Search Results (558)

The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane’s boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane’s movements. The Neural Network algorithm optimized the crane’s movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane’s structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane’s fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life. Full article

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to classify 19-channel EEG entropy features into three classes (dementia, stroke, healthy control; N = 45; 15 per class). The architecture suggests four new designs. First, the Channel Importance Attention (CIA) block, which adaptively learns to re-weight the importance of electrodes via squeeze-excitation. Second, the dual-branch encoder, which combines the global multi-head self-attention with the local depthwise-separable convolution. Third, the gated sigmoid fusion mechanism. Fourth, the bottleneck residual classification head, to solve overfitting. Eight entropy feature sets: Amplitude-Aware Permutation Entropy (AAPE), Attention Entropy (AttEn), Dispersion Entropy (DisEn), Distribution Entropy (DistrEn), Fluctuation-based Dispersion Entropy (FDispEn), Fuzzy Entropy (FuzEn), Linear Gaussian Estimation of the Conditional Entropy (LinEn), and Symbolic Dynamics (SyDy) were evaluated individually with stratified 5-fold cross-validation on within-fold SMOTE augmentation. Results: EEG-ChTABNet consistently outperformed the baseline Transformer on all 8 feature sets. DisEn and SyDy features yielded peak classification accuracy of 73.3% (AUC: 0.823 and 0.857, respectively) compared to the corresponding baseline of 57.8% and 55.6%. SyDy achieved the best overall AUC of 0.857 and the dementia detection sensitivity was up to 86.7% over multiple feature sets. Conclusions: EEG-ChTABNet shows the effectiveness of channel-adaptive, dual-branch Transformer Designs for EEG-based neurological classification from Small-Sample Entropy Feature Data, and Identifying SyDy and DisEn as the Most Discriminative Feature Representations for Three-Class Neurological EEG Classification. Full article

Against the backdrop of rapid urbanization, urban sewer networks face increasing challenges, including infrastructure deterioration and imbalanced resource allocation. Conventional single-dimensional risk assessment methods fail to capture the coordinated development of such complex systems. This study proposes a comprehensive HFM framework integrating Health (H), Failure (F), and Management (M), coupled with a Coupling Coordination Degree (CCD) model and an obstacle degree model to evaluate system interactions and identify key constraints. A game theory-based weighting approach combining AHP and CRITIC is applied to integrate subjective and objective weights, while fuzzy mathematics is used for multidimensional evaluation. CCD spatial analysis is conducted at the drainage unit scale. Results show that: (1) The system is in a transitional stage from disorder to coordination, with CCD values mainly ranging from 0.4 to 0.8 and exhibiting significant spatial heterogeneity. (2) High-risk areas tend to have better health conditions and stronger management inputs, whereas low-risk areas may still face latent risks due to insufficient management. (3) Key obstacles are concentrated in Failure and Management systems, particularly pipeline functionality and management capacity. Overall, system risk arises from mismatches between risk sources and management allocation rather than purely structural deficiencies. The proposed framework effectively identifies imbalance areas and priority interventions, supporting the transition toward proactive risk regulation. Full article

To address the scattered protection efforts and uneven effectiveness of traditional villages in southwestern Hubei, this study focuses on 92 nationally recognized traditional villages in Enshi Prefecture. By integrating literature research, field investigation, and multi-source data fusion, we developed an innovative model that combines the Analytic Network Process (ANP), entropy weight, and fuzzy comprehensive evaluation, thereby integrating subjective and objective weighting to improve evaluation accuracy. A quantitative evaluation was conducted across 13 criteria and 32 indicators, including traffic conditions, intangible cultural heritage resources, and industrial foundation. The results reveal that traditional villages in Enshi Prefecture exhibit a significant spatial pattern of “overall dispersion with local concentration,” accompanied by a high concentration index. Traffic conditions, intangible cultural heritage, and infrastructure emerge as the core factors affecting protection effectiveness, and a spatial differentiation pattern of “two cores and one corridor” is identified within the region. Based on the quantitative evaluation, we propose targeted cluster protection strategies, including a “dual-core multi-node” transportation network, “three-industry linkage” industrial collaboration, and a living heritage approach that integrates cultural relics with intangible cultural heritage. These strategies were validated in pilot villages such as Yejiaoyuan Village, resulting in significant increases in village satisfaction and tourist volume. The findings provide methodological support and practical paradigms for the systematic protection and sustainable development of traditional villages in southwestern ethnic minority areas. Full article

This paper presents a Sim2Real policy transfer framework for distributed control in cyber-physical production systems using State-Based Potential Games (SbPGs). While fuzzy inference systems (FISs) or other conventional control policies provide interpretable and stable control policies for manufacturing processes, their direct deployment in real systems is often affected by Sim2Real discrepancies caused by actuator imperfections, sensor uncertainty, and process variability. To address this limitation, we propose a hybrid control architecture in which an optimized rule-based conventional control policy (i.e., FIS used in a non-adaptive, expert-knowledge-driven manner) serves as a baseline controller and SbPG-based policy adaptation refines the control actions online, while keeping the distributed manner, and is proven to converge. To evaluate robustness during Sim2Real deployment, deterministic and stochastic noise injection mechanisms are introduced to emulate systematic actuator biases and random disturbances. The proposed framework is validated on a laboratory-scale distributed production system. Experimental results in both simulation and real-world environments demonstrate that the SbPG-based adaptation compensates for disturbances and maintains production objectives under actuator, sensor, and parameter uncertainties. Compared to standalone FIS control, the proposed approach consistently reduces overflow and power consumption while satisfying production demands under noisy operating conditions. Additional ablation studies further confirm the robustness of the policy transfer strategy and the effectiveness of global and local interpolation mechanisms in the SbPG learning. Full article

Architectural planning often involves balancing multiple and potentially conflicting objectives, such as safety, economy, functionality, and aesthetics. However, conventional benchmarking approaches typically focus on single performance dimensions and provide limited support for multi-objective decision-making. To address this limitation, this study proposes a benchmark learning framework for multi-objective street house planning that explicitly incorporates architects’ planning preferences. The framework integrates fuzzy sets to define preference functions, indifference curves to represent trade-offs and derive preference weights, and utility functions to quantify satisfaction levels. In addition, Data Envelopment Analysis (DEA) and efficient frontier theory are employed to evaluate planning efficiency and identify optimal benchmark cases. Using empirical data from 627 street houses, the results indicate that the proposed approach effectively captures architects’ subjective preferences while providing an objective assessment of planning efficiency. The integration of indifference curves and the efficient frontier enables explicit visualization of trade-offs, whereas the combination of utility functions and efficiency analysis facilitates the identification of benchmark learning cases. The proposed framework provides a systematic approach to multi-objective optimization in architectural planning by bridging subjective decision-making with quantitative performance evaluation. It offers practical guidance for architects and planners and contributes to the advancement of benchmark-based methodologies in complex design environments. Full article

This study develops a GIS-based multicriteria decision analysis framework to assess the territorial suitability of onshore wind energy in Colombia. The proposed approach combines technical and socio-environmental suitability modelling with territorial interpretation based on conflict and governance, moving beyond conventional siting models focused mainly on wind resource availability and infrastructure proximity. The technical assessment included wind speed, wind power density, terrain slope, land cover, land use, and proximity to electrical grids, main roads, settlements, and water bodies. In addition, a National Conflict Index and a National Governance Index were constructed to represent broader territorial conditions that may affect project implementation. Quantitative variables, including wind speed, wind power density, terrain slope, and distance-based criteria, were transformed onto a common suitability scale using linear fuzzy membership functions, whereas qualitative variables, including land cover and land use, were incorporated through categorical reclassification. The National Conflict Index and National Governance Index were first constructed using CRITIC to obtain objective weights for their internal variables. Subsequently, the final onshore wind suitability criteria were weighted through the linear Best–Worst Method based on expert judgment. The standardized suitability layers and corresponding BWM-derived weights were integrated through weighted spatial overlay to generate a national suitability map, while the conflict and governance indices were used to interpret the territorial conditions associated with the resulting suitable areas. The results show a highly selective territorial pattern, with the most favorable areas concentrated mainly in La Guajira (1286.09 km²) and Cesar (574.45 km²), and more fragmented secondary opportunities in Nariño, Boyacá, Norte de Santander, Cundinamarca, Atlántico, and Magdalena. Three territorial intervention scenarios were identified: priority intervention, complementary or selective development, and low relative priority. The main contribution of the study is the articulation of a BWM-weighted technical and socio-environmental suitability model with CRITIC-based conflict and governance indices, offering a replicable framework to support strategic planning and public policy decisions for onshore wind deployment in Colombia. Full article

This paper proposes a robust multi-criteria decision-making (MCDM) framework for evaluating and selecting Point-of-Sale (POS) systems in the context of Retail 5.0, where decisions involve multiple criteria and inherent uncertainty. The approach integrates entropy-based objective weighting with fuzzy AHP for subjective assessment, while incorporating Z-number theory to explicitly account for decision-makers’ confidence. Unlike conventional methods that assume equal importance between subjective and objective components, the proposed framework introduces a confidence-adjusted integration mechanism, in which Z-numbers are used to dynamically modulate the influence of subjective judgments based on their reliability. This enables a more balanced and context-sensitive weighting process that better reflects both data characteristics and human uncertainty. The contribution of this study is twofold: methodologically, it develops a reliability-driven integration framework that enhances the robustness and credibility of criteria weighting; practically, it demonstrates the applicability of the approach through a real-world POS system selection case. The results confirm that the proposed method provides more stable and informative decision outcomes, highlighting its effectiveness in complex decision-making environments. Full article

Assessing environmental flows downstream of reservoirs under changing climate and increasing water demand remains a critical challenge in catchment management. This study presents an integrated framework for optimizing environmental flow releases by explicitly linking reservoir operation with climate change and population growth. The key novelty lies in the development of a modified objective function that incorporates environmental flow requirements alongside evolving hydrological and demand conditions. Reservoir inflows were simulated using an artificial intelligence-based rainfall–runoff model, employing a neuro-fuzzy inference system to capture nonlinear relationships between climate variables and runoff. Future rainfall projections were derived from four general circulation models (ACCESS1.0, CanESM2, MIROC5, and NorESM-M1) across four-time horizons (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The simulated inflows were coupled with a reservoir operation model to optimize environmental flow releases, with system performance evaluated using reliability and vulnerability metrics. Results show that climate change alone has a limited impact on environmental flow supply; however, when combined with population-driven increases in water demand, significant reductions in system performance occur. In the worst-case scenario, the reliability of meeting environmental flow requirements drops below 20%, accompanied by a marked increase in system vulnerability. These findings demonstrate that water demand pressures play a dominant role in shaping future environmental flow availability. The proposed framework provides a robust and adaptable approach for integrating hydrological variability and socio-economic drivers into reservoir management, supporting more informed decision-making for balancing water supply and ecosystem sustainability under future uncertainty. Full article

Land-use planning in ecologically sensitive landscapes requires balancing biodiversity conservation, ecosystem service provision, agricultural production, settlement expansion, and infrastructure demand within a single spatial system. This challenge is particularly significant in Mediterranean environments, where long-term land transformations and increasing development pressures intensify conflicts among competing land-use priorities. Accordingly, the present study develops an integrated spatial zoning and decision-support framework for Osmaniye Province, southern Türkiye. The framework integrates fuzzy multi-criteria evaluation, CatBoost-based machine learning, SHAP-based interpretability, and NSGA-II multi-objective optimization. The workflow followed a sequential decision process in which an expert-derived zoning surface was first established through fuzzy evaluation, reconstructed from continuous spatial predictors using CatBoost, interpreted through SHAP, and refined through NSGA-II under explicit spatial constraints. By using the expert-derived zoning surface as the learning target, the CatBoost stage aimed to evaluate the internal consistency and spatial learnability of the planning logic within a present-day zoning context. The results indicated that the integrated framework distinguished conservation, controlled-use, and development priorities while identifying the key environmental and anthropogenic drivers shaping class-specific zoning outcomes. The final zoning structure allocated 37.9% of the study area to conservation, 43.6% to controlled use, and 18.5% to development. The study shows that by including a transitional zone with varying proportions of conservation, controlled use, and development, a more balanced distribution among the three goals can be achieved compared to a fixed partition into these three zones. The findings further demonstrate that this approach is more effective than current zoning, which does not accommodate such trade-offs. Full article

The increasing digitalization of manufacturing systems and emphasis on sustainable development are transforming maintenance from a purely operational function into a strategic driver of customer value in the digital economy. However, the relationship between maintenance maturity and consumer-perceived sustainability remains largely unexplored. This study addresses the following research questions: (RQ1) How does maintenance maturity influence consumer-perceived sustainability and trust? (RQ2) How can operational resilience be linked to customer perception through a structured modeling approach? (RQ3) Which maintenance strategy provides the highest combined operational and sustainability value? To address these questions, the Integrated Maintenance Maturity Model with a Customer-Centric Sustainability Layer (IMMM–CCSL) is proposed. The framework links maintenance maturity with consumer sustainability perception using a structured fuzzy-based aggregation approach. Five consumer-oriented dimensions are considered: product lifecycle extension, service continuity and trust, consumer maintenance experience, perceived ecological performance, and post-sale engagement. A composite Customer Sustainability Index (CCSI) is introduced to quantify the relationship between maintenance maturity and consumer perception. The model is applied in an illustrative case study comparing reactive, preventive, predictive, and AI-enhanced maintenance strategies. The results indicate that CCSL values range from 0.709 to 0.749, while the overall CCSI equals 0.729, suggesting a consistently high level of consumer-perceived sustainability associated with higher maintenance maturity. Predictive maintenance demonstrates the highest contribution to both operational reliability and perceived sustainability outcomes within the analyzed case. Overall, the IMMM–CCSL framework offers a structured, interpretable tool for aligning maintenance strategy with sustainable production and consumption objectives, supporting managers and policymakers in translating technical capabilities into measurable consumer sustainability outcomes. The findings should be interpreted as exploratory and case-specific, given the illustrative nature of the study. Full article

This study addresses the challenge of prioritizing Virtual Reality (VR) and Augmented Reality (AR) applications in Product Lifecycle Management (PLM) under multiple conflicting criteria. A comprehensive fuzzy Multi-Criteria Decision-Making (FMCDM) framework is proposed to support robust and unbiased decision-making. The methodology integrates multiple objective weighting techniques, including Entropy, Criteria Importance Through Intercriteria Correlation (CRITIC), Method based on the Removal Effects of Criteria (MEREC), and Standard Deviation, which are aggregated using the Bonferroni operator to obtain balanced criterion weights. The Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is employed as the primary ranking approach, supported by comparative methods such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), Evaluation based on Distance from Average Solution (EDAS), Weighted Aggregated Sum Product Assessment (WASPAS), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for validation. The results indicate that Virtual Reality Digital Prototyping and Design Review (A3) is the most preferred alternative, achieving the highest utility value (0.95267), followed by Augmented Reality-Assisted Assembly and Inspection Guidance (A1) and Augmented Reality-Supported Maintenance and Operator Training (A4). A high Stability Index of 0.9133 confirms robustness, and sensitivity analysis shows stable rankings. The framework provides a reliable and scalable decision-support system for smart manufacturing. Full article

In this work, a novel entropy-weighted fuzzy c-means variation, referred to as Group-based Entropy Weighted Fuzzy C-Means (GEWFCM), is proposed. This variation introduces a semantic level of partitioning of features into groups. This approach enables the provision of optimal semantic meaning to the clusters, thereby capturing the intrinsic structure of the features, which are naturally grouped into homogeneous semantic sets; the weights are independent of the clusters. The cluster weights provide a direct measure of the importance of each group, determining which dimensions of the phenomenon are relevant, and the intragroup weights determine the most relevant features within a group. Additionally, GEWFCM is computationally more efficient than other cluster-specific weighted fuzzy clustering algorithms, due to the independence of the weights from the clusters. The efficacy of the method was assessed by evaluating census data from 16 Italian cities, with the objective of partitioning urban settlements based on characteristics of residential buildings, including construction technique, period, number of floors, and state of conservation. The findings suggest that the proposed algorithm effectively captures the semantic meaning of clusters. In addition, a comparative analysis between GEWFCM and the well-known Entropy Weighted Fuzzy C-Means (EWFCM) algorithm showed that, although both algorithms provide high similarity of results for all case studies, GEWFCM is significantly faster. Full article

This study analyzes tourist loyalty in the Monarch Butterfly Biosphere Reserve by integrating affinity-based segmentation and the Forgotten Effects Theory within a fuzzy logic framework. The objective was to identify how visitor affinities condition the indirect construction of loyalty in contexts of high environmental complexity. Data were collected through a structured questionnaire administered to 316 tourists using a non-probabilistic sampling approach. Using the Pichat Algorithm and the Forgotten Effects Theory, the research captured gradual membership patterns and mediated relationships that conventional models often overlook. Results indicate that, while age, particularly Generation X, acts as a connecting axis, postgraduate education levels generate a polarization of visitor perceptions across segments. Significant forgotten effects (up to 0.30) were identified, suggesting that variables such as satisfaction, entertainment, and relaxation act as mediating mechanisms between learning, perceived value, and the intention to revisit. This study suggests that loyalty is not constructed directly but is indirectly shaped by affinity-based visitor structures. It recommends that management strategies evolve toward environmental edutainment models and that marketing efforts be diversified according to differentiated visitor profiles. These findings demonstrate the utility of fuzzy logic for the strategic management of high-value ecological destinations. Full article

Background: Accurate and early detection of hematological disorders from microscopic peripheral blood smear images remains a technically challenging task due to inherent imaging limitations, including noise contamination, low contrast, staining variability, and significant cellular overlap. Conventional deep learning-based object detection frameworks often exhibit limited robustness under such conditions and demonstrate reduced sensitivity to small-scale morphological structures, particularly platelets and abnormal cell variants. Methods: To address these challenges, this study proposes a hybrid detection framework that integrates a fuzzy logic-driven image preprocessing module with the YOLOv11 object detection architecture. The proposed preprocessing pipeline employs adaptive fuzzy membership functions to normalize pixel intensity distributions, suppress high-frequency noise, and enhance edge-defined cellular boundaries. This transformation produces a structurally optimized feature representation, improving downstream feature extraction and localization performance. The proposed framework was evaluated on a curated dataset of 3000 annotated microscopic blood smear images spanning five hematological classes. Results: Experimental results show that the fuzzy logic module improves mAP@0.5 by +3.4% and mAP@0.5:0.95 by +3.6%, confirming its effectiveness in enhancing both classification and localization accuracy. Conclusions: These findings demonstrate the robustness and practical applicability of the proposed hybrid approach under challenging imaging conditions. Full article