Search Results (1,172)

Due to the increase in the number of ontologies in various domains, ranking them to facilitate their selection for reuse is an important task in ontology engineering to date. To assess the multi-faceted quality configurations of candidate ontologies, Multi-Criteria Decision Making (MCDM) frameworks are used. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an MCDM method that is widely adopted for the task of ontology ranking. However, traditional TOPSIS implementations rely almost exclusively on the Euclidean distance metric. This introduces severe rank volatilities and systematic biases when evaluating heterogeneous ontology metadata. To address these limitations, this paper introduces a novel Multi-Distance Ensemble TOPSIS (Ensemble-TOPSIS) method for robust ontology ranking. Rather than forcing a localized geometric choice, the proposed Ensemble-TOPSIS method simultaneously projects alternative ontologies through a multi-distance ensemble composed of Euclidean, Chebyshev, cosine, and Mahalanobis configurations. The Ensemble-TOPSIS method was applied to three datasets of ontologies from the artificial intelligence, agricultural, and biological domains to test its scalability and multi-domain applicability. The experimental results reveal that all the ontologies from the three domains were successfully ranked by the proposed Ensemble-TOPSIS method. Furthermore, the statistical rank correlation using Spearman’s $\rho$, Kendall’s $\tau$, and the $WS$ rank similarity coefficients was calculated between the TOPSIS variants, and the proposed Ensemble-TOPSIS method achieved the highest correlation in the majority of cases. Moreover, a comprehensive Monte Carlo simulation across 1200 stochastically generated, non-linear, and skewed multicollinear decision domains established the asymptotic stability of the proposed Ensemble-TOPSIS method, which achieved the highest global mean performance ($\overline{\rho }=0.88$, $\overline{\tau }=0.75$, $\overline{WS}=0.94$), minimized rank variance (${\sigma }^2\left(WS\right)=0.0006$), and optimally maximized the lower-bound worst-case performance profile ($\rho =0.67$) compared to individual baseline formulations. Full article

The water resources carrying capacity (WRCC) lays a foundational basis for long-term coordinated water resource governance. Based on the Driving–Pressure–State–Impact–Response (DPSIR) framework, this study constructed a WRCC evaluation system containing 21 indicators and adopted a combined weighting method integrating entropy weight and coefficient of variation. Weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and geographical detector tools were jointly applied to quantify spatial-temporal WRCC disparities within the Jialu River Basin, alongside extraction of core driving forces during 2010–2022. Marked spatial disparities existed across administrative units, with basin-average WRCC ranging from 0.18–0.35. Zhengzhou maintained relatively high carrying levels, Kaifeng stayed chronically low, Xuchang improved after 2019, while Zhoukou experienced an overall decline, forming a relatively stable spatial pattern: Zhengzhou > Zhoukou > Xuchang > Kaifeng. Socioeconomic factors stood among the major drivers of spatial divergence. R&D expenditure and urbanization rate exhibited the highest explanatory capacity, with respective q statistics of 0.58 and 0.57. In contrast, natural factors including precipitation and groundwater reserves showed limited impacts, with q values of only 0.11 and 0.09. Factor interaction analysis showed that bivariate enhancement was the primary interaction type (70.53%), followed by nonlinear enhancement (21.05%) and nonlinear weakening (8.42%). The mean q value of the interactive effects reached 0.58, which was 45.0% higher than that of individual factors, suggesting prominent multi-factor synergistic effects. These results deliver empirical evidence for differentiated watershed regulation and cross-jurisdictional water–ecological coordination, and offer actionable governance insights for densely urbanized plain tributary basins with intense human–water conflicts. Full article

Hybrid marine energy systems that integrate wave and current technologies can improve resource complementarity and spatial utilization. However, the ranking stability of selected hybrid concepts under changes in criterion weights, score assumptions, and multi-criteria decision analysis (MCDA) methods requires further examination. This study extends an existing two-stage concept-selection procedure by evaluating four shortlisted wave energy converter–hydrokinetic turbine configurations using stochastic weight-space sampling, criterion-wise weight sensitivity, cross-method consistency, and bounded score-perturbation analyses. A fixed normalized decision matrix is first evaluated using the Simple Additive Weighting (SAW) method across three sets of 10,000 criterion-weight scenarios generated using normalized-uniform, Dirichlet α = 1, and Dirichlet α = 0.5 distributions. The same scenarios are then evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), with ranking consistency quantified using Spearman’s rank correlation and complete-ranking agreement. Score sensitivity is subsequently examined through bounded one-point perturbations of the Stage 2 criterion scores, with SAW and TOPSIS recalculated under equal criterion weights to identify dominance-breaking and rank-reversal conditions. The oscillating water column–Savonius configuration, W1H3, remains first-ranked under all three sampled weight distributions because its normalized criterion scores are equal to or higher than those of every competing configuration across all five criteria. Criterion-wise sensitivity analysis shows that W1H3 is not outranked over the investigated weight range, although it ties with the point absorber–Savonius configuration, W2H3, when the full weight is assigned to mooring synergy or control compatibility. A crossover between W2H3 and the oscillating water column–hybrid Savonius–Darrieus configuration, W1H4, occurs at a co-location-feasibility weight of 0.384615. Across the three weight-sampling distributions, SAW and TOPSIS achieve complete-ranking agreement of 65.91–87.08%, with mean Spearman rank correlations of 0.9318–0.9742; the remaining differences are confined to the ordering of W2H3 and W1H4. Bounded score perturbations show that single one-point score change is sufficient to break the dominance of W1H3 over W2H3, whereas four changes are required for W2H3 to attain a unique first rank under both methods. The results demonstrate that W1H3 is rank-stable under the investigated weight and method variations for the adopted decision matrix, while the score-perturbation analysis identifies the bounded score changes under which the preferred ranking may change. Full article

This study develops an integrated Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making (MCDM) framework to systematically evaluate and rank composite material combinations based on 24 fibers (16 natural and 8 synthetic), 15 matrices (thermosets, thermoplastics, and biopolymers), and 9 fiber volume fractions (30–70%) for UAV inlet applications. Ten evaluation criteria covering technical performance, environmental sustainability, and economic viability were weighted using AHP pairwise comparisons based on Saaty’s 1–9 scale, yielding a consistency ratio of CR = 0.009, which confirms the reliability of the judgments. The TOPSIS analysis identified Carbon (PAN-HM)/Epoxy as the optimal composite material, achieving the highest TOPSIS score of 0.8893. In contrast, Flax/Epoxy emerged as the best natural fiber composite, with a TOPSIS score of 0.2686, indicating a performance gap of approximately 231% in favor of the synthetic composite. Comprehensive sensitivity analysis across four weighting scenarios (Equal, Technical, Environmental, and Economic) confirmed the stability of the reinforcement rankings, with Carbon (PAN-HM) remaining the top synthetic fiber and flax the top natural fiber across all scenarios. The findings contribute to the growing body of knowledge on sustainable aerospace materials and provide practical guidance for UAV designers seeking to optimize material selection for micro-turbojet engine inlet components, supporting the development of more environmentally responsible UAV designs while maintaining the performance requirements for safe and reliable operation. Full article

Existing industrial buildings represent a critical but under-addressed field for operational carbon emission reduction, as retrofit decisions are constrained by production continuity, limited investment capacity, and heterogeneous technical options. This study developed a decision support framework integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate and prioritize operational phase decarbonization retrofit schemes for existing industrial buildings. The framework was applied to the Thyssenkrupp S Plant in eastern China, where ten candidate schemes were identified through an energy audit, on-site investigation, and expert consultation. The results show that heating, ventilation, and air conditioning (HVAC) operational control and temperature set-point optimization ranked first, followed by lighting operational management and automatic control. These management-based measures offer strong near-term applicability because of their low investment, short payback periods, limited implementation disturbance, and immediate emission reduction benefits. Their sustained effectiveness, however, requires standardized procedures, staff education, energy monitoring, and appropriate automation. Rooftop photovoltaics provide the largest annual carbon reduction but have a lower short-term priority because of their high upfront investment. Expert-consistency testing and sensitivity analyses, including criterion weight perturbation, preference scenarios, and Monte Carlo simulation, support the robustness of the leading ranking pattern. The findings support staged retrofit planning that prioritizes durable management measures in the short term, equipment-level efficiency improvements in the medium term, and renewable energy deployment in the long term. Full article

Off-highway vehicle electrification requires traction motors combining high torque density with reliable performance across demanding duty cycles, yet finite-element-based optimization remains computationally demanding for broad design-space exploration. This study addresses the gap with a sensitivity-analysis-based, surrogate-assisted multi-objective optimization framework for a 12-pole/72-slot, 120 kW Interior Permanent-Magnet Synchronous Motor (IPMSM) for a compact wheel-loader drivetrain, coupling Ansys Motor-CAD with Ansys OptiSLang. A Latin Hypercube sensitivity study of thirteen geometric parameters identifies the dominant design drivers, and an evolutionary algorithm operating on the validated surrogate produces a Pareto-optimal set, from which the final design is selected using the CRITIC–TOPSIS method applied to finite-element-validated feasible designs. Relative to the baseline, the validated performance shows a 7.4% increase in continuous torque, a 4.0% increase in peak torque, a 4.0% increase in efficiency, and a 53.5% reduction in torque ripple, with mass essentially unchanged, while also revealing that surrogate predictions were markedly optimistic relative to the finite-element results. These findings demonstrate an efficient, reliable route to high-performance IPMSM design for off-highway applications. Full article

To achieve the multi-objective collaborative optimization of milling processes, orthogonal experiments are conducted to develop prediction models for vibration acceleration and milling force, and range analysis together with variance analysis are adopted to reveal the sensitivity of each milling parameter to machining performance. Taking low vibration, small milling force and high material removal rate (MRR) as optimization objectives, the Multi-Objective Whale Optimization Algorithm (MOWOA) is employed to tackle this multi-criteria optimization problem, and a set of Pareto non-dominated solutions with balanced trade-offs are acquired. By integrating the weight assignment of the Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), comprehensive decision-making for all candidate schemes is implemented in accordance with practical machining requirements of users, and the optimal milling process parameters are determined. The results indicate an inherent trade-off among machining efficiency, milling load and machine tool vibration. An increase in the material removal rate will inevitably lead to simultaneous rises in milling force and machine tool vibration magnitude. The optimal combination of process parameters screened to meet comprehensive multi-objective requirements is spindle speed n = 12,000.00 r/min, feed rate v_f = 1048.26 mm/min, and axial milling depth a_p = 3.00 mm. Full article

End-of-life electric-vehicle and plug-in hybrid (EV/PHEV) battery packs pose a recurrent decision: refurbish, redeploy in second-life storage, recycle, or reject. Technical condition, safety, economics, regulation, traceability, and environmental benefit interact, making pathway selection a systems-level decision problem. This paper develops a risk-informed multi-criteria framework for the conditional viability of refurbished batteries under data-scarce conditions. Failure mode, effects, and criticality analysis (FMECA) supplies a pathway-specific residual-risk penalty; multi-criteria decision analysis (weighted-sum and the Technique for Order of Preference by Similarity to Ideal Solution, TOPSIS) orders four alternatives on six benefit criteria; and a screening-level avoided-burden indicator, not a life-cycle assessment, positions the environmental criterion. An Integrated Viability Index (IVI) offsets weighted benefits against the risk penalty through one tunable coefficient. All inputs are illustrative and literature-informed; the demonstration tests decision logic, not empirical pathway performance. Preference is conditional: refurbishment leads under economic and technical priority with credible risk mitigation, second-life reuse under environmental priority, and recycling under safety, regulatory, and infrastructure constraints, while rejection never leads. As the risk penalty rises, leadership migrates traceably toward recycling, and IVI–TOPSIS divergence localizes exactly where the risk treatment changes the decision. A proposed Refurbished-Battery Suitability Index (RBSI) would couple measured diagnostics to the IVI; its calibration remains future work. Full article

This paper proposes a framework based on the Analytic Network Process (ANP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for the tactical planning of a synchromodal freight transport operation. The motivation stems from the lack of a comprehensive identification, analysis, and categorization of the factors influencing tactical planning in synchromodal logistics and their evaluation for integration into decision-making. We develop a Decision Support System (DSS) to select the most suitable network configuration for a synchromodal operation based on the critical factors of tactical planning, considering 17 criteria into five dimensions. The framework has been applied to a real case study of a Spanish transport company considering different network configurations from Martorell (Spain) to Wolfsburg (Germany). The results show that the most relevant criteria for tactical planning are the legal conditions, the incentive policies for the choice of intermodal services, the level of flexibility in transport operation, the number of external logistics organizations in the region for intermodal services, and the level of available transport resources. Full article

Electric motorcycle morphology design involves relationships among whole-vehicle profiles, exposed local modules, affective semantics, evaluation criteria, and AI-assisted generation constraints. These relationships are often treated as separate outputs, making it difficult to explain how perceptual evidence supports design generation. This study proposes an evidence-weighted heterogeneous graph mapping approach for analyzing symmetry–asymmetry patterns in electric motorcycle morphology design. A dataset of 176 electric motorcycles from 31 brands was constructed and organized into whole-vehicle archetypes and local styling modules. Kansei engineering and semantic differential evaluation obtained perceptual data from 78 valid questionnaires. The Analytic Hierarchy Process (AHP) was applied to determine expert-based criterion weights, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank whole-vehicle and local-module alternatives. In the proposed graph, vehicle samples, styling modules, Kansei dimensions, evaluation weights, ranking outputs, and prompt constraints are represented as heterogeneous nodes, while perceptual association, module–whole coordination, symmetry–asymmetry balance, and evidence-to-generation mapping are represented as weighted edges. The results identify system integration, form proportion and tension, safety perception, visual futurism, and brand identity as dominant perceptual dimensions. A second-round evaluation with 71 participants assessed the correspondence between the generated concepts and the target perceptual semantics. The proposed graph mapping provides an explicit and traceable evidence path from perceptual evaluation and multi-criteria decision support to AI-assisted concept-generation constraints. Full article

Light spectral composition is an important environmental factor affecting strawberry growth and fruit production in controlled-environment agriculture. This study evaluated the effects of five light-emitting diode (LED) spectra on photosynthetic performance, vegetative growth, flowering, fruit production, and final biomass in ‘Akihime’ strawberry (Fragaria × ananassa). Plants were grown for 31 weeks under LED treatments with different proportions of blue, green, red, and far-red light, while the photosynthetic photon flux density was maintained at 250–300 μmol m⁻² s⁻¹. Photosynthetic responses were influenced mainly by developmental stage, although significant treatment × weeks after transplanting interactions were detected for the maximum relative electron transport rate and light saturation point. LE3 (B15.6:G18.7:R49.2:FR16.3) produced greater petiole length and root dry weight than the control. LE2 (B19.2:G30.0:R49.5:FR1.3) showed the earliest observed anthesis, although final flower number and fruit number did not differ among treatments. LE3 and LE4 produced significantly greater mean fruit weight than the control, and LE3 ranked highest in the Best–Worst Method–Technique for Order Preference by Similarity to an Ideal Solution (BWM–TOPSIS) evaluation. These findings suggest that strawberry responses depended on the complete spectral composition rather than on the red-to-blue ratio alone. Stage-specific spectral management may therefore improve the performance of ‘Akihime’ strawberry in indoor vertical farming. Full article

Multi-Level Governance (MLG) frameworks effectively diagnose the complexity of regional renewable energy transitions but lack operational mechanisms for institutional implementation. This study develops a hybrid evidence-based architecture that integrates the Viable System Model (VSM) with computational intelligence and objective multi-criteria decision analysis. Methodologically, a systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol of 339 peer-reviewed articles (2015–2025) feeds a Latent Dirichlet Allocation (LDA) model that extracts K = 30 strategic topics (semantic coherence $C_v$ optimized over K = 5–40), operationalizing System 4 environmental sensing. In parallel, a 1 km² pixel-based spatial model integrates a National Conflict Index (INC, 2019–2024) with technical feasibility layers (Global Wind Atlas v4.0, Solargis, Servicio Geológico Colombiano) and applies CRITIC (CRiteria Importance Through Intercriteria Correlation) objective weighting and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) prioritization (System 3 control). Robustness is confirmed through $\pm 10$–$20\%$ weight perturbation (Spearman > 0.92). Empirically, the framework reveals a localization paradox: approximately 68% of optimal wind zones (>9 m/s at 100 m hub height) in the Colombian Caribbean overlap with the highest national conflict quartile, narrowing a theoretical capacity exceeding 100 GW (50 GW offshore wind, 30 GW onshore wind, 42 GW solar PV, 1.17 GW geothermal) to roughly 24 GW of governance-viable capacity. Scenario calibration (Accelerated 80/20, Balanced 50/50, Justice-Oriented 30/70 technical/conflict weighting) demonstrates that System 5 normative orientation materially reshapes territorial prioritization. The framework advances VSM from a qualitative diagnostic metaphor to a reproducible governance architecture for high-variety regional contexts. Full article

Bioenergy development from biomass resources requires multi-criteria decision-making (MCDM) methods to rank and select suitable feedstock alternatives. Conventional TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a widely used MCDM method that assists in selecting alternatives based on their relative closeness to the ideal solution. Unlike the existing TOPSIS, which provides only an overall ranking of alternatives, this study proposes a framework that integrates TOPSIS ranking with threshold-based performance classification and mapping to enable a more comprehensive assessment and support decision-making, thereby improving the interpretability of complex multi-criteria decision problems. The proposed framework was applied to evaluate, rank, and select eight potential biomass feedstocks in Ethiopia by integrating feedstock availability, technological readiness, and economic criteria. The ranking results indicate that molasses is the most suitable, followed by non-edible oil crops (castor seed, Ethiopian mustard, and Jatropha curcas) and lignocellulosic residues (cereal, cane, and coffee residues), while water hyacinth ranked lowest. The results were mapped into a strategic implementation timeline, with molasses prioritized for the short term, non-edible oil crops for the medium term, and lignocellulosic residues for the long term in Ethiopia’s bioenergy development. The study further supports decision-makers in strategic energy planning and facilitates bioenergy development. Full article

Commercial seaweed extracts (SWEs) are widely used as plant biostimulants; however, information on formulation-specific optimum dosages for greenhouse-grown Capsicum annuum seedlings is limited. This study evaluated the effects of five commercial SWE formulations applied at five dosages (1–5 mL L⁻¹) on seedling growth, biomass accumulation, and root growth under greenhouse conditions. Morphological traits, biomass characteristics, and growth efficiency indices were assessed using univariate and multivariate statistical approaches to identify formulation-specific optimum application rates. Commercial SWE application significantly (p < 0.05) increased chlorophyll content, shoot and root growth, and fresh biomass, although the magnitude of the response varied among the formulations and application rates. The optimum dosages identified by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were ASCO at 2 mL L⁻¹, AQUA at 3 mL L⁻¹, BIO at 2 mL L⁻¹, KAT at 3 mL L⁻¹, and SAGA at 3 mL L⁻¹. Principal component and Pearson correlation analyses further demonstrated coordinated improvements across multiple growth traits. These findings demonstrate that optimum seedling performance depends on formulation-specific rather than universal application rates. The identified optimum dosages provide practical guidance for selecting commercial seaweed biostimulant application rates in greenhouse C. annuum nurseries. Full article

Public-data electric-vehicle charging-station siting needs a screening workflow that can use spatial proxies while keeping demand, grid-capacity, and implementation claims separate from the score. This study develops an interaction-aware Nonlinear Interaction–Einstein Aggregation (NI-EA) framework for Dubai and extends it with source-conditioned robust candidate-set diagnostics. From 7410 admitted candidate/amenity records, 5097 inside-boundary candidates are scored using a candidate-derived activity-density proxy, a charger-coverage-gap proxy, and a grid-access proxy. The analysis compares NI-EA with WSM, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Einstein aggregation; reconstructs a 63-scenario interaction/curvature/blending rank matrix; evaluates weighting, road-network, and official-DEWA source sensitivities; and reports necessary and possible top-K candidate sets, family-balanced finite-scenario acceptability, rank-displacement summaries, and bounded spatial-evidence context from official community, transport, parking, DEWA, and OpenStreetMap-derived sources. The baseline leader is S1421/Boonmax, while official-DEWA coordinate-source reconciliation changes the leader to S3473. Across the reconstructed interaction, weighting, road-network, and official-DEWA scenario families, the top-15 necessary core contains 12 candidates, and the top-15 possible envelope contains 18 candidates. Activity-radius and charger-count coverage alternatives are reported separately as proxy-definition sensitivities. TOPSIS has 0/15 top-15 overlap with NI-EA because it favors a different profile with much higher coverage-gap scores but low activity density. The reported output is therefore a source-conditioned planning shortlist and robustness audit, not an observed-demand map, feeder-capacity validation, financial feasibility assessment, or construction recommendation. Full article