight-sensitivity analysis covering 56 perturbation scenarios at ±20% and ±50%. The results show that XGBoost achieves the highest TOPSIS closeness coefficient on MetroPT (Ci = 0.6097), whereas LightGBM ranks first on AI4I (Ci = 0.9837). The VIKOR analysis produces the same ranking as TOPSIS on MetroPT (Spearman ρ = 1.000) and shows a strong but not perfect agreement on AI4I (ρ = 0.800). The sensitivity analysis indicates that the MetroPT ranking remains unchanged across all ±20% perturbation scenarios, while three ranking reversals occur under the ±50% perturbations. For AI4I, the top-ranked model remains unchanged across the tested perturbation scenarios, although the complete ranking can vary under changes in criterion weighting. These findings demonstrate that ML model selection in predictive maintenance should not rely solely on conventional predictive metrics, and that the proposed framework provides a structured decision-support approach for jointly considering predictive and operational performance. Full article

Older adults commonly face challenges such as insufficient spatial accessibility and a lack of safety facilities in their home environments. This paper proposes a multi-criteria decision-making (MCDM) model integrating the Analytic Hierarchy Process (AHP) and Particle Swarm Optimization (PSO) to enhance the scientific rigor and practicality of age-friendly renovation solutions. Based on 543 valid questionnaires and interview data, an evaluation system was constructed, covering four key dimensions: safety assurance, spatial accessibility, independent living capability, and environmental comfort. The weight allocation incorporates dual-source fusion of expert judgment and sample-aggregated resident preferences to improve model rationality and stability. Results demonstrate that the AHP-PSO model outperforms comparative models in prediction accuracy (MAE = 0.121), functional reasonableness (FDR = 0.136), dimensional balance (DBR = 0.912), and robustness (FSR = 94.8%). Comprehensive adaptation analysis reveals that while most residences satisfactorily meet the needs of the elderly, emergency response facilities, kitchen convenience, and furniture adaptability remain critical shortcomings constraining overall performance. The findings validate the effectiveness of the AHP-PSO model for age-friendly home modifications, providing methodological support for resource allocation optimization and aging-in-place policy formulation. Full article