Search Results (330)

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications. Full article

Hainan Island is one of the main tea-producing regions in South China. Climate change has increased agricultural instability, causing fluctuations in tea yield and quality. Based on daily surface meteorological data from 19 national meteorological observation stations on the island from 1990 to 2019, as well as related factors such as topography, a spatial analysis model for climate zoning indicators was established. Zoning indicators were spatialized through GIS spatial analysis, and fuzzy logic was applied to construct membership functions based on climatic elements to assess climatic suitability for tea cultivation. This approach helped refine zoning for tea planting areas and assess potential future climate changes. Results show high climatic suitability for tea production in spring (March-May) and autumn (September–October), but low suitability in summer (June–August) due to high temperatures and strong sunlight. The most suitable zone for tea planting is centered in the northeastern parts of the island; the suitable zone is mainly distributed in the central mountainous areas and the western coastal region; the sub-suitable zone mainly includes central and southern parts of Dongfang; and the unsuitable zone mainly includes eastern and southern parts of Dongfang and southern parts of Changjiang. Under future climatic scenarios, the island’s temperatures will further increase, and suitable temperature areas will shrink from the periphery toward the central mountainous regions. Precipitation will also increase over time, leading to an expansion of suitable precipitation areas on the island. This study helps promote sustainable tea production and the rational utilization of agricultural climate resources on Hainan Island. Full article

Dissolved Gas Analysis (DGA) of power system transformers has emerged as one of the most effective transformer health diagnosing tools by analyzing the gases dissolved in the insulating oil. There are various traditional DGA techniques like Key Gas Method, Roger’s Ratio, IEC ratio, Dornenburg’s Ratio, and Duval Triangle method. However, these techniques have limitations such as inconsistent results, the inability to detect low-energy faults, and reliance on expert knowledge due to complex interpretation. To overcome these limitations, this paper introduces an integrated fuzzy logic system that enhances DGA interpretation by combining the diagnostic strengths of Key Gas Method, Roger’s Ratio, IEC ratio, and Duval Triangle methods. To obtain a final, human-readable diagnosis, the output of each technique is incorporated into a higher-level fuzzy inference system once each is modeled separately with fuzzy logic, having known membership functions and rule bases. To test this model, oil samples of known results of different transformers are used and compared to the results given by the proposed fuzzy inference system. The proposed method is easier and more feasible for practical use since it not only improves fault detection accuracy and reliability but also allows for easier interpretation by non-specialists. This study makes an additional contribution to a higher-level, more effective, and more accurate method for transformer fault detection by overcoming the interpretational difficulties and weaknesses of conventional DGA approaches. Full article

As low temperature is a key factor affecting the growth and development of plants and the utilization of agricultural waste has significant research value, this study explores the effects of 16 agricultural wastes on the growth of P. fraseri under natural low-temperature conditions and evaluates its cold resistance capacity. Soil chemical properties were analyzed and all the wastes were found to exhibit alkalinity. The highest total nitrogen content was found in group A (garden soil/coir/municipal sludge = 7:1:2). In this group, the branch number, branch length, and branch diameter were the largest. Interestingly, the plants in group E (garden soil/coir/pig manure = 7:1:2) had the highest average number of new shoots, with 5.72. Analysis of the physiological indexes of leaves revealed that the proline content, superoxide dismutase activity, fresh weight, and dry weight of plants in group L (garden soil/coir/pear residue = 7:1:2) were the highest. The stomatal conductance and transpiration rate of the leaves of plants in group L were the largest, at 86.23 mmol∙m⁻²∙s⁻¹ and 1.67 mmol∙m⁻²∙s⁻¹, respectively. Furthermore, combined with morphological and physiological indicators for membership function analysis, the results show that plants in group A exhibited optimal growth under natural low temperature. Correlation analysis indicated varying degrees of correlation between 38 pairs of indicators, including branch number and branch length, intercellular CO₂ concentration and stomatal conductance, and leaf fresh weight and dry weight. Heatmap analysis showed that branch number, branch length, and branch diameter were highest in group A plants, while the highest levels of proline occurred in group L plants. In this study, groups A and L are recommended for growth under naturally low-temperature conditions. Full article

This paper systematically investigates interval-valued intuitionistic fuzzy (IVIF) sets and filters within the framework of hoop algebras, unifying and extending classical fuzzy set theory and intuitionistic fuzzy sets (IFS) in algebraic logic. We clarify the foundational relationships among fuzzy sets, IFS, and hoop algebras, and introduce novel characterizations of IVIF filters, including necessary and sufficient conditions for their existence and structure. Theoretical advancements include the demonstration that IVIF filters can be described via their endpoint functions, the establishment of a bounded distributive lattice of IVIF filters, and the identification of congruence relations induced by these filters. Algorithmic and numerical aspects are addressed through explicit pseudocode and detailed examples, illustrating how the verification and construction of IVIF filters can be performed in finite hoop algebras. Practical implications are highlighted in decision-making scenarios where modeling uncertainty and vagueness with interval-valued membership and non-membership degrees offers enhanced flexibility and robustness. Our results lay a rigorous foundation for further applications of IVIF filters in fuzzy logic, artificial intelligence, and multi-criteria decision analysis. Full article

In order to identify saline–alkali-tolerant rapeseed varieties suitable for cultivation on moderately saline–alkali soils and to expand the use of such lands, six rapeseed varieties were selected as experimental materials. Field experiments were conducted to evaluate agronomic traits, photosynthesis, stress physiology, yield, and quality throughout the entire growth period. Statistical methods, including correlation analysis, principal component analysis, membership function analysis, and cluster analysis, were employed to evaluate and select saline–alkali-tolerant varieties. The results indicated that H62 and 20C14 yielded the highest seed production, reaching 2287.99 kg·hm⁻² and 2277.15 kg·hm⁻², respectively. During the mid-to-late growth stages, the majority of agronomic traits, photosynthetic parameters, and stress physiology indicators for 20C14 were significantly superior to those of the other varieties. The results of the principal component analysis showed that the total root length at maturity stage, root–shoot ratio at flowering stage, and proline content at maturity stage were the most important indicators for screening saline–alkali-tolerant rapeseed varieties. A comprehensive analysis of these indicators revealed the following descending order of saline–alkali tolerance among the varieties: 20C14 > 20C17 > 20C4 > H62 > H158 > 17C2. Cluster analysis was performed to classify the rapeseed into strong saline–alkali-tolerant type (20C14 and 20C17), moderate saline–alkali-tolerant type (20C4, H62, and H158), and weak saline–alkali-tolerant type (17C2). Consequently, 20C14 and 20C17 are recommended as suitable rapeseed varieties for cultivation on soda saline–alkali soils. Full article

In this study, a hybrid sensor-based electronic nose circuit was developed using eight metal-oxide semiconductors and 14 quartz crystal microbalance gas sensors. This study included 100 participants: 60 individuals diagnosed with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers. A total of 338 experiments were performed using breath samples throughout this study. In the classification phase of the obtained data, in addition to traditional classification algorithms, such as decision trees, support vector machines, k-nearest neighbors, and random forests, the fuzzy logic method supported by the optimization algorithm was also used. While the data were classified using the fuzzy logic method, the parameters of the membership functions were optimized using a nature-inspired optimization algorithm. In addition, principal component analysis and linear discriminant analysis were used to determine the effects of dimension-reduction algorithms. As a result of all the operations performed, the highest classification accuracy of 94.58% was achieved using traditional classification algorithms, whereas the data were classified with 97.93% accuracy using the fuzzy logic method optimized with optimization algorithms inspired by nature. Full article

The reliability of decision-making algorithms within soft set theory is fundamentally constrained by their underlying membership functions. Traditional binary approaches overlook the implicit connections between the attributes a candidate possesses and those it lacks—connections that can be inferred from the wider candidate pool. To address this core challenge, this paper puts forward the Relational Membership Value Calculation (RMVC), an algorithmic framework whose core is a fine-grained relational membership function. Our approach moves beyond binary logic to capture these nuanced interrelationships. We provide a rigorous theoretical analysis of the proposed algorithm, including its computational complexity and robustness, which is validated through a comprehensive sensitivity analysis. Crucially, a comparative analysis using the Gini Index quantitatively demonstrates that our method provides significantly higher granularity and discriminatory power on a representative case study. The RMVC is implemented as an open-source Python program, providing a foundational tool to enhance the reasoning capabilities of AI-driven decision support and expert systems. Full article

Cottonseeds, rich in high-quality protein and fatty acids, represent a vital plant-derived feedstuff and edible oil resource. To systematically investigate genetic variation patterns in nutritional quality and screen superior germplasm, this study analyzed 26 nutritional quality traits and 8 fiber traits across 259 upland cotton (Gossypium hirsutum L.) accessions using multivariate statistical approaches. Results revealed significant genetic diversity in cottonseed nutritional profiles, with coefficients of variation ranging from 3.42% to 26.37%. Moreover, with advancements in breeding periods, the contents of protein, amino acids, and the proportion of unsaturated fatty acids (UFAs) increased, while oil content and C16:0 levels decreased. Correlation analyses identified significant positive associations (p < 0.05) between proteins, amino acids, UFAs, and most fiber traits, except for seed index (SI), fiber micronaire (FM), and fiber elongation (FE). Through a principal component analysis–fuzzy membership function (PCA-FMF) model, 13 elite accessions (F > 0.75) with high protein content, high UFA proportion, and excellent fiber quality were identified. These findings provide both data-driven foundations and practical germplasm resources for value-added utilization of cottonseed and coordinated breeding for dual-quality traits of nutrition and fiber. Full article

To improve the efficiency and reduce the cost of traditional sympodial bamboo harvesting, this study evaluated the effects of four harvesting intensities—selective harvesting, one-third clump, one-half clump, and complete clump harvesting—on understory plant diversity in pure Dendrocalamus giganteus stands over a five-year recovery period. A total of 36 species were recorded in the first year, increasing to 71 in the third year and stabilizing at 74 species by year five. Understory α-diversity showed an increasing trend followed by a decline. In the early recovery stage, species diversity was significantly correlated with soil chemical properties (p < 0.05), but no significant correlation was observed in the later stage. Fuzzy membership function analysis indicated that the 1/2 clump harvesting treatment outperformed others, ranking as follows: 1/2 clump > 1/3 clump > selective > complete clump harvesting. These results suggest that 1/2 clump harvesting is optimal for promoting understory vegetation growth, but its positive effects on biodiversity are time-limited, with the plant community showing a trend toward simplification over time. Full article

As a key economic tree in southern China, Camellia oleifera faces severe yield losses under drought. Grafting onto drought-tolerant rootstocks offers a potential mitigation strategy. To elucidate the impact of rootstocks on the drought resistance of the superior Camellia oleifera Abel. cultivar “Xianglin 210”, grafted seedlings with five scion–rootstock combinations, were subjected to gradient drought stress. Key physiological and biochemical indices related to photosynthesis, antioxidant enzymes, and osmotic adjustment were measured. Drought resistance was comprehensively evaluated using membership function analysis, and the expression of stress-responsive genes was quantified via quantitative real-time PCR (qRT-PCR). The results demonstrated that under drought stress, (1) stomatal conductance (Gs) decreased by 31.2–48.7%, while instantaneous water use efficiency (WUE) increased by 18.5–35.4%; (2) proline (Pro) and soluble sugars (SS) accumulated significantly, with increases of 2.3–4.1-fold and 1.8–3.2-fold, respectively; (3) activities of antioxidant enzymes were enhanced by 56–127%, mitigating oxidative damage; (4) membership function analysis ranked drought resistance as follows: Xianglin 27 (0.812) > Guangxi Superior Germplasm (0.698) > C. yuhsienensis (0.654) > Hunan Superior Germplasm (0.591) > Xianglin 1 (0.523); (5) qRT-PCR revealed significant upregulation of ABA signaling pathway genes (CoPYL6, CoPP2C75/51/24/26, CoSnRK2.8, and CoABI5) and transcription factors (CoLHY and CoWRKY70), indicating activation of drought-responsive regulatory networks. These findings provide a theoretical foundation for selecting drought-tolerant rootstocks and optimizing cultivation practices in Camellia oleifera, and provide practical criteria for selecting drought-tolerant rootstocks, facilitating sustainable Camellia oleifera cultivation in water-limited regions. Full article

This study investigated the effects of four selenium fertilizers (nano-Se, EDTA-chelated Se, organic Se, and microbial Se) at three concentrations (50, 25, and 12.5 mg·L⁻¹) on garlic (Allium sativum L. cv. ‘Xusuan 918’) through foliar application during critical growth stages. Comprehensive evaluation combining agronomic traits, yield components, nutritional quality (soluble sugars, vitamin C), and selenium accumulation revealed distinct fertilizer-specific responses. Organic Se at 50 mg·L⁻¹ (O1) maximized vegetative growth (21.83% increased plant spread), while 25 mg·L⁻¹ microbial Se (M2) showed optimal yield enhancement (10.04% higher bulb dry weight vs. CK). Notably, 50 mg·L⁻¹ nano-Se (N1) achieved simultaneous improvement in nutritional quality and selenium biofortification (29-fold bulb Se enrichment). Principal component analysis integrated with membership function method identified N1 treatment (D-value = 0.571) as the most effective protocol for selenium-enriched garlic production, demonstrating the importance of fertilizer selection for crop-specific selenium management strategies. Full article

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The composite assessment value of low-phosphorus tolerance (D) was calculated by evaluating these 12 response indicators through principal component analysis, in conjunction with the fuzzy membership function method. Nine low-phosphorus tolerance factors (LPTFs)—including above-ground fresh weight (0.69), below-ground fresh weight (0.52), total root length (0.56), root surface area (0.63), root volume (0.67), above-ground Pi concentration (0.78), below-ground Pi concentration (0.52), bioconcentration factor (0.77), and P utilization efficiency (−0.76)—showed significant correlations with D (p < 0.05). Utilizing these nine LPTFs, cluster analysis classified the 13 lines into the following three groups according to their low-phosphorus (P) tolerance: high-P-efficient, medium-P-efficient, and low-P-efficient lines. Under low Pi and Pi-deficiency treatments, line 27 was identified as a high-P-efficient line, while lines 1, 6, and 9 were classified as low-P-efficient lines. Notably, eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress, respectively. However, the high-P-efficient line 27 exhibited a stronger regulatory capacity with a higher expression of two genes (SPX4, SQD2) in the roots and nine genes (SPX1, SPX3, SPX4, PHT1;1, PAP10, PAP23, SQD1, SQD2, NPC4) in the leaves under low Pi stress. These findings reveal differential responses to low Pi stress among slash pine lines, with line 27 displaying superior low-P tolerance, enabling better adaptation to low Pi environments and the maintenance of normal growth, development, and physiological activities. Full article

Background/Objectives: Predicting comprehensive patient characteristics is essential for optimal individualized rehabilitation plans for acute stroke patients. However, current models primarily predict single outcomes. This study aimed to assess the applicability of latent class analysis (LCA) in rehabilitation practice by identifying comprehensive characteristics and associated predictors in acute stroke patients. Methods: We conducted a retrospective observational study using the Japan Association of Rehabilitation Database, including 10,270 stroke patients admitted to 37 acute-care hospitals between January 2005 and March 2016. Patients were classified using LCA based on outcomes at discharge, including Functional Independence Measure (FIM), National Institutes of Health Stroke Scale (NIHSS) subscales for upper-extremity function, length of hospitalization, and discharge destination. Predictor variables at admission included age, FIM scores, NIHSS subscales for upper-extremity function, stroke type, and daily rehabilitation volume. Results: 6881 patients were classified into nine distinct classes (class size: 4–29%). Class 1, representing the mildest cases, was noted for independent ambulation and good upper limb function. Class 2 comprised those with the most severe clinical outcome. Other classes exhibited a gradient of severity, commonly encountered in clinical practice. For instance, Class 7 included right-sided paralysis with preserved motor activities of daily living (ADLs) and modified dependence in cognitive functions, such as communication. All predictors at admission were significantly associated with class membership at discharge (p < 0.001). Conclusions: LCA effectively identified unique clinical subgroups among acute stroke patients and demonstrated that key admission variables could predict class membership. This approach offers a promising insight into targeted, personalized rehabilitation practice for acute stroke patients. Full article

Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to define the scale effect, which can lead to scale sensitivity and discontinuity results. To address these limitations, this study introduces the Fuzzy Geographically Weighted Colocation Quotient (FGWCLQ) method. By integrating fuzzy theory, FGWCLQ replaces binary distance cutoffs with continuous membership functions, providing a more flexible and stable approach to spatial association mining. Using Point of Interest (POI) data from the Beijing urban area, FGWCLQ was applied to explore both intra- and inter-category spatial association patterns among star hotels, transportation facilities, and tourist attractions at different fuzzy neighborhoods. The results indicate that FGWCLQ can reliably discover global prevalent spatial associations among diverse facility types and visualize the spatial heterogeneity at various spatial scales. Compared to the deterministic GWCLQ method, FGWCLQ delivers more stable and robust results across varying spatial scales and generates more continuous association surfaces, which enable clear visualization of hierarchical clustering. Empirical findings provide valuable insights for optimizing the location of star hotels and supporting decision-making in urban planning. The method is available as an open-source Matlab package, providing a practical tool for diverse spatial association investigations. Full article