Search Results (4,289)

A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials. Full article

Background: Given the need to optimize Panax notoginseng cultivation, screen high-quality germplasm, and clarify its insufficiently elucidated genetic–phenotype–quality associations (e.g., saponin accumulation), this study was conducted. Methods: Agronomic traits were measured, saponin accumulation was determined via high-performance liquid chromatography (HPLC), and comprehensive performance was evaluated through integrated cluster analysis and fuzzy membership function assessment; additionally, single-nucleotide polymorphism (SNP)-based genetic diversity analysis was conducted to explore the genetic basis of trait variations. Results: Agronomic traits exhibited coefficients of variation (CVs) of 2.95–18.12%, with primary root length showing the highest variability. Phenotypic cluster analysis divided the materials into three groups. Group I (“Miaoxiang No.1”, “Dianqi No.1”, “Miaoxiang Kangqi No.1”) was characterized by tall plants, sturdy stems, heavy roots, and long/large leaves. Saponin determination results revealed significant differences in notoginsenoside R1, ginsenoside Rb1, ginsenoside Re, ginsenoside Rd, and total saponins among cultivars (order: “Zijing” > “Dianqi No.1” > original cultivar > “Miaoxiang Kangqi No.1” > “Miaoxiang No.1” > “Miaoxiang No.2”), with “Zijing” having the highest total saponin accumulation (18.13%); no significant difference was observed in ginsenoside Rg1 accumulation. The GATK initially identified 16,329,600 SNPs, and 115,930 high-quality SNPs were retained after Samtools filtration. SNP-based Neighbor-joining (NJ) clustering grouped the cultivars into three categories, with the original cultivar clustered alone as one category. Through comprehensive evaluation, three superior germplasm lines (“Miaoxiang Kangqi No.1”, “Miaoxiang No.1”, “Dianqi No.1”) were identified. A significant negative correlation (p < 0.05) was found between compound leaf petiole length and saponin accumulation. Conclusions: This integrated analytical strategy clarifies the links between genetics, phenotype, and quality, providing a scientific foundation for P. notoginseng germplasm screening and facilitating future molecular breeding efforts. Full article

This paper addresses the fundamental problem of seed selection in semi-supervised clustering, where the quality of initial seeds has a significant impact on clustering performance and stability. Existing methods often rely on randomly or heuristically selected seeds, which can propagate errors and increase dependence on expert labeling. To overcome these limitations, we propose FA-Seed, a flexible and adaptive model that integrates active querying with self-guided adaptation within the framework of fuzzy hyperboxes. FA-Seed partitions the data into hyperboxes, evaluates seed reliability through measures of membership and association density, and propagates labels with an emphasis on label purity. The model demonstrates strong adaptability to complex and ambiguous data distributions in which cluster boundaries are vague or overlapping. The main contributions of FA-Seed include: (1) automatic estimation and selection of candidate seeds that provide auxiliary supervision, (2) dynamic cluster expansion without retraining, (3) automatic detection and identification of structurally complex regions based on cluster characteristics, and (4) the ability to capture intrinsic cluster structures even when clusters vary in density and shape. Empirical evaluations on benchmark datasets, specifically the UCI and Computer Science collections, show that our approach consistently outperforms several state-of-the-art semi-supervised clustering methods. Full article

The quality risks of land trusteeship services are increasingly prominent, leading to reduced crop yields for farmers and land degradation; however, relevant research remains insufficient. This paper aims to identify and evaluate the quality risk level of land trusteeship services. It comprehensively adopts a field survey, web crawler technology, and expert consultation methods to identify quality risk types, and then uses the fuzzy comprehensive evaluation method to assess the risk level based on survey data from Chinese farmers. The main conclusions are as follows: (1) Overall, the quality risk level of land trusteeship services is at a relatively high risk level. In terms of spatio-temporal patterns, the quality risk level shows an upward trend, and the quality risk level of mid-production services is increasing at the fastest rate. There are significant variations in service quality risk across prefecture-level cities in the Shandong Province of China. (2) In terms of risk heterogeneity, the quality risk level of small-scale pure farmers is higher than that of part-time farmers and large professional farmers, in that order. The quality risk level of the “farmer + service organization” model is higher than that of the “farmer + intermediary + service organization” model. According to the order of the quality risk level of different crops, the ranking (from highest to lowest) is cash crops, wheat, and corn. (3) The high quality risks of land trusteeship services will impact the multifunctionality of land systems. It exacerbates the land pollution and fertility degradation because of excessive application of chemical inputs like pesticides, fertilizers, and mulch by service organizations. It consequently destroys ecological systems, hinders sustainable agricultural development, and impacts farmers’ income and national food security by reducing yields. The research findings contribute to controlling the quality risks of land trusteeship services and protecting land. Full article

For centuries, artisans have resolved intricate engineering conundrums with intuitive ingenuity, bequeathing a legacy of design wisdom that remains largely untapped in contemporary biomimetics. This “anthro-creative” form of biomimicry, deeply embedded within traditional crafts such as Chaozhou woodcarving, is predominantly tacit and qualitative, which has traditionally eluded systematic interpretation. To address this, we propose the Hierarchical Biomimetics-Based Evaluation System (HBBES), a transdisciplinary framework that couples expert-defined hierarchies through the Analytic Hierarchy Process (AHP) with perceptual assessments from one hundred public evaluators via Fuzzy Comprehensive Evaluation (FCE). Applied to canonical works—including the Lobster and Crab Basket (overall score: 4.36/5.00)—the HBBES revealed a striking finding: both expert and public valuations are anchored not in structural hierarchy, but in aesthetic resonance, particularly the craft’s lifelike morphological analogy and nuanced modulation of light. Beyond offering a replicable pathway for translating artisanal intuition into operative design principles, this study proposes a culture-driven paradigm for biomimetics, bridging intangible heritage with technological innovation. Full article

This research introduces a novel dual CO₂ storage (DCS) approach by simultaneously storing CO₂ gas in abandoned mines and securing it within mineralized backfill. For this method, CO₂ mineralized backfill materials (CMBM) are pumped into CO₂ mineralized storage segments (CMSSs) to support the roof while gaseous CO₂ is injected into gaseous CO₂ storage segments (GCSSs) to maximize storage amounts. This study focuses on the Yu-Shen coal area in Yulin City, Shaanxi Province, China. A three-level evaluation model was constructed to predict DCS feasibility based on the analytic hierarchy process (AHP) and fuzzy comprehensive assessment method. The model was generalized and applied to the whole coal area. Each indicator affecting adaptability is plotted on a thematic map to determine the corresponding membership degree. The aptness for 400 boreholes distributed in the entire area was derived and a zoning map which divides the whole area into different suitability was drawn. This paper puts forward a mathematical model for predicting DCS suitability. The findings establish an engineering paradigm that simultaneously addresses CO₂ sequestration, industrial waste recycling, and ecological water table preservation. The research results can provide references for determining the site of DCS, contributing to the generalization of DCS in a larger range. Full article

In recent years, with continuous technological advances, BIM technology has gradually expanded from the traditional construction industry into the field of hydraulic engineering. Since BIM models, which span the entire project lifecycle, contain substantial amounts of data and the operation and maintenance phase accounts for the majority of this lifecycle, higher computational demands are imposed. Consequently, the lightweighting of BIM models has become imperative. In this study, an improved Quadric Error Metric (QEM) algorithm was applied to simplify the geometric data of the constructed BIM model. The research investigates whether the lightweight model can reduce the computational requirements during its application in the operation and management of hydraulic engineering, thereby enhancing its general applicability. Furthermore, a fuzzy comprehensive evaluation model was established to assess the effectiveness of the lightweighting process. The experimental results indicate that the optimized model occupies significantly less memory space. Additionally, model loading time and rendering CPU usage were substantially improved. The lightweight effect was evaluated as excellent based on the fuzzy comprehensive evaluation. Full article

Runway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated passive, semi-active, and intelligent controllers such as PID, H∞, and ANFIS; however, the comprehensive application of a robust adaptive neuro-fuzzy inference system (RANFIS) to active landing-gear control has not yet been addressed. The novelty of this work lies in combining robustness with adaptive learning of fuzzy rules and neural network parameters, thereby filling this critical gap in the literature. To investigate this, a six-degrees-of-freedom aircraft dynamic model was developed, and three controllers were comparatively evaluated: model-based neural network (MBNN), adaptive neuro-fuzzy inference system (ANFIS), and the proposed RANFIS. Performance was assessed in terms of rise time, settling time, peak value, and steady-state error under stochastic runway excitations. Simulation results show that while MBNN and ANFIS provide satisfactory control, RANFIS achieved superior performance, reducing vibration peaks to ≤0.3–1.0 cm, shortening settling times to <1.5 s, and decreasing steady-state errors to <0.05 cm. These findings confirm that RANFIS offers a more effective solution for enhancing comfort, safety, and structural durability in next-generation active landing-gear systems. Full article

Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, which can address the fuzziness of customers’ emotional responses in comments and the nonlinearity of modeling. However, due to the black box problem in ANFIS, the nonlinearity of the modeling cannot be shown explicitly. To solve the above problems, a chaos-driven ANFIS approach is proposed to develop customer preference models using online comments. The model’s nonlinear relationships are represented transparently through the fuzzy rules obtained, which provide human-readable equations. In the proposed approach, online reviews are analyzed using sentiment analysis to extract the information that will be used as the data sets for modeling. After that, the chaos optimization algorithm (COA) is applied to determine the polynomial structure of the fuzzy rules in ANFIS to model the customer preferences. Using laptop products as a case study, several approaches are evaluated for validation, including fuzzy regression, fuzzy least-squares regression, ANFIS, ANFIS with subtractive cluster, and ANFIS with K-means. Compared to the other five approaches, the values of mean relative error, variance of error, and confidence interval of validation error are improved based on the proposed approach. Full article

Higher education institutions play a critical role in the transition to a low-carbon future due to their research capacity and societal influence. Accordingly, the calculation of greenhouse gas (GHG) emissions and the prioritization of mitigation strategies are of particular importance. In this study, a comprehensive campus-level GHG inventory was prepared for a public university in Türkiye in alignment with the ISO 14064-1:2018 standard, and mitigation strategies were evaluated. To prioritize these strategies, both the classical Policy Modeling Consistency (PMC) index and, for the first time in the literature, a fuzzy extension of the PMC model was applied. The results reveal that the total GHG emissions for 2023 amounted to 4888.63 tCO₂e (1.19 tCO₂e per capita), with the largest shares originating from investments (31%) and purchased electricity (28.38%). While the classical PMC identified only two high-priority actions, the fuzzy PMC reduced score dispersion, resolved ranking ties, and expanded the number of high-priority actions to seven. The top strategies include awareness programs, energy-efficiency measures, virtual meeting practices, advanced electricity monitoring, and improved data management systems. By comparing the classical and fuzzy approaches, the study demonstrates that integrating fuzzy logic enhances the transparency, reproducibility, and robustness of strategy prioritization, thereby offering a practical roadmap for campus decarbonization and sustainability policy in higher education institutions. Full article

Anti-lock braking systems (ABSs) play a vital role in vehicle safety by preventing wheel lockup and maintaining stability during braking. However, their performance is strongly affected by variations in tire–road friction, which often limits the effectiveness of conventional controllers. This research proposes and evaluates a fuzzy logic controller (FLC)-based ABS using a quarter-vehicle model and the Burckhardt tire–road interaction, implemented in MATLAB/Simulink. Two input variables (slip error and slip rate) and one output variable (brake pressure adjustment) were defined, with triangular and trapezoidal membership functions and 15 linguistic rules forming the control strategy. Simulation results under diverse road conditions—including dry asphalt, concrete, wet asphalt, snow, and ice—demonstrate substantial performance gains. On high- and medium-friction surfaces, stopping distance and stopping time were reduced by more than 30–40%, while improvements of up to 25% were observed on wet surfaces. Even on snow and ice, the system maintained consistent, albeit modest, benefits. Importantly, the proposed FLC–ABS was benchmarked against two recent studies: one reporting that an FLC reduced stopping distance to 258 m in 15 s compared with 272 m in 15.6 s using PID, and another where PID outperformed an FLC, achieving 130.21 m in 9.67 s against 280.03 m in 16.76 s. In contrast, our system achieved a stopping distance of only 24.41 m in 7.87 s, representing over a 90% improvement relative to both studies. These results confirm that the proposed FLC–ABS not only demonstrates clear numerical superiority but also underscores the importance of rigorous modeling and systematic controller design, offering a robust and effective solution for improving braking efficiency and vehicle safety across diverse road conditions. Full article

The Huangshui River Basin is located in the transition zone between the Loess Plateau and the Qinghai–Tibet Plateau, characterized by a fragile hydrological and ecological environment. Groundwater serves as a vital water source for local economic development and human livelihood. With the acceleration of urbanisation and climate change, groundwater resources face challenges such as pollution and over-exploitation. This study employs an improved DRASTIC model, tailored to the characteristics of the groundwater system in the Huangshui River Valley of the upper Yellow River, to integrate groundwater resources, groundwater environment, and ecological environment systems. Improving the DRASTIC model for groundwater vulnerability assessment. A two-tiered evaluation system with nine indicator parameters was proposed, including six groundwater quality vulnerability indicators and five groundwater quantity vulnerability indicators. Fuzzy analytic hierarchy process and entropy weight method were used to determine the weights, and Geographic Information System (GIS) spatial analysis was employed to evaluate groundwater vulnerability in the Huangshui River basin in 2006 and 2021. The results indicate that the proportion of areas with high groundwater quality vulnerability increased from 10.7% in 2006 to 31.57% in 2021, while the proportion of areas with high groundwater quantity vulnerability decreased from 22.33% to 14.02%. Overall, groundwater quality vulnerability in the Huangshui River basin is increasing, while groundwater quantity vulnerability is decreasing. Based on the evaluation results of water quality and quantity vulnerability, protection zoning maps for water quality and quantity were compiled, and preventive measures and recommendations for water quality and quantity protection zones were proposed. Human activities have a significant impact on groundwater vulnerability, with land use types and groundwater extraction coefficients having the highest weights. This study provides a scientific basis for the protection and sustainable use of groundwater in the Huangshui River basin. Full article

Smaller data environments with expert systems are generally driven by the need for interpretable reasoning frameworks, such as fuzzy rule-based systems (FRBS), which cannot often quantify epistemic uncertainty during decision-making. This study proposes a novel Layered Entropy Model (LEM) comprising three semantic layers: Membership Function Entropy (MFE), Rule Activation Entropy (RAE), and System Output Entropy (SOE). Shannon entropy is applied at each layer to enable granular diagnostic transparency throughout the inference process. The approach was evaluated using both synthetic simulations and a real-world case study on the PIMA Indian Diabetes dataset. In the real data experiment, the system produced sharp, fully confident decisions with zero entropy at all layers, yielding an Epistemic Confidence Index (ECI) of 1.0. The proposed framework maintains full compatibility with conventional Type-1 FRBS design while introducing a computationally efficient and fully interpretable uncertainty quantification capability. The results demonstrate that LEM can serve as a powerful tool for validating expert knowledge, auditing system transparency, and deployment in high-stakes, small-data decision domains, such as healthcare, safety, and finance. The model contributes directly to the goals of explainable artificial intelligence (XAI) by embedding uncertainty traceability within the reasoning process itself. Full article

An Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for paediatric wrist injury classification (fracture versus sprain) was developed utilising infrared thermography (IRT). ANFIS combines artificial neural network (ANN) learning with interpretable fuzzy rules, mitigating the “black-box” limitation of conventional ANNs through explicit membership functions and Takagi–Sugeno rule consequents. Forty children (19 fractures, 21 sprains, confirmed by X-ray radiograph) provided thermal image sequences from which three statistically discriminative temperature distribution features namely standard deviation, inter-quartile range (IQR) and kurtosis were selected. A five-layer Sugeno ANFIS with Gaussian membership functions were trained using a hybrid least-squares/gradient descent optimisation and evaluated under three premise-parameter initialisation strategies: random seeding, K-means clustering, and fuzzy C-means (FCM) data partitioning. Five-fold cross-validation guided the selection of membership functions standard deviation (σ) and rule count, yielding an optimal nine-rule model. Comparative experiments show K-means initialisation achieved the best balance between convergence speed and generalisation versus slower but highly precise random initialisation and rapidly convergent yet unstable FCM. The proposed K-means–driven ANFIS offered data-efficient decision support, highlighting the potential of thermal feature fusion with neuro-fuzzy modelling to reduce unnecessary radiographs in emergency bone fracture triage. Full article

This paper presents a structured strategy for integrating the Sustainable Development Goals (SDGs) into university courses by linking them to competencies and learning outcomes. The proposed methodology, based on fuzzy logic, evaluates the degree of alignment between teaching activities and selected SDGs through matrices that connect competencies with assessment activities and expected learning outcomes, improving the gap regarding the inclusion of the SDGs and their articulation in terms of competencies. The approach was applied to two subjects from the Master’s Degree in Renewable Energy and Energy Efficiency at the Distance University of Madrid: “Electricity Market” and “Wind Energy”. In both cases, the learning outcomes were redesigned, and the activities were adjusted to ensure meaningful incorporation of sustainability principles into the curriculum. The method enables quantification of each activity’s contribution to the SDGs and supports a critical review of curriculum design to ensure coherent integration. The results indicate that project-based activities show the highest alignment with the SDGs, particularly with Goals 7, and 12, which achieve an average rating of 0.7 (high). The developed tool provides a practical and replicable solution for sustainability-oriented curriculum planning and can be adapted to other disciplines and educational programs. Full article