Search Results (1,031)

Modern smart cities face increasing pressure to invest in sustainable and reliable energy systems while navigating uncertainties arising from fluctuating market conditions, evolving technology landscapes, and diverse expert opinions. Traditional multi-criteria decision-making (MCDM) approaches often fail to fully represent these uncertainties as they typically rely on crisp inputs, lack temporal memory, and do not explicitly account for stochastic variability. To address these limitations, this study introduces a novel Stochastic Fractional Fuzzy Tensor (SFFT)-based Group Decision-Making framework. The proposed approach integrates three dimensions of uncertainty within a unified mathematical structure: fuzzy representation of subjective expert assessments, fractional temporal operators (Caputo derivative, $\alpha =0.85$) to model the influence of historical evaluations, and stochastic diffusion terms ($\sigma =0.05$) to capture real-world volatility. A complete decision algorithm is developed and applied to a realistic smart city renewable energy selection problem involving six alternatives and six criteria evaluated by three experts. The SFFT-based evaluation identified Geothermal Energy as the optimal choice with a score of 0.798, followed by Offshore Wind (0.722) and Waste-to-Hydrogen (0.713). Comparative evaluation against benchmark MCDM methods—TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), and WSM (Weighted Sum Model)—demonstrates that the SFFT approach yields more robust and stable rankings, particularly under uncertainty and model perturbations. Extensive sensitivity analysis confirms high resilience of the top-ranked alternative, with Geothermal retaining the first position in 82.4% of 5000 Monte Carlo simulations under simultaneous variations in weights, memory parameter ($\alpha \in [0.25,0.95]$), and noise intensity ($\sigma \in [0.01,0.10]$). This research provides a realistic, mathematically grounded, and decision-maker-friendly tool for strategic planning in uncertain, dynamic urban environments, with strong potential for deployment in wider engineering, management, and policy applications. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

In this paper, the selection of the optimal base material to be used in the repair welding process is presented. The aim of the study was to determine which of the available materials has the best characteristics, based on an analysis conducted in a company engaged in construction works. Three base materials were considered in the study: ABRADUR 58, E DUR 600, and CrWC 600 electrodes. Repair welding was performed on components for a construction machinery facility using the manual metal arc welding procedure. For the selection of the optimal base material, a combined Multi-Attribute Decision-Making (MADM) approach was applied. The base materials were evaluated based on four attributes: wear track width, cost, mass loss, and hardness of welded layers. The Best–Worst Method (BWM) was used to determine the attribute weights, while the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Ranking based on the Distances And Range (RADAR) methods were applied in parallel for the ranking and selection of base materials. The analysis showed that in the considered case, the E DUR 600 electrode was the most suitable choice, which was confirmed through the application of both the TOPSIS and RADAR methods. Full article

The global expansion of healthcare services has made medical waste management an increasingly critical and complex issue. Medical wastes require specialized management due to their high infection risk, potential for environmental pollution, and adverse effects on public health. The correct collection, transportation, and final disposal are vital for protecting environmental health and ensuring the safety of hospital personnel and the community. Numerous disposal methods exist. Selecting the appropriate one, however, is a multi-dimensional decision-making problem, necessitating the simultaneous evaluation of various conflicting criteria. Adana, one of Turkey’s largest provinces, generates significant medical waste volumes due to its dense population and developed health infrastructure. Therefore, choosing the most suitable disposal method for hospitals in Adana is crucial for establishing an effective and sustainable waste management system. Making this decision using traditional methods is difficult. The multitude of criteria prevents any single method from being optimal across all aspects. This complexity mandates the use of Multi-Criteria Decision-Making (MCDM) methodologies. In this study, MCDM methods were applied, based on expert opinions, to select the disposal method at a university hospital in Adana. The research examined twelve criteria and four alternatives. The CRITIC (Criteria Importance Through Intercriteria Correlation) method was employed to objectively weigh the criteria. For the rigorous evaluation and ranking of the alternatives, three robust MCDM methods were utilized: PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and EDAS (Evaluation based on Distance from Average Solution). The final results conclusively identified incineration as the most appropriate disposal method for the hospital. Full article

The current study presents a holistic technology evaluation and integration methodology for enhancing the aerodynamic efficiency and performance of a tactical, fixed-wing Blended-Wing-Body (BWB) Unmanned Aerial Vehicle (UAV) through the synergetic integration of several aerodynamic and performance-enhancing technologies. Based upon several individual technology investigations conducted in the framework of the EURRICA (Enhanced Unmanned aeRial vehicle platfoRm using integrated Innovative layout Configurations And propulsion technologies) research project for BWB UAVs, a structured Technology Identification, Evaluation, and Selection (TIES) is conducted. That is, a synergetic examination is made involving technologies from three domains: configuration layout, flow control techniques, and hybrid-electric propulsion systems. Six technology alternatives, slats, wing fences, Dielectric Barrier Discharge (DBD) plasma actuators, morphing elevons, hybrid propulsion system and a hybrid solar propulsion system, are assessed using a deterministic Multi-Attribute Decision Making (MADM) framework based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Evaluation metrics include stall velocity (V_s), takeoff distance (s_g), gross takeoff weight (GTOW), maximum allowable GTOW, and fuel consumption reduction. Results demonstrate that certain configurations yield significant improvements in low-speed performance and endurance, while the corresponding technology assumptions and constraints are, respectively, discussed. Notably, the configuration combining slats, morphing control surfaces, fences, and hybrid propulsion achieves the highest ranking under a performance-future synergy scenario, leading to over 25% fuel savings and more than 100 kg allowable GTOW increase. These findings provide quantitative evidence for the potential of several technologies in future UAV developments, even when a novel configuration, such as BWB, is used. Full article

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R², RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R² from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications. Full article

Ziziphus jujuba Mill. cv. Jinsi (Z. jujuba), a commercially significant cultivar of Chinese jujube, is extensively cultivated across diverse regions of China. However, comprehensive evaluations addressing the quality disparities of Z. jujuba originating from different geographical regions have received limited attention. To systematically evaluate quality variations in Z. jujuba across origins, 14 commercially cultivated commercial batches from 7 Chinese provinces were collected, with comprehensive parameters determined, including appearance, color, safety, aroma, flavor, and functional components. Multivariate statistical analyses, specifically Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and the entropy weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), were employed for data interpretation. All samples met national standards for aflatoxin and SO₂ residues. Shanxi samples had the largest length and weight, while Jiangsu and Shaanxi showed optimal color. Key volatiles included nitrogen oxides and sulfides, with sweetness as the main sensory trait. Ningxia samples had the highest total triterpenes, Jiangxi the highest flavonoids, and Shandong the highest polysaccharides, and Shaanxi samples possessed the highest total oligosaccharides. Entropy weight TOPSIS ranked quality as Ningxia > Shaanxi > Jiangsu > Jiangxi > Shanxi > Shandong > Henan. These findings confirm origin-related environmental effects on Z. jujuba quality, providing a scientific basis for its quality evaluation and sustainable development. Full article

Scientific planning and optimal development of multi-type power sources are critical prerequisites for supporting the robust evolution of emerging power systems. However, existing techno-economic evaluation methods often face challenges such as higher-order uncertainty and weight conflicts, making it difficult to provide reliable support for comparing and selecting power source schemes. To address this, this paper proposes an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method based on Fermatean Fuzzy Sets (FFS) for techno-economic evaluation of multi-type power sources. First, building on the traditional TOPSIS framework, we introduce Fermatean Fuzzy Sets to construct a FF Hybrid Weighted Distance (FFHWD) measure. This measure simultaneously captures the subjective importance of evaluation indicators and decision-makers’ risk preferences. Second, we design a subjective-objective coupled weighting strategy integrating Fuzzy Analytic Hierarchy Process (FAHP) and Entropy Weight Method (EWM) to achieve dynamic weight balancing, effectively mitigating biases caused by single weighting approaches. Finally, the FFHWD is integrated into the improved TOPSIS framework by defining FF positive and negative ideal solutions. The comprehensive closeness coefficients of each power source scheme are calculated to enable robust ranking and optimal selection of multi-type power source alternatives. Empirical analysis of five representative power generation technologies—thermal power, hydropower, wind power, photovoltaics (PV), and energy storage—demonstrates the following comprehensive techno-economic ranking: hydropower > photovoltaics > thermal power > wind power > energy storage. Hydropower achieves the highest closeness coefficient (−0.4198), whereas energy storage yields the lowest value (−2.8704), effectively illustrating their respective advantages and limitations within the evaluation framework. This research provides scientific decision-making support and methodological references for optimizing multi-type power source configurations and planning new power systems. Full article

Balancing efficiency and equity within agricultural supply chains is crucial for rural revitalization and sustainable development. This study focuses on the three-tiered chain of ‘farmers–cooperatives–retailers’, constructing a joint decision-making model linking pricing, sales effort, and order volume. It compares the performance differences between decentralized and centralized decision-making structures. Methodologically, we introduce four corrective factors—risk-bearing capacity, cooperation level, capital investment, and information access—to the traditional Shapley value. By employing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to calculate proximity, we derive an enhanced Shapley–TOPSIS allocation coefficient. Furthermore, we design a secondary distribution rule of ‘effort-based value-added distribution according to labor contribution,’ tightly binding the marginal returns of sales effort to input intensity, thereby reconciling structural fairness with incentive compatibility. Empirical findings indicate that, compared with decentralized approaches, centralized decision-making significantly enhances overall system revenue and reduces retail prices. The refined distribution scheme outperforms the baseline Shapley value in fairness and stability, effectively mitigating the misalignment where effort contributors receive disproportionately low returns. The optimal sales effort level is approximately 0.35. Under the ‘distribution according to labor’ approach, retailers (the primary effort providers) see a marked increase in their value-added share, whereas farmers and cooperatives also gain positive benefits, enhancing alliance stability. Unlike existing studies that rely mainly on revenue-sharing contracts or a single Shapley allocation, this study, on the one hand, explicitly endogenizes sales effort into demand and profit functions and systematically characterizes the joint mechanism between effort and profit allocation under both centralized and decentralized structures. On the other hand, an improved Shapley–TOPSIS modeling procedure and an ‘effort added-value allocation according to contribution’ rule are proposed. By adjusting demand parameters and the weights of the adjustment factors, the proposed framework can be readily extended to other agricultural products and green supply chain settings, providing a replicable tool and managerial implications for designing sustainable profit allocation schemes. Full article

High-nitrogen austenitic stainless steels (HNASS) require compositional strategies that simultaneously maximize corrosion resistance and microstructural stability while suppressing delta (δ) ferrite and deleterious precipitates. Here, an explainable multi-objective design workflow is developed that couples thermodynamic descriptors from the Calculation of Phase Diagrams (CALPHAD) approach—using both equilibrium and Scheil solidification calculations—with machine learning surrogate models, random forest (RF) and Extreme Gradient Boosting (XGBoost), trained on 60,480 compositions in the Fe–C–N–Cr–Mn–Mo–Ni–Si space. The physics-informed feature set comprises phase fractions; transformation and precipitation temperatures for δ-ferrite, chromium nitride (Cr₂N), sigma (σ) phase and M₂₃C₆ carbides; liquidus and solidus temperatures; and the pitting-resistance equivalent number (PREN). The RF model achieves consistently low prediction errors, with a PREN root-mean-square error (RMSE) of ≈0.004, and exhibits strong generalization. Shapley additive explanations (SHAP) reveal metallurgically consistent trends: increasing nitrogen (N) suppresses δ-ferrite and promotes Cr₂N; carbon (C) promotes M₂₃C₆; molybdenum (Mo) promotes the σ-phase; and C and silicon (Si) widen the freezing range. Using the trained surrogate as the objective evaluator, the non-dominated sorting genetic algorithm III (NSGA-III) builds Pareto fronts that minimize the δ-ferrite range, Cr₂N, σ-phase, M₂₃C₆ and the freezing range (ΔT) while maximizing PREN. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank the Pareto-optimal candidates and to select compositions that combine elevated PREN with controlled precipitation windows. This workflow is efficient, reproducible and interpretable and provides actionable composition candidates together with a transferable methodology for data-driven stainless steel design. Full article

Failure Mode and Effects Analysis (FMEA) is a systematic risk assessment tool that effectively evaluates the safety and reliability of products prior to their deployment. However, traditional FMEA fails to consider and leverage inherent system-specific information during risk assessment, while also neglecting the weights of risk factors (RFs) when processing data related to the Risk Priority Number (RPN). This leads to significant subjectivity in the final risk ranking of failure modes. To overcome these drawbacks, this study proposes an improved FMEA risk assessment method based on load sharing, aiming to develop an improved FMEA method that addresses the critical limitations of traditional approaches by integrating load sharing principles and systematic weight determination, thereby enhancing risk assessment objectivity and accuracy in complex multi-component systems. First, probabilistic linguistic terms are adopted to quantify experts’ risk assessment information, and the geometric mean method is then used to aggregate assessments from multiple experts. Second, the Fuzzy Best–Worst Method (FBWM) is employed to determine the relative weights of the three RPN factors (Occurrence, Severity, and Detection). Additionally, partial system structural data are obtained through load sharing, and these data—combined with the calculated factor weights—are integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate the final risk ranking of failure modes. Finally, a case study of a magnetic crane is conducted to verify the feasibility and effectiveness of the proposed method, supplemented by comparative experiments to demonstrate its superiority. Full article

As hydrogen emerges as a key vector in the shift toward cleaner energy systems, the evaluation of storage technologies becomes essential to support its integration across diverse applications. This work provides a comparative analysis of four hydrogen storage methods, compressed gas, metal hydrides, metal–organic frameworks (MOFs), and carbon-based materials, using a structured multi-criteria decision-making (MCDM) approach, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation is based on a comprehensive set of technical, economic, and environmental criteria, including safety, storage capacity, efficiency, cycle durability, technological maturity, environmental impact, cost, and scalability. The analysis adopts a technology-oriented perspective, focusing on the intrinsic performance and feasibility of hydrogen storage systems rather than on a detailed techno-economic optimization. The results show that metal hydrides offer the most balanced performance, driven by high volumetric capacity and solid-phase stability, followed closely by compressed hydrogen, which stands out for its technological maturity and well-established infrastructure, despite facing significant challenges related to safety and space efficiency due to high-pressure storage requirements. Carbon-based materials and MOFs, although promising in specific aspects such as safety, storage density, or material sustainability, are hindered by technological immaturity and operational limitations. Full article

This study examines the synergistic influence of recycled concrete aggregates (RCAs), industrial steel fibers (ISFs), recycled steel fibers (RSFs), and hybrid ISF/RSF (HSF) on the structural, durability, and environmental performance of roller-compacted concrete pavement (RCCP). Twenty mixtures were prepared with 0 and 50% RCA and fiber dosages of 0–0.9%, including plain, single-fiber, and HSF systems. Compressive, splitting tensile, and flexural strengths, as well as freeze–thaw resistance up to 300 cycles, were experimentally evaluated. Environmental performance was quantified through a cradle-to-gate life cycle assessment (LCA) covering nine impact categories and integrated with a multi-criteria decision analysis (MCDA) using the weighted sum method (WSM) and technique for order of preference by similarity to ideal solution (TOPSIS). Results indicate that 50% RCA replacement reduced compressive strength by ~21% but decreased global warming potential (GWP) by 15%. Hybrid fiber reinforcement significantly improved mechanical and durability properties, achieving up to 51% higher tensile strength and >85% strength retention after 300 freeze–thaw cycles compared with the control mix. The LCA showed notable reductions in GWP, acidification potential, and non-renewable energy demand when ISF and natural aggregates were partially substituted with RSF and RCA. The MCDA identified N50_R50_ISF0.3_RSF0.3 (50% RCA with 0.6% HSF) as the optimal mixture, achieving the highest eco-efficiency index (WSM = 0.80; TOPSIS = 0.73). These findings confirm that integrating RCA with hybrid steel fibers enhances the mechanical and durability performance of RCCP while substantially reducing environmental burdens, providing a viable strategy for low-carbon and circular pavement construction. Full article

As a vital support for sustainable energy power systems, shared energy storage has the potential to address challenges in energy storage within rural grids. Nevertheless, the comprehensive value of rural shared energy storage (RSES) exhibits scenario-dependent variations across operation models, and existing studies have neither revealed this sensitivity nor established a scientifically unified evaluation method. This study first identifies typical rural grid scenarios using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and analyzes RSES operation models. Then, this paper creates a three-dimensional evaluation system of RSES based on environmental, social, and governance (ESG) concepts that support sustainable development goals. Furthermore, to reconcile conflicts between subjective and objective weights, this paper proposes a combination weighting method based on Nash negotiation, subsequently using an improved technique for order preference by similarity to an ideal solution (TOPSIS) for multi-attribute decision-making. Finally, this paper completes simulations and discussions by an improved IEEE 33 bus system. The decision-making trial and evaluation laboratory (DEMATEL) technique and sensitivity analysis validate the validity and feasibility of the method proposed from horizontal and vertical dimensions. Based on the results, preferred strategies of RSES currently are energy aggregation and service purchase, for which this study provides recommendations. Full article

With the increasing complexity of transportation systems, traditional qualitative descriptions fail to objectively reflect the level of autonomy in traffic signal control systems—especially the lack of a systematic evaluation framework that links technology synergy, task autonomy, and system-level autonomy. To address this critical systematic gap, this study integrates the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to develop a systematic quantitative classification model for assessing system autonomy. The model constructs a three-level indicator framework—“technology–task–system”—based on the systematic closed-loop architecture of traffic signal control systems (upper interaction layer + lower technology chain layer), thereby enabling a holistic and quantitative evaluation of traffic signal control system autonomy. Results indicate that human involvement in the system decreases from 86% at the non-autonomous L0 level to 13% at the fully autonomous L3 level. This systematic quantitative method first reveals the inherent evolution logic of system autonomy (technology → task → system). Additionally, it provides a theoretical foundation for two key applications: the performance comparison across different traffic signal control systems and the planning of their intelligent development pathways—filling the gap of scattered, non-systematic evaluations in existing research. It also serves as a practical tool for these applications. Full article