Search Results (2,435)

Inductive power transfer (IPT) is a promising charging approach for rail trams because their trajectories are fixed, lateral displacement is constrained by the rails, and charging infrastructure can be installed at predetermined locations. Nevertheless, the long vehicle body, high power demand, variable air gap, and dynamic operating conditions of rail trams impose stringent requirements on magnetic-coupler design. This study proposes a bibliometric-assisted analytic hierarchy process (AHP) framework for the comprehensive performance evaluation of magnetic couplers used in rail–tram IPT systems. The framework considers five performance dimensions: power-efficiency characteristics, spatial characteristics, power density, time characteristics, and energy-transfer capability. Bibliometric keyword-occurrence statistics are introduced as an external source of evidence to support the initial construction of AHP judgment matrices, thereby reducing the exclusive dependence of conventional AHP on the judgments of a small expert group. A 2M2T low-floor tram is used as a case study, and two magnetic-coupler configurations, namely the $2\times 1$ and $3\times 1$ configurations, are evaluated using electromagnetic and circuit-simulation results. The case study illustrates the application of the proposed framework to the comparison of magnetic-coupler configurations under the operating and installation constraints of the investigated tram. The present validation is limited to simulation-based analysis of one tram platform and two configurations; further experimental and multi-configuration validation is required. 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

Türkiye’s strategic geographical location offers an exceptional opportunity for solar energy harvesting, yet optimizing large-scale investments requires rigorous pre-assessment methodologies. This study presents a comprehensive multi-criteria techno-economic analysis and real-world validation of a 13.18 MWp solar power plant (SPP) located in Kirsehir, a region characterized by high solar irradiance (1750 kWh/m²). Utilizing PVsyst software, four distinct configurations—monofacial and bifacial modules at 21° and 25° tilt angles—were systematically simulated and evaluated across varying equity-to-loan ratios using key financial indicators (NPV, IRR, PI, and Payback Period). The simulation results identified the 21° bifacial configuration, enhanced by the innovative integration of high-albedo industrial calcite (CaCO₃) waste as ground cover, as the optimal engineering solution. Crucially, the accuracy of this optimization was evaluated against 12 months of field data. While the raw measured annual production was recorded as 22,793,323 kWh, the validation was strictly based on the production adjusted for grid outages (23,499,604 kWh). Comparing this adjusted value with the simulated annual generation (22,816,114 kWh) yielded a total annual discrepancy of only 3% and a volumetrically weighted average error of 5.07%. Furthermore, to isolate model fidelity from inter-annual meteorological variability, the validation was assessed using the Performance Ratio (PR). The adjusted volumetrically weighted PR (87.43%) demonstrated a remarkably close alignment with the simulated PR (87.48%), exhibiting a marginal deviation of merely 0.05%. These performance metrics indicate a general consistency between the simulation model and operational field records across the evaluated period. Environmentally, the maximized energy yield of the 21° bifacial system facilitates the avoidance of approximately 6507.58 tonnes of CO₂ emissions annually. This research not only establishes the viability of scalable, low-cost calcite ground covers but also provides a highly robust, de-risked decision-support framework for utility-scale PV investments in similar geographic latitudes. Full article

Photovoltaic technology selection in Gulf Cooperation Council (GCC) desert environments is affected by coupled dust, thermal, ultraviolet (UV), humidity, and salinity stresses, which are not fully represented by static weighting and additive multi-criteria decision-making models. This study develops a GCC evidence-calibrated nonlinear decision-support framework that integrates published literature-derived GCC/desert-stress calibration, adaptive hybrid entropy–desert weighting, and bipolar fuzzy Einstein aggregation. The framework is applied to compare passivated emitter and rear cell (PERC), tunnel oxide passivated contact (TOPCon), and heterojunction technology (HJT) photovoltaic technologies using calibrated evidence from Qatar, the United Arab Emirates, Saudi Arabia, and Oman. The results show that dust tolerance receives the highest final hybrid weight (0.258), followed by thermal resistance (0.228), UV resistance (0.207), efficiency (0.173), and cost effectiveness (0.134). The nonlinear Einstein aggregation ranks HJT first (0.889), followed by TOPCon (0.861) and PERC (0.742). Benchmark comparison with TOPSIS, VIKOR, and PROMETHEE II shows high rank agreement, while Monte Carlo perturbation analysis indicates that HJT preserves the first rank in 93% of perturbation runs. The proposed framework links PV technology selection with published GCC desert-stress evidence and provides a reproducible basis for technology prioritization in harsh solar energy deployment environments. A stress-to-decision translation table is also provided to clarify how desert degradation mechanisms are converted into decision criteria and reusable selection guidance. Full article

Context: The Agile methodology has been prevalent in the software industry for more than two decades, marking a shift from plan-driven to market-driven approaches and introducing various challenges. While the literature identifies numerous challenges in Agile development, little attention has been given to their ranking and prioritization, which are critical for effective project management and decision making. This study fills this gap by combining empirical evidence from the literature and practitioners. Objectives: This study aims to identify and hierarchically prioritize the most recent challenges faced by Agile practitioners during product development. To achieve this, a Systematic Literature Review (SLR) was conducted using 115 published studies between 2010 and 2025 followed by empirical data collection from 30 Agile experts through semi-structured interviews conducted with practitioners from Agile companies and an online survey. This study applies Cumulative Voting (100-Dollar Test) and Multi-Criteria Decision Making (MCDM) techniques to rank and prioritize these challenges. Results: The SLR identifies several recurring Agile challenges; however, limited research has focused on their ranking and prioritization. The present study reveals new challenges, such as user interface complexities, lack of pre-development and pre-operational cost information, and lack of cost scalability at the module and feature levels. The current study identifies Inadequate Architecture (22%), Lack of Standardized Framework (18%), Communication and Coordination (16%), Poor Requirement Verification (13%), and Minimum Documentation (8%) as the most significant challenges. Conclusions: This study provides valuable insight for Agile practitioners and organizations, enabling more informed project planning, resource allocation, and strategic decision making. By focusing on the most critical challenges, teams can enhance software quality, streamline processes, and improve overall productivity in Agile environments. Full article

Olive cultivation is an important agricultural sector in Albania, where disease monitoring remains challenging due to fragmented orchards, heterogeneous management practices, and limited adoption of advanced sensing technologies. Recent studies have shown the potential of unmanned aerial vehicles (UAVs), sensor systems, and artificial intelligence (AI) for monitoring specific olive diseases in different olive-growing regions. However, the suitability of olive diseases reported in Albania for monitoring with UAVs and AI has not yet been systematically assessed. This paper examines the main olive diseases relevant to Albania using a semi-quantitative, literature-based multicriteria framework in which five monitoring criteria are scored from 1 to 3 and combined using equal weights. It also formalizes a UAV-first screening workflow that links image acquisition, AI-based canopy segmentation, feature extraction, anomaly scoring, decision thresholds, and targeted field or laboratory confirmation. Based on this assessment, the study identifies the most promising disease targets for future research and outlines key considerations for sensor selection and validation. The paper provides a context-specific foundation for future UAV- and AI-supported disease monitoring in Albanian olive groves. The revised analysis also distinguishes indicative acquisition targets from experimentally validated detection limits and specifies practical requirements for ground truth, radiometric calibration, dataset design, and geospatial validation. Full article

The rapid growth of Türkiye’s savings finance sector under Law No. 7292 has created demand for objective, multidimensional performance evaluation tools. This study assesses the financial performance of six savings finance companies in Türkiye over 2022–2024 using a hybrid CRITIC-EDAS multi-criteria decision-making model. Criterion weights for eight financial indicators—spanning profitability, operational efficiency, growth, and financial structure—were derived objectively via CRITIC, while EDAS produced the rankings, which were applied to criterion-direction-normalized data because the dataset contains negative values. The operating expense-to-revenue ratio carried the greatest weight in 2022 and 2023; gross profit margin became dominant in 2024. Katılımevim led the rankings in 2022 (ASi = 1.000); Eminevim then took the lead in 2023 (ASi = 1.000) and held it in 2024 (ASi = 0.968), with Fuzulev second (ASi = 0.815). A normalization artifact affecting two revenue-denominator ratios for one company (İmece) in 2024 was corrected through winsorization. Validity was confirmed in two stages: Spearman correlations between EDAS and the TOPSIS, MABAC, and MARCOS rankings exceeded the 0.89 reliability threshold in all three years, and a nine-scenario sensitivity analysis supported the rankings’ robustness. These findings give regulators, investors, and managers a replicable framework for evaluating performance in this young, underexamined sector and point to operational efficiency and outlier management as priorities for oversight. 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

This study develops an interpretable machine learning framework to predict the compressive strength of concrete incorporating blast-furnace slag (BFS). To address the critical issue of data leakage prevalent in conventional random splitting, a rigorous grouped validation strategy, specifically the GroupKFold algorithm, was implemented based on unique mixture proportions. Seven machine learning algorithms, including linear baselines and tree-based ensembles, were comprehensively evaluated. Results indicate that the XGBoost model achieved the highest predictive accuracy and stability, yielding a mean Root Mean Squared Error (RMSE) of 5.39 MPa and a minimal standard deviation of 0.54 MPa. A multi-criteria decision-making (MCDM) approach mathematically confirmed XGBoost as the optimal model. Furthermore, SHapley Additive exPlanations (SHAP) combined with data-density rug plots were utilized to uncover the non-linear interactions between BFS and other components. Rather than asserting direct causality, the SHAP analysis provides robust model-based associations that align with macroscopic physical expectations while strictly preventing over-interpretation in sparse data regions. Finally, a conceptual graphical user interface (GUI) is proposed to bridge the gap between theoretical models and future batch-plant deployment. This research balances rigorous high-precision prediction with transparent interpretability for BFS concrete design. Full article

The industrial sector faces one of the biggest challenges in decarbonization, mainly due to the high costs associated with the development and implementation of low-carbon, energy-efficient technologies and solutions. The long lifespan of industrial assets and infrequent replacement contribute to maintaining high levels of energy consumption and emissions. As electric motors represent a significant portion of energy consumption in industries, improving their efficiency generates substantial reductions in consumption, energy demand, and emissions, thus optimizing overall energy performance. This article proposes an integrated guideline for the application of multi-criteria decision-making (MCDM) methods, computational thinking (CT), and technical standards in industrial energy management problems. To validate this proposal, the guidelines were applied to a real-world case of electric motor selection in an industrial complex. In this context, the structured analysis of the problem, when based on computational thinking, MCDM methods, and technical standards, provides transparency and traceability to decisions. The motor-selection case study, which incorporated computational thinking and MCDM tools (AHP/TOPSIS) aligned with technical standards, demonstrated that these integrated guidelines can substantially improve decision-making in industrial contexts by structuring selection problems and aligning them with the strategic objectives of organizations. Full article

In operational terms, post-fire landscapes require monitoring priorities that reflect hydrogeomorphic susceptibility rather than burned extent alone. This study tests whether European Forest Fire Information System (EFFIS) burned area evidence, combined with open terrain, rainfall, drainage, soil, land cover, and settlement context data, can be translated into source-to-output traceable monitoring priority units across Mediterranean and Iberian–Atlantic regions. The backbone integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL) driver structuring, cell criticality score (CCS) screening from the CCS-V0 conventional weighted linear GIS baseline to CCS-V5 staged refinement, upper-quartile (Q75) hotspot topology, multi-criteria decision analysis/cost penalty (MCDA/COST-PEN) ranking, and quadratic unconstrained binary optimization (QUBO)-ready selected/reserve organization. Across Vesuvius–Campania, Attica, Cyprus, Portugal, and Spain, the screening outputs show procedural portability without implying geomorphological equivalence, field-confirmed hydrogeomorphic damage, or cross-theater hazard comparability: hotspot geometry, dominance, fragmentation, and candidate composition remain theater-dependent. In Vesuvius–Campania, CCS-V1 rainfall conditioning reduced Q75 hotspot clusters from 58 to 18 and increased the dominant cluster ratio from 23.1% to 63.07%; in Portugal, CCS-V5 contracted hotspot support from 17,240 to 862 cells while increasing dominance to 66.13%. The final QUBO-ready layer retained 11 of 32 candidate units. The contribution links (i) EFFIS/Moderate-Resolution Imaging Spectroradiometer (MODIS) source sector traceability and valid support construction, (ii) DEMATEL-informed CCS refinement and hotspot topology interpretation, (iii) role-based transfer testing across non-equivalent theaters, and (iv) QUBO-ready prioritization for verification-oriented post-fire monitoring. 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

Cultural heritage sites are affected by environmental and anthropogenic pressures that require decision-analysis methods capable of combining heterogeneous datasets while accounting for uncertainty. Multi-Criteria Decision Analysis (MCDA), particularly when integrated with Geographic Information Systems (GIS) and Earth Observation (EO) data, is widely used in geospatial analysis involving multiple, often conflicting criteria. This review examines the evolution, application domains, and methodological challenges of MCDA in cultural heritage risk assessment. The literature indicates a predominant reliance on weighting-based methods, especially the Analytic Hierarchy Process (AHP) combined with GIS-based weighted overlay techniques, while uncertainty treatment, temporal monitoring, validation, and multi-threat applications remain limited. Three illustrative applications show that asset-level, regional susceptibility, and historic-urban frameworks address complementary decision needs but differ in their data, expertise, and institutional requirements. Recent developments show a trend to combine MCDA with fuzzy logic, machine learning, and uncertainty modeling, although methodological consistency across these approaches remains uneven. The findings suggest that multi-criteria risk assessment for cultural heritage may depend less on introducing new analytical techniques and more on improving the integration of existing methods. Incorporating repeatable environmental observations, sensitivity analyses, multi-threat assessment, and stakeholder participation may support a more coherent and reproducible approach to heritage-risk assessment. Full article

Video Anomaly Detection (VAD) has emerged as a promising technology for automated surveillance and industrial monitoring. However, selecting an appropriate VAD architecture for real-world deployment remains challenging because benchmark-oriented evaluations often overlook practical considerations such as computational efficiency, latency, maintainability, supervision requirements, and long-term operational stability. To address this gap, this study proposes an expert-driven Multi-Criteria Decision Analysis (MCDA) framework for the deployment-oriented selection of VAD architectures. Eight representative architecture families were evaluated against six industrially relevant criteria, with scenario-specific priorities derived using the Best–Worst Method (BWM) from five domain experts across four representative industrial deployment scenarios. Edge-oriented architectures ranked first in three scenarios, achieving MCDA scores of 4.26, 3.90, and 4.07, whereas lightweight CNN-based architectures achieved the highest score (4.12) in the resource-constrained scenario. Inter-expert agreement ranged from Kendall’s W = 0.54 to 0.85, and Monte Carlo analysis confirmed the robustness of rankings, with top-rank probabilities of 69–74% for edge-oriented architectures and 100% for lightweight CNNs in the resource-constrained scenario. These findings demonstrate that architectural suitability depends on the deployment context rather than on a universally superior modeling paradigm, and that industrial VAD should be approached as a deployment-oriented systems engineering problem. The proposed framework provides a transparent and robust basis for aligning VAD architecture selection with operational requirements. Full article

Aggregation operators are central to multi-criteria decision-making under complex fuzzy information, where the additional phase dimension of complex fuzzy sets captures periodic or cyclical uncertainty beyond the reach of classical fuzzy sets. Existing Archimedean families used in the complex-fuzzy aggregation-operator literature, namely the algebraic, Einstein, Hamacher, and Aczél–Alsina families, are all generated from integer-order kernels; complete monotonicity of a generator’s pseudo-inverse, the condition known to be necessary and sufficient for a bivariate Archimedean construction to extend consistently to an arbitrary number of arguments, has not, to our knowledge, been established or invoked as a design criterion within that literature. To address this gap, this paper introduces a new family of Complex Fuzzy Mittag-Leffler (CFML) operators generated by a two-parameter additive generator that is built from the one-parameter Mittag-Leffler function $E_{\alpha }$ and a positive exponent $\lambda$. The completely monotone character of this generator guarantees the above consistency across dimensions for every aggregation exponent no smaller than one, and the fractional order $\alpha$ supplies a tunable additional degree of freedom that reweights how criteria are compensated during aggregation. The associated operational laws and the corresponding weighted averaging and weighted geometric operators are defined and proved to be idempotent, bounded, and monotone. Notably, the classical Aczél–Alsina and algebraic product operators emerge as exact limiting cases as the fractional order tends to one. A complete multi-criteria decision-making algorithm is proposed and illustrated, for demonstration purposes only, through a hypothetical case study evaluating generative artificial-intelligence diagnostic systems, with a sensitivity analysis showing that varying the fractional order and the aggregation exponent can alter alternative rankings relative to the classical limiting operators, illustrating the added flexibility that the fractional order provides. Full article