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Data containing noises, outliers, and imbalanced class distributions pose challenges to the traditional classifiers. By incorporating both the membership and non-membership functions, the intuitionistic fuzzy (IF) set has shown potential in designing robust learning algorithms for classifiers. However, the non-membership function used in these IF-based classifiers usually only utilizes the local distribution information of the training samples, and the classifiers are built upon single-hidden layer networks, which degrade the performance of the corresponding classifiers. Broad learning system (BLS) is an emerging neural network model with fast learning speed and flexible network architecture; however, it still fails to distinguish n samples. To this end, in this paper, we propose a new definition of the non-membership function within intuitionistic fuzzy sets and subsequently propose an intuitionistic fuzzy broad learning system (IFBLS) model. The proposed non-membership function incorporates two ratio numbers based on four distances, allowing for the utilization of global information on the distribution of samples and mitigating misclassification of valid samples as noise which is often observed in traditional methods. By using a score function that considers both the membership and non-membership functions to redistribute the importance of the training samples, the proposed IFBLS benefits from both the powerful representation capability of the original BLS and the robust learning of IF-based models. Extensive experiments conducted on 21 imbalanced binary classification problems sourced from the UCI and KEEL repositories illustrate that the proposed IFBLS achieves state-of-the-art performance by attaining the highest testing accuracy in 17 out of the 21 problems. [ABSTRACT FROM AUTHOR]

To enhance the power-to-hydrogen (P2H) efficiency and multiple energy utilization efficiency of the hydrogen-electric integrated energy system (HE-IES), an optimal operation framework considering the P2H factor is proposed. First, the proton exchange membrane electrolytic cells model which considers mass transfer characteristics is established. Efficient hydrogen production operating parameters are obtained through simulation to maximize the P2H efficiency. Second, the HE-IES is established, including the hydrogen flow system, wind power (WP), energy storage systems, gas boiler, combined heat and power and methanation unit. Based on the characteristics of the devices and efficient hydrogen operating parameters, the optimal operation framework considering the P2H efficiency factor is proposed. To realize the low economic operating costs and maximize the utilization of WP, a multi-objective function based on fuzzy membership function solving is applied. Finally, the case studies show that the proposed operation framework reduces the economic operation cost by 28.29% and improves the WP utilization rate by 31.9%. [ABSTRACT FROM AUTHOR]

This research aims to analyze the implementation of a fuzzy logic-based approach in improving the diagnosis of power transformer oil deterioration, which is critical for maintaining the efficient performance and operational life of transformers. Traditional diagnoses are based on strict measurements that do not account for the factors of variability and uncertainty of the actual data. In this article, we perform six different types of tests in this regard, and data have been collected during the period of 2021 to 2022 of 188 power transformer failures in the New KotLakhpat Lahore unit, whose voltage range is 132/66 kv and rating capacity is 40/50 MVA. In this case, a fuzzy logic-based scheme is developed based upon the membership function, a rule-based and defuzzification method that works with imprecision and the implementation of uncertainty in assessing the condition of transformer oils. Moisture, acidity, and a dissolved gas analysis indicator, along with other indication approaches such as interfacial tension, viscosity, and tangent delta measurement, are used to analyze the deterioration process in transformer oils. In the visual representation, oil samples with the following properties were first fuzzified: 19.9 mm2/s of viscosity, 0.453 mgKOH/g of acidity, 695 ppm of DGA, 20.8 mg/kg of moisture, 19.98 of IFT, and 4.35 × 100.14 of tangent delta. The output that was generated by software using the values entered into the parameters (HI and Age) after defuzzification is 45. Fuzzy logic serves as a concrete framework for transforming the diagnostics system and deterring the threats to the entire transformer's health and reliability in the future. By using this technique, various faults were hypothetically and practically analyzed in a transformer to implement early detection technologies with the possibility to reduce maintenance costs and extend operational life up to 45 years. Various case studies indicate the effectiveness of fuzzy logic in comparison to traditional diagnostics. [ABSTRACT FROM AUTHOR]

Low temperatures are a key factor affecting the growth, development, and geographical distribution of prickly ash. This study investigated the impact of ecological and geographical factors on the freezing tolerance of prickly ash germplasm. Thirty-seven germplasm samples from 18 different origins were collected, and their freezing tolerance was comprehensively evaluated. The correlation between freezing tolerance and the ecological and geographical factors of their origins was also analyzed. Significant differences in freezing tolerance were observed among germplasm from different origins. The semi-lethal temperature of the germplasm ranged from − 12.37 to 1.08 °C. As temperatures decreased, the relative conductivity (REC) and catalase (CAT) activity of the germplasm gradually increased, while soluble sugar (SS), soluble protein (SP), free proline (Pro), and Peroxidase (POD) activities decreased and then increased. Superoxide dismutase (SOD) activity initially increased and then decreased. A comprehensive evaluation of freezing tolerance was conducted using a logistic equation, membership function, and cluster analysis. Germplasm from Tongchuan and Hancheng (Shaanxi Province, China), Asakura (Japan), and Yuncheng (Shanxi Province, China) exhibited the highest freezing tolerance, whereas those from Rongchang (Chongqing Municipality, China), Qujing (Yunnan Province, China), and Honghe (Yunnan Province, China) had the lowest. The correlation analysis revealed a significant positive correlation between freezing tolerance and latitude, and a significant negative correlation with the temperature of origin. Germplasm from higher latitudes showed higher SS content, SOD and CAT activities, stronger antioxidant enzyme activity, and better freezing tolerance compared to those from lower latitudes. REC was lower in germplasm originating from low-temperature areas than in those from high-temperature areas. Additionally, SP, Pro content, SOD, and POD activities were higher, indicating effective scavenging of active oxygen free radicals. No significant correlation was found between altitude and longitude of origin and freezing tolerance. However, at similar latitudes, prickly ash from higher altitudes displayed higher antioxidant enzyme activity and stronger freezing tolerance compared to those from lower altitudes. These findings provide a scientific basis for breeding prickly ash cultivars suited to different ecological regions. [ABSTRACT FROM AUTHOR]

Humanity's efforts are manifested in the creation of novel solutions to complex problems in diverse fields. Traditional mathematical methods fail to solve real-world problems due to their complexity. Researchers have come up with new mathematical theories like fuzzy set theory and rough set theory to help them figure out how to model the uncertainty in these fields. Soft set theory is a novel approach to real-world problem solving that does not require the membership function to be specified. This aids in the resolution of a wide range of issues, and significant progress has recently been made. After Jun et al. came up with a hybrid system that combined fuzzy and soft set concepts, many people came up with hybrid ideas in different algebraic structures. In this paper, we introduce the concepts of subsystem and strong subsystem of a hybrid finite state machine (HFSM) and investigate a portion of their significant properties. We also provide an example that shows that every subsystem does not need to be a strong subsystem. Additionally, we study the cyclic subsystem of HFSMs and also obtain their equivalent results and examples. Finally, we define the notions of homomorphism of subsystems and strong subsystems of HFSMs. [ABSTRACT FROM AUTHOR]

The Type-2 fuzzy set is a fuzzy set with fuzzy membership degrees. This set is used when accurately determining the membership degree of a fuzzy set is challenging. It has been observed that higher-type fuzzy sets improve accuracy. However, to use fuzzy sets of higher types in deterministic space, the type needs to be reduced. Another critical challenge is ensuring hardware implementation capability and optimal performance in real-time applications while using fuzzy techniques. Memristor structures are emerging hardware platforms with biological similarities to the human nervous system, and its nanoscale implementation and low power consumption, making them suitable for hardware implementation. This paper introduces various approaches to implementing a fuzzy system with type-2 membership fuzzy sets, and for the first time, demonstrates the utilization of memristor structures to reduce the type. The suggested circuits allow the membership functions to have any shape and resolution, and the implementation results demonstrate the efficiency of the proposed hardware. The main goal of this paper was to showcase a hardware implementation that incorporates on-chip training, allowing adaptability to the environment without dependence on the host system (In-Situ Training). The ArC One hardware platform is used to demonstrate the results experimentally. In modelling and classification, the simulation and experimental results show an increase in accuracy more than 2% has been achieved, compared to previous works. [ABSTRACT FROM AUTHOR]

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Fuzzy logic is the core method for handling uncertainty and vagueness of information in agricultural natural language processing, and it also plays a crucial role in word segmentation and text classification algorithms using the neural network. Word segmentation is often the primary step in Chinese text classification tasks and has a profound effect on the generation ability of classification algorithm-based fuzzy logic. However, the high complexity of text classification models structure and specificity of agricultural data take a great challenge to studying the effect of word segmentation. Although there have been several attempts to resolve this issue, the main effort focuses on word segment Precision or the generalization performance of multiple word segment methods for the same classification algorithm and does not involve agricultural text. To solve this problem from the perspective of rational analysis and empirical analysis, a comprehensive analysis has been made to study the effect of Chinese word segmentation on fuzzy-based classification algorithms for agricultural questions. It initially discusses the characteristics of agricultural questions for the subsequent analysis of the field adaptability of word segmentation and classification algorithms, employs fuzzy logic to convert the Chinese word segmentation task into a sequence labeling problem, and then analyzes the characteristics, techniques, and performance disparities of the seven mainstream open-source Chinese word segmentation integration tools at the current stage. Subsequently, an exploration has been conducted into the impact of Chinese word segmentation on the generalization performance of classification algorithms under the proposed unified model framework for text classification based on fuzzy logic. Finally, many experiments have been performed on the actual data crawled from typical agricultural websites to empirically study the differences and robustness of the effect of different word segmentation tools on classification performance, as well as the contribution of the external dictionary. Comparative experimental results show which word segmentation tools have a solid effect on classification performance and a strong robust effect on the typical text feature extraction layer for classification tasks, and the external dictionary have no significant effect on classification performance. The research results have essential reference significance for how to select appropriate word segmentation tools to deal with Chinese natural language processing tasks in future. [ABSTRACT FROM AUTHOR]

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values. [ABSTRACT FROM AUTHOR]

Soybean sprouts are a common culinary vegetable due to their high nutrition and tasty flavors. To select soybean varieties with excellent sprout quality, 101 soybean materials were collected from different regions of China, and eight sprout quality parameters were determined for overall evaluation. The results showed that eight sprout quality parameters achieved varying degrees of difference and correlation. Based on the principal component analysis (PCA), three principal components were extracted, with a cumulative contribution rate of 78.314%. Further, the comprehensive evaluation value (D) of soybean sprout quality was calculated by membership function analysis based on PCA, and the quality of soybean sprouts was ranked accordingly. Subsequently, a regression equation for the prediction of soybean sprout quality was established using a stepwise regression analysis, and the model showed a good prediction performance (correlation coefficient of prediction > 0.8; residual predictive deviation > 2.0). On these grounds, it was proposed that the quality of soybean sprouts could be comprehensively predicted by four parameters: hypocotyl length, edible rate, 100-seed weight, and total isoflavone content and saponin content. In conclusion, this study provides excellent varieties for soybean sprout production and new variety breeding, and it provides an important reference for the prediction of soybean sprout quality. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aggregate gradation of asphalt mixture is one of the most important factors affecting the service life of asphalt pavement. In order to study the gradation of asphalt mixture with the best comprehensive performance, this study puts forward a new method of mineral aggregate gradation optimization based on the fuzzy analytic hierarchy process and comprehensive evaluation method, aiming at the multi-level and multi-index evaluation of the road performance of asphalt mixture. First, the orthogonal test design method is applied to design nine target gradations with coarse aggregate (>4.75 mm) and fine aggregate (<4.75 mm) of AC-20 (asphalt concrete) mixture serving as two factors and the upper, middle, and lower positions of the gradation curve as three levels, and then the road performance test research is carried out. Second, a comprehensive model for evaluation of road performance of mineral aggregate gradation is established. A fuzzy complementary judgment matrix is constructed, and the index weights of each level and the hierarchical total ranking weight are calculated. Then, the membership function is introduced into the comprehensive evaluation model for the road performance of mineral aggregate gradation, and the membership values of each index of asphalt mixture road performance are obtained. Finally, the fuzzy comprehensive evaluation method is used to find out the comprehensive evaluation value of the road performance of the nine graded asphalt mixtures, and the mineral aggregate gradation is optimized. The research results show that the 1# gradation of the asphalt mixture has the highest comprehensive road performance evaluation value, and the combination of the fuzzy hierarchical analysis process and comprehensive evaluation method can more objectively and comprehensively evaluate the comprehensive road performance of the asphalt mixture and provide a useful reference for the optimal selection of asphalt mixture mineral gradation. [ABSTRACT FROM AUTHOR]

Background: In Persian medicine, early diagnosis and treatment of stomach dystemperament is crucial for preventing other diseases. However, traditional medicine diagnosis often involves ambiguous and less structured information making it challenging for practitioners. Integrating fuzzy ontology with case-based reasoning (CBR) systems can enhance diagnostic accuracy in this filed. Objectives: This study aimed to develop and evaluate a fuzzy ontology-based CBR system for diagnosing and treating stomach dystemperament in Persian medicine. Methods: This was a mixed-methods research in which a fuzzy ontology-based CBR system was developed based on the fuzzy features, utilizing trapezoidal, triangular, right shoulder and left shoulder membership functions to represent linguistic variables such as hunger level and digestion power. The research phases included identifying relevant terms, concepts, and relationships, developing the fuzzy case-base ontology using the IKARUS-Onto methodology, and subsequently designing and implementing the CBR system. The system performance was evaluated in terms of its sensitivity, specificity, accuracy, precision, and F1-score. Results: Initially, a case-base fuzzy ontology was created. Then, the database was built up using 88 expert-validated medical records. Of these cases, 72% (63 cases) were diagnosed with phlegmatic dystemperament, 18% (16 cases) with cold-dry dystemperament, and 10% (9 cases) had no stomach dystemperament. The CBR system was developed and evaluated using sensitivity, specificity, accuracy, precision, and F1-score which were 97.5%, 87.5%, 96.6%, 98.7%, and 98.1%, respectively. Conclusions: Our fuzzy ontology-based CBR demonstrated high performance in diagnosing stomach dystemperament in Persian medicine. This system shows promise in improving diagnostic accuracy and facilitating the identification of similar cases. While initial results are encouraging, further evaluation in a real clinical environment is recommended to fully assess its practical utility. [ABSTRACT FROM AUTHOR]

Twin Support Vector Machine (TSVM) transforms a single large quadratic programming problem (QPP) in support vector machine (SVM) into two smaller QPPs by finding two non-parallel classification hyperplanes, so that its computational time is reduced to a quarter of what the traditional SVM takes. However, TSVM ignores the data distribution of class, which makes TSVM sensitive to noise. In this paper, a fuzzy twin support vector machine based on centered kernel alignment (FTSVM-CKA) is proposed to solve the problem that TSVM is sensitive to noise. Firstly, a feature-weighted kernel function is constructed by using the information gain, and it is applied to the calculation of the centered kernel alignment (CKA). This assigns greater weight to strongly correlated features, emphasizing their classification importance over weakly correlated features. Secondly, the CKA method is utilized to derive a heuristic function for calculating the dependency between samples and their corresponding labels, which assigns fuzzy membership to different samples. Based on this, a fuzzy membership assignment strategy is proposed that can effectively address the sensitivity of TSVM to noise. Thirdly, this strategy is combined with TSVM to propose the FTSVM-CKA model. Moreover, this study employs a coordinate descent strategy with shrinking by active set to tackle the computational complexity arising from high-dimensional inputs. This can effectively accelerate the training speed of the model while ensuring classification performance. In order to evaluate the performance of FTSVM-CKA, this study conducts experiments designed on artificial and UCI datasets. The results demonstrate that FTSVM-CKA can efficiently and quickly solve binary classification problems with noise. [ABSTRACT FROM AUTHOR]

Drought is a major limiting factor in the cultivation of Sophora alopecuroides in China's arid and semi-arid regions. This study aimed to identify drought-resistant S. alopecuroides ecotypes and explore key drought tolerance indicators during germination by simulating drought conditions with a 10% PEG-6000 solution, using pure water as a control. Determination of seven germination indicators for S. alopecuroides, including germination rate (GR), germination energy (GE), germination index (GI), vigor index (VI), promptness index (PI), fresh weight (FW), and dry weight (DW), was conducted. Principal component analysis (PCA), membership function, cluster analysis, and linear regression were employed to comprehensively evaluate the drought resistance of thirty-five S. alopecuroides ecotypes. The results showed that drought stress caused reductions in six of the seven indicators across all ecotypes, except for DW, compared to the control. Correlation analysis revealed varying relationships among the indicators, with most showing significant or highly significant correlations. PCA reduced the seven indicators to two independent comprehensive factors, with a cumulative contribution rate of 83.99%. Based on the D-value and cluster analysis, the thirty-five ecotypes were ranked for drought resistance and classified into four categories. The top five drought-tolerant genotypes during the germination stage were identified as Yutian, Alar, Jinghe, Baoding, and Guyuan. Moreover, the stepwise regression model was established and demonstrated that GR, GE, PI, FW, and DW are key indicators for screening and identifying drought-resistant S. alopecuroides ecotypes. This study offers a comprehensive and reliable method for evaluating drought resistance in S. alopecuroides ecotypes and provides a reference for selecting ecotypes for artificial cultivation in Northwestern China. [ABSTRACT FROM AUTHOR]

Dementia is the most critical neurodegenerative disease that gradually destroys memory and other cognitive functions. Therefore, early detection is essential, and to build an effective detection model, it is required to understand its type, symptoms, stages and causes, and diagnosis methodologies. This paper presents a novel approach to classify dementia based on a data set with some relevant patient features. The classification methodology employs non-singleton general type-2 fuzzy sets, non-singleton interval type-2 fuzzy sets, and non-singleton type 1 fuzzy sets. These advanced fuzzy sets are compared with traditional singleton fuzzy sets to evaluate their performance. The Takagi–Sugeno–Kang TSK inference method is used to handle fuzzy reasoning. In the process, the parameters of the membership functions (MFs) and rules are obtained using ANFIS, and non-singleton MFs are optimized with PSO. The results demonstrate that non-singleton general type-2 fuzzy sets improve classification accuracy compared to singleton fuzzy sets, demonstrating their ability to model the uncertainties inherent in the diagnosis of dementia. This improvement suggests that non-singleton fuzzy systems offer a more robust framework for developing effective diagnostic tools in the medical domain. Accurate classification of dementia is of utmost importance to improve patient care and advance medical research. [ABSTRACT FROM AUTHOR]

This paper introduces a sum-of-squares (S-O-S) approach to Stability Analysis and Stabilization (SAS) of nonlinear dynamical systems described by General Conformable Polynomial Fuzzy (GCPF) models with a time delay. First, we present GCPF models, which are more general compared to the widely recognized Takagi–Sugeno Fuzzy (T-SF) models. Then, we establish SAS conditions for these models using a Lyapunov–Krasovskii functional and the S-O-S approach, making the SAS conditions in this work less conservative than the Linear Matrix Inequalities (LMI)-based approach to the T-SF models. In addition, the SAS conditions are found by satisfying S-O-S conditions dependent on membership functions that are reliant on the polynomial fitting approximation algorithm. These S-O-S conditions can be solved numerically using the recently developed SOSTOOLS. To demonstrate the effectiveness and practicality of our approach, two numerical examples are provided to demonstrate the effectiveness and practicality of our approach. [ABSTRACT FROM AUTHOR]

This article considers exponential stabilization (ES) of switched interval type‐2 (SIT2) fuzzy systems with asynchronous switching control. The SIT2 fuzzy system switches from one mode to another according to transition probabilities (TPs). A general SIT2 fuzzy quantized event‐triggered output controller (QETOC) is designed to save the communication resources. The QETOC employs distinctive membership functions from those of the IT2 fuzzy model and switches with mode‐pendent switching delay, which is more practical than asynchronously switched control techniques with a common switching delay and control schemes without TP. By designing discretized multiple Lyapunov function (DMLF) and using convex combination technique, sufficient conditions formulated by linear matrix inequalities (LMIs) are obtained to ensure that the DMLF does not 'jump high' at the moment of switching, and hence the conservatism of obtained results is significantly reduced since no additional dwell time is needed for the stabilization. It is discovered that the SIT2 fuzzy system is not necessary to be unstable on mismatched intervals, and it can be unstable on matched intervals. The advantages of theoretical analysis are verified by numerical simulations, and the classical assumption that each subsystem is unstable on mismatched intervals is conservative for control design. [ABSTRACT FROM AUTHOR]

This paper proposes a new design method of energy management strategy (EMS) with adaptive super-twisting sliding mode control (ASTSMC) for fuel cell/battery/supercapacitor hybrid vehicle (FCHEV). The main objective of the proposed EMS is to improve power performance, fuel cell lifetime, and fuel consumption while considering the regulation of the DC-bus voltage. The proposed EMS is designed based on a frequency-decoupling technique using an adaptive low-pass filter, Harr wavelet transform (HWT), and FLC to decouple the required power into low, medium, and high-frequency components for fuel cell, battery, and supercapacitor, respectively. The presented frequency-decoupling-based strategy can improve the power performance of the vehicle as well as reduce load stress and power fluctuation in the fuel cell. Nevertheless, the neural network optimization algorithm (NNOA) is employed to optimize the membership functions of FLCs while considering the hydrogen consumption and constraints on the state of charge (SOC) of the battery and supercapacitor. To achieve robustness and high precision control, the ASTSMC is developed based on a nonlinear disturbance observer (NDOB) to stabilize the DC-bus voltage and currents of the energy sources, ensuring that the fuel cell, battery, and supercapacitor track their obtained reference values. The FCHEV system with the proposed EMS is modeled on MATLAB/Simulink, and three typical driving cycles such as HWFET, UDDS, and WLTP driving schedules are used for evaluation. The findings exhibit that the proposed EMS can effectively improve the fuel economy, reduce power fluctuation in the fuel cell, and prolong its lifetime compared to other existing methods such as the equivalent consumption minimization strategy (ECMS), state machine (SM), and FLC-based EMSs. [ABSTRACT FROM AUTHOR]

To analyze the heat tolerance of japonica rice in Liaohe plain rice-growing region, and establish an effective evaluation system of heat tolerance, 20 main varieties of japonica rice suitable for cultivating over this region were selected and planted under simulated temperature increase using the free air temperature increase system. The heat tolerance of different japonica rice varieties was comprehensively evaluated by multivariate statistical analysis methods based on the heat tolerance coefficients of 10 rice trait indicators, the effective panicle number (X1), grain number per spike (X2), seed setting rate (X3), 1000-grain weight (X4), yield (X5), brown rice rate (X6), milled rice rate (X7), protein content (X8), amylose content (X9), and taste value (X10) after harvesting and air-drying the rice. The results showed that the trait indicators except taste value were significantly or extremely reduced under the warming. Correlation analysis based on heat tolerance coefficients revealed different degrees of association between rice trait indicators. Principal component analysis condensed the 10 individual indicators into 4 mutually independent comprehensive indicators. The 4 comprehensive indicators had contribution rates of 34.816%, 19.265%, 15.636%, and 10.605%, respectively, giving a cumulative contribution rate of 80.322%. The comprehensive evaluation values of heat tolerance (Di value) were calculated using the membership function method. The higher the Di value, the stronger the heat tolerance. The heat tolerance of 'Yanjing 456' was the strongest and that of 'Shennong 016' was the worst. The 20 japonica rice varieties were classified into three categories based on their heat tolerance: the first category comprised 7 heat-tolerant varieties, the second category included 6 moderately heat-tolerant varieties, and the third category consisted of 7 heat-sensitive varieties. [ABSTRACT FROM AUTHOR]

Semantic segmentation is crucial for a wide range of downstream applications in remote sensing, aiming to classify pixels in remote sensing images (RSIs) at the semantic level. The dramatic variations in grayscale and the stacking of categories within RSIs lead to unstable inter-class variance and exacerbate the uncertainty around category boundaries. However, existing methods typically emphasize spatial information while overlooking frequency insights, making it difficult to achieve desirable results. To address these challenges, we propose a novel dual-domain fusion network that integrates both spatial and frequency features. For grayscale variations, a multi-level wavelet frequency decomposition module (MWFD) is introduced to extract and integrate multi-level frequency features to enhance the distinctiveness between spatially similar categories. To mitigate the uncertainty of boundaries, a type-2 fuzzy spatial constraint module (T2FSC) is proposed to achieve flexible higher-order fuzzy modeling to adaptively constrain the boundary features in the spatial by constructing upper and lower membership functions. Furthermore, a dual-domain feature fusion (DFF) module bridges the semantic gap between the frequency and spatial features, effectively realizes semantic alignment and feature fusion between the dual domains, which further improves the accuracy of segmentation results. We conduct comprehensive experiments and extensive ablation studies on three well-known datasets: Vaihingen, Potsdam, and GID. In these three datasets, our method achieved 74.56%, 73.60%, and 81.01% mIoU, respectively. Quantitative and qualitative results demonstrate that the proposed method significantly outperforms state-of-the-art methods, achieving an excellent balance between segmentation accuracy and computational overhead. [ABSTRACT FROM AUTHOR]

Summary: Deploying a wireless sensor network (WSN) for accessing the network of distant environments is more crucial. Node defect detection is a core technology of WSN and is essential for most WSN networks. The defective node reduces the overall service quality (OSQ) of the global WSN network system. Therefore, a type II fuzzy inference system (T2_FIS) is proposed for detecting defective nodes in the WSN network. The adaptive genetic algorithm (AGA) and proposed method are introduced for tuning the parameters in the T2_FIS system. The proposed T2_FIS method effectively detects the broken node in the WSN network using the fuzzy rules. In addition, the improved Mud Ring (IMR) optimization algorithm is proposed to replace the faulty node with the neighborhood node in the network. The defective nodes are identified and replaced with the closest nodes based on the membership function (MF) generated by the T2_FIS. Furthermore, the lifetime and throughput of the WSN network are increased by minimizing energy consumption. The overall performance is evaluated using the MATLAB tool, and the implementation results are compared with the existing methods. The total energy consumption for the proposed method is 50.451, with a throughput and lifetime of 153.657 and 22979.25. Furthermore, the performance metrics for the existing and proposed strategies are analyzed, and the accuracy of the proposed method is proven to be 99.3827%, the false alarm rate (FAR) is 0.0008, and the false positive rate (FPR) is 0.0617. The results show the effectiveness and performance of the proposed method. [ABSTRACT FROM AUTHOR]

This article addresses the state estimator design issue of interval type-2 (IT2) fuzzy systems under deception attacks. The permission of sensors to access the network is determined by a stochastic protocol obeying Markov scheduling rules. An attack-resilient IT2 fuzzy state estimator is proposed to mitigate the adverse effect of deception attacks on estimation performance. By using the known bounds of system membership functions and introducing two new weighting functions, the MFs of fuzzy state estimator are reconstructed. A hidden Markov model is employed to describe the asynchronous behaviour of scheduling rules between the sensor and the estimator. Sufficient conditions are derived to ensure the ultimate boundedness of estimation error. Finally, a numerical example demonstrates the applicability of the proposed attack-resilient asynchronous state estimation method. [ABSTRACT FROM AUTHOR]

Information theory provides suitable tools for solving practical problems, particularly multi-criteria decision-making (MCDM) problems under neutrosophic environments. Generally, a wide range of MCDM solution methods are constructed based on distance measures category. The defined distance measures for continuous neutrosophic numbers, especially its trapezoidal type, is very limited rather than discrete type. The main goals of this study is to come up with a way to add two new types of weighted distance measures based on meaningful surfaces: Euclidean and Hamming. To define these measures, all three components of the neutrosophic trapezoidal fuzzy number (truth, indeterminacy, and falsity membership functions) have been used simultaneously. The proof of some theorems and properties for the weighted distance measures demonstrates their validity. The CODAS algorithm is known as one of the distance-based methods for solving MCDM problems. The following represents the CODAS algorithm based on two novel distance measures. In addition, an explanatory example from the research literature is given to check the performance of the proposed hybrid algorithm. The results of this study indicate that the algorithm based on the proposed measures obtains a reasonable and appropriate ranking order between the options. Furthermore, the sensitivity parameter analysis and comparative analysis show the flexibility and accuracy of the suggested measures in the combined algorithm. The acceptable efficiency of proposed distance measures formed on the surfaces can shed light on research related to distance measures in the methodology and implicated aspects. [ABSTRACT FROM AUTHOR]

This paper is part of the increasing interest regarding the application of interval type-3 fuzzy logic in real-world problems, where a better handling of uncertainty can be useful in achieving enhanced results. The main contribution of this paper is the proposal of new methods, such as Interval Type-3 Reduction and a practical way for modeling Interval Type-3 Membership Functions, based on the Footprint of Uncertainty (FOU) and Core of Uncertainty (COU) concepts, which reduce the gap between the theory and the practical implementation of Mamdani Interval Type-3 Fuzzy Systems. The main aim of the paper is not proving the superiority of Interval Type-3 Fuzzy Systems but providing a framework and a comprehensive illustration of the theory concepts to help future research work in developing optimization methodologies and new applications for this kind of systems, as well as finding their potential applicability, which can result from their ability in handling more complex uncertainty. Simulation results with two illustrative application examples show the potential of the presented approach in achieving an efficient implementation of Interval type-3 fuzzy systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the most widely used fuzzy linear programming models is the multi‐objective interval type‐2 fuzzy linear programming (IT2FLP) model, which is of particular importance due to the simultaneous integration of multiple criteria and objectives in a single problem, the fuzzy nature of this type of problems, and thus, its closer similarity to real‐world problems. So far, many studies have been done for the IT2FLP problem with uncertainties of the vagueness type. However, not enough studies have been done regarding the multi‐objective interval type‐2 fuzzy linear programming (MOIT2FLP) problem with uncertainties of the vagueness type. As an innovation, this study investigates the MOIT2FLP problem with vagueness‐type uncertainties, which are represented by membership functions (MFs) in the problem. Depending on the localisation of vagueness in the problem, that is, vagueness in the objective function vector, vagueness in the technological coefficients, vagueness in the resources vector, and any possible combination of them, various problems may arise. Furthermore, to solve problems with MOIT2FLP, first, using the weighted sum method as an efficient and effective method, each of the MOIT2FLP problems is converted into a single‐objective problem. In this research, these types of problems are introduced, their MFs are stated, and different solution methods are suggested. For each of the proposed methods, the authors have provided an example and presented the results in the corresponding tables. [ABSTRACT FROM AUTHOR]

[Objective] The aim of this study was to explore the feasibility of biochar replacing peat in the cultivation of Calendula officinalis. [Method] The pot experiment was conducted for 4 treatments of replacing 5% (BS5), 10% (BS10), 15% (BS15), and 20% (BS20) peat with biochar using a compound substrate of peat, perlite, and vermiculite as the control (CK). The changes in growth and ornamental characteristics of Calendula officinalis and their correlation with substrate properties were analyzed. [Result] The growth of Calendula officinalis was promoted and its ornamental characters were improved after partial replacement of peat with biochar. The shoot dry weight and root dry weight of BS5-BS20 treatments were 1.90-3.31 and 1.77-2.38 times higher than those of CK, respectively. The carotenoids contents of BS10-BS20 treatments were 1.76-6.90 times higher than those of CK. The membership function analysis showed that growth and ornamental characters of Calendula officinalis were the best under BS15 treatment. Adding biochar increased total porosity of the compound substrate by 4.14%-15.26% and increased contents of available phosphorus and potassium by 39.33%-148.68% and 335.07%-850.25%, respectively. The activities of sucrase, urease and phosphatase were increased by 12.86%-35.24%, 14.85%-65.24% and 18.95%-33.33%, respectively. Redundancy analysis (RDA) showed that main physical and chemical factors affecting growth and ornamental characters of Calendula officinalis were available phosphorus, bulk density, and aeration pores. The main biological factors were phosphatase activity, urease activity, microbial biomass nitrogen, and fungi. [Conclusion] It was feasible to replace 5%-20% peat with biochar to cultivate Calendula officinalis. Biochar promoted the growth of Calendula officinalis and enhanced its ornamental value by improving physical, chemical and biological properties of the compound substrate. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Globally, microplastic pollution of soil ecosystems poses a major risk. The early studies found that the impact of microplastics on different plants could vary depending on the type of microplastic, the mass concentration or the plant species. This study investigated the effect of 3 mass concentrations (0.1%, 1%, and 2.5%) and 3 types of microplastics (PE MPs, PLA MPs, and PVC MPs) on adzuki bean biomass, root traits, Chlorophyll content and antioxidant enzymes. According to our findings, all microplastics had an impact on biomass, but PLA MPs had the strongest inhibitory effect. The high mass concentration of microplastics had a significant influence on chlorophyll content. Adzuki beans exhibited varying degrees of damage upon exposure to microplastics, but they were able to withstand the oxidative stress brought on by PE MPs by increasing the activity of antioxidant enzymes (SOD and POD). Comparing the adverse effects of PE MPs on adzuki beans to those of PLA MPs and PVC MPs, principal component analysis and membership function value analysis revealed that the former had fewer impacts. Disparities in the observed effects may be attributed to variations in the properties of microplastics. Subsequent investigations into the mechanisms underlying microplastic toxicity need a more comprehensive exploration. [ABSTRACT FROM AUTHOR]

A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor's r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset. [ABSTRACT FROM AUTHOR]

Signature verification often entails ambiguous scenarios where a signature may closely resemble multiple reference samples or differ due to inherent handwriting variations. In such cases, a neutrosophic engine can enhance the verification process by addressing the uncertainties and ambiguities present in signatures. This is achieved by representing the features and similarity scores produced by the matching algorithm as neutrosophic sets, thereby capturing the degrees of membership, non-membership, and indeterminacy. However, type-1 neutrosophic logic, which assigns fixed values to these membership functions, proves inadequate in capturing the varying degrees of uncertainty in signature characteristics. This limitation stems from its inability to adjust to different levels of uncertainty. To address these shortcomings, the proposed forensic signature examination approach explores the utilization of type-2 neutrosophic logic. This approach offers greater flexibility and granularity in handling ambiguity, indeterminacy, and uncertainty, thereby enhancing the accuracy of signature verification systems. Type-2 neutrosophic logic facilitates more precise decision-making by allowing for the evaluation of multiple sources of ambiguity and conflicting information. Experimental results demonstrate the advantages of employing a type-2 neutrosophic engine for signature verification, particularly in its superior capability to manage uncertainty and variability when compared to type-1 neutrosophic logic. This advancement leads to more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) outcomes. Comparative analysis using a benchmark dataset of handwritten signatures reveals that the type-2 neutrosophic similarity measure achieves an accuracy rate of 98%, outperforming the 95% accuracy rate associated with type-1 neutrosophic logic. [ABSTRACT FROM AUTHOR]

Traffic management poses pervasive challenges in urban areas, resulting in substantial time and monetary losses, alongside critical safety risks. This paper presents a novel approach that merges graph theory with fuzzy logic to optimize the placement of traffic signals, signs and personalized traffic route selection. Emphasizing the enhancement of road infrastructure as a pivotal solution, the study integrates fuzzy graph theory to normalize parameters with varying limits. The innovative framework explores the connectivity of Intuitionistic Anti-Fuzzy–Fuzzy Combination Graphs (IAFFCG), unveiling an optimization strategy tailored to specified parameter preferences. This holistic approach offers a comprehensive solution to the complex challenges of traffic management and road infrastructure enhancement. Furthermore, the study introduces a method for normalizing traffic congestion using IAFFCG theory which enables the examination of the strength and frailty of network connectivity simultaneously, utilizing anti-fuzzy–fuzzy membership and non-membership functions that capture the uncertainty of traffic conditions across categories ranging from easy to difficult. Finally, the incorporation of fuzzy automata into this IAFFCG presents a groundbreaking approach in the field of decision-making, marking a significant advancement in this domain. [ABSTRACT FROM AUTHOR]

In agriculture, crop planning and land distribution have been important research subjects. The distribution of land involves several multi-functional tasks, such as maximizing output and profit and minimizing costs. These functions are influenced by a variety of uncertain elements, including yield, crop price, and indeterminate factors like seed growth and suitable fertilizer. In order to address this problem, other researchers have used fuzzy and intuitionistic fuzzy optimization approaches, which did not include the indeterminacy membership functions. However, the neutrosophic optimization technique addresses the problem by using individual truth, falsity, and indeterminacy membership functions. So, to improve the optimal solution, the Neutrosophic Goal Programming (NGP) problem with hexagonal intuitionistic parameters is employed in this study. The membership functions for truth, indeterminacy, and falsity are constructed using hyperbolic, exponential, and linear membership functions. Minimizing the under deviations of truth, over deviations of indeterminacy, and falsity yields the NGP achievement function, which is used to attain optimal expenditure, production, and profit under the constraints of labour, land, food requirements, and water. Bio-inspired computing has been a major research topic in recent years. Optimization is mostly accomplished through the use of bio-inspired algorithms, which draw inspiration from natural behaviour. Bio-inspired algorithms are highly efficient in exploring large solution spaces, and helps to manage trade-offs between various goals, and providing the global optimal solution. Consequently, bio-inspired algorithms such as Grey Wolf Optimization (GWO), Social Group Optimization (SGO), and Particle Swarm Optimization (PSO) are employed in the current work to determine the global optimal solutions for the NGP achievement function. The data for the study was collected from the medium-sized farmers in Ariyalur District, Tamil Nadu, India. To illustrate the uniqueness and application of the developed method, the optimal solutions of the suggested method are compared with Zimmermann, Angelov, and Torabi techniques. The proposed technique demonstrates that the bioinspired algorithms' optimal solution to the neutrosophic goal is superior to the existing approaches. [ABSTRACT FROM AUTHOR]

In agriculture, one of the most crucial elements for sustained plant production is light. Artificial lighting can meet the specific light requirements of various plants. However, it is a challenge to find optimal lighting schemes that can facilitate a balance of plant growth and nutritional qualities. In this study, we experimented with the light intensity required for plant growth and nutrient elements. We designed three light intensity treatments, 180 μmol m−2 s−1 (L1), 210 μmol m−2 s−1 (L2), and 240 μmol m−2 s−1 (L3), to investigate the effect of light intensity on lettuce growth and quality. It can be clearly seen from the radar charts that L2 significantly affected the plant height, fresh weight, dry weight, and leaf area. L3 mainly affected the canopy diameter and root shoot ratio. The effect of L1 on lettuce phenotype was not significant compared with that of the others. The total soluble sugar, vitamin C, nitrate, and free amino acid in lettuce showed more significant increases under the L2 treatment than under the other treatments. In addition, the transpiration rate and stomatal conductance were opposite to each other. The comprehensive evaluation of the membership function value method and heatmap analysis showed that lettuce had the highest membership function value in L2 light intensity conditions, indicating that the lettuce grown under this light intensity could obtain higher yield and better quality. This study provides a new insight into finding the best environmental factors to balance plant nutrition and growth. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global water scarcity and extreme weather intensify drought stress, significantly reducing cotton yield and quality worldwide. Drought treatments are conducted using a population of chromosome segment substitution lines generated from E22 (G. hirsutum) and 3–79 (G. barbadense) as parental lines either show superior yields or fiber quality under both control and drought conditions. Fourteen datasets, covering 4 yields and 4 quality traits, are compiled and assessed for drought resistance using the drought resistance coefficient (DRC) and membership function value of drought resistance (MFVD). Genome‐wide association studies, linkage analysis, and bulked segregant analysis are combined to analyze the DR‐related QTL. A total of 121 significant QTL are identified by DRC and MFVD of the 8 traits. CRISPR/Cas9 and virus‐induced gene silencing techniques verified DRR1 and DRT1 as pivotal genes in regulating drought resistant of cotton, with hap3‐79 exhibiting greater drought resistance than hapE22 concerning DRR1 and DRT1. Moreover, 14 markers with superior yield and fiber quality are selected for drought treatment. This study offers valuable insights into yield and fiber quality variations between G. hirsutum and G. barbadense amid drought, providing crucial theoretical and technological backing for developing cotton varieties resilient to drought, with high yield and superior fiber quality. [ABSTRACT FROM AUTHOR]

This article deals with the security sliding mode control (SMC) issue for T‐S fuzzy systems under random injection attacks. As a way to increase the communication efficiency of network control systems, the weighted try‐once‐discard (WTOD) protocol is utilized to decide whether the sensor nodes transmit or not. A compensating strategy is introduced in this controller side, based on which a fuzzy sliding mode controller depending on the scheduling signal is constructed. Then, the reachability of the given sliding surface together with the stochastic stability of the closed‐loop system are analyzed by incorporating the membership function, and the relaxed stability conditions are derived. Moreover, a GA‐based solving algorithm is proposed to search an optimized sliding matrix and improve the SMC performance. Finally, numerical simulation results are provided to verify the proposed SMC strategy. [ABSTRACT FROM AUTHOR]

This article presents a new design approach for resilient fault‐tolerant event‐triggered (ET) based security control of Markovian jump nonlinear systems (MJNSs) in the presence of nonperiodic denial‐of‐service (DoS) attacks. The approach is developed based on interval type‐2 (IT‐2) fuzzy models of the plant. The state error produced by data packet loss is turned into a parameter in the proposed ET condition during nonperiodic DoS attacks. The uncertain triggering variable is considered in the resilient fault‐tolerant ET condition for the MJNSs. In contrast to the usual strategy, the fault‐tolerant ET strategy is designed to send the required data packets to the controller during nonperiodic DoS attacks, which significantly contributes to the minimization of performance loss. Dissipative, passive, and H∞$$ {\boldsymbol{H}}_{\infty } $$‐control schemes are all covered in a unifying framework by using a generalized performance index. Plant and controller modes are considered under mismatched membership functions to regulate asynchronous phenomena. Sufficient conditions depending on the membership functions that guarantee the stability of the closed‐loop system are derived. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The model of bipolar complex fuzzy linguistic set is a very famous and dominant principle to cope with vague and uncertain information. The bipolar complex fuzzy linguistic set contained the positive membership function, negative membership function, and linguistic variable, where the technique of fuzzy sets to bipolar fuzzy sets are the special cases of the bipolar complex fuzzy linguistic set. In this manuscript, we describe the model of Aczel-Alsina operational laws for bipolar complex fuzzy linguistic values based on Aczel-Alsina t-norm and Aczel-Alsina t-conorm. Additionally, we compute the Aczel-Alsina power aggregation operators based on bipolar complex fuzzy linguistic data, called bipolar complex fuzzy linguistic Aczel-Alsina power averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power weighted averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power geometric operator, and bipolar complex fuzzy linguistic Aczel-Alsina power weighted geometric operator with some dominant and fundamental laws such as idempotency, monotonicity, and boundedness. Moreover, we initiate the model of the Weighted Aggregates Sum Product Assessment technique with the help of consequent theory. In the context of geographic information systems and spatial information systems, coupling aims to find out the relationships among different components within a geographic information system, where coupling can occur at many stages, for instance, spatial coupling, data coupling, and functional coupling. To evaluate the above dilemma, we perform the model of multi-attribute decision-making for invented operators to compute the best technique for addressing geographic information systems. In the last, we deliberate some numerical examples for comparing the ranking results of proposed and prevailing techniques. [ABSTRACT FROM AUTHOR]

Summary Sensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive optimization approach for a fuzzy logic-based energy management system (EMS) designed for a fuel cell hybrid electric vehicle (FCHEV) powered by a fuel cell and a battery. The aim is to enhance EMS performance and overall system efficiency by optimizing fuzzy logic parameters using a genetic algorithm (GA). The effectiveness of the proposed strategy is demonstrated through simulations using ADVISOR software and the MATLAB/SIMULINK environment. The optimization process consists of four phases: tuning the membership function types, fuzzy variable ranges, membership function parameters, and rule weights. The proposed method has resulted in a significant improvement in the fitness function, highlighting the high sensitivity of the fuzzy logic system to optimization parameters. The optimized EMS is compared with the original one and a thermostat-based strategy built into ADVISOR. Results show a 28.5% improvement in fuel utilization and a 13% reduction in the low-efficiency operation of the fuel cell system. [Display omitted] • A comprehensive optimization approach for energy management system in FCHEVs. • Simulation environment development with real-time FCHEVs interactive monitoring. • FCHEVs Performances improvement in terms of energy efficiency and management. • FCHEVs Energy losses minimization with robust and efficient decision-making. • Innovative solutions provided to overcome the operating constraints of FCHEVs. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a mechanical procedure to generate fuzzy data from the observed real number data. The observed data may be imprecise owing to some unexpected situation. In this case, the decision-makers may treat the observed data to be fuzzy data by providing suitable membership functions. However, the determination of those membership functions is frequently subjective for the different decision-makers. To avoid this biased subjectivity raised by the decision-makers, this paper proposes a mechanical procedure to generate fuzzy data. The essence of this mechanical procedure is to propose an extended form to generate a fuzzy set from a pre-determined family. This extended form is based on two functions, such that the conventional form can be covered when these these two functions are taken to be the identity functions. One of these two functions is adopted to rearrange the pre-determined family to be a nested family. A more general form is also proposed by considering a countable subfamily. [ABSTRACT FROM AUTHOR]

This paper presents an innovative multi-objective optimization methodology aiming to address the complex problem of optimal network reconfiguration simultaneously with the allocation of capacitor banks (CBs) and distributed generations (DGs) in a radial distribution system (ONRSACD). Integrating CBs, DGs, and network reconfiguration (NR) in distribution systems enhances power quality and reduces energy costs at distribution substations. Nevertheless, their incorporation poses challenges due to the significant associated investment costs. The proposed approach leverages the hybrid multi-population algorithm (HMPA) to maximize techno-economic benefits and satisfy critical operational constraints to achieve a robust solution. The primary objective is to achieve a good compromise between mutually conflicting costs: substation energy costs and device investment costs. In pursuit of a realistic network behavior model, the method considers the hourly variations of diverse load types and DGs output. To address the inherent multi-objective nature of the problem, the proposed approach seamlessly integrates the fuzzy logic tool. Each objective function is converted into the fuzzy domain using its respective membership function, and upper and lower bounds are determined through a carefully devised strategy. Validation and performance assessment of the proposed methodology are conducted on the 33-bus and 69-bus radial distribution test systems. Additionally, the efficacy of the hybrid multi-population algorithm (HMPA) is benchmarked against several well-established algorithms, including artificial ecosystem-based optimization (AEO), Harris Hawks optimization (HHO), particle swarm optimization (PSO), and moth flame optimization (MFO). [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is stated as a disease in the eyes that affects the retina blood vessels and causes blindness. The early diagnosis and detection of the DR in patients preserve the patient's vision. In general, for the diagnosis of eye diseases, retinal fundus images are employed. The advancement in the automatic diagnosis of diseases attained higher significance for rapid advancement in computing technology in the medical field. Besides, for the diagnosis of the diseases, fundus image automatic detection is involved in the recognition of blood vessels evaluated based on the length, branching pattern, and width. However, fundus images have low contrast and it is difficult to evaluate the identification of the disease in blood vessels. As a result, it is necessary to adopt a consistent automated method to extract blood vessels in the fundus images for DR. The conventional automated localization of the macula and optic disk in the retinal fundus images needs to be improved for DR disease diagnosis. But existing methods are not sufficient for the early identification and detection of DR. This paper proposed an entropy distributed matching global and local clustering (EDMGL) for fundus images. The developed EDMGL comprises the different uncertainties for the evaluation of the classes based on local and global entropy. The fundus image local entropy is evaluated based on the spatial likelihood fuzzifier membership function estimation for segmentation. The final proposed algorithm membership function is estimated using the addition of weighted parameters through membership estimation based on the global and local entropy. The classification performance of the proposed EDMGL is evaluated based on the dice coefficient, segmentation accuracy, and partition entropy. The performance of the proposed EDMGL is comparatively examined with the conventional technique. The comparative analysis expressed that the performance of the proposed EDMGL exhibits ∼5% improved performance in terms of accuracy, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

Bermudagrass (Cynodon dactylon), a salt-tolerant species surviving in environments with pH up to 9.3, and it exhibits variable germination responses under salt and alkaline stress. This study evaluates the impact of neutral and alkali salts with varying pH levels on bermudagrass seed germination. Six bermudagrass germplasm accessions were analyzed using neutral (NaCl: Na2SO4 = 1:1, pH 6.12–7.14) and alkali (NaHCO3:Na2CO3 = 1:1, pH 9.62–9.90) salt treatments. Salt concentrations ranged from 0 to 250 mmol/L, with increments of 25 mmol/L. The assessed parameters included seed germination rate, germination potential, germination index, radicle length, plumule length, seedling weight, and radicle and plumule length ratio. The salt tolerance threshold of each germplasm was calculated using a linear regression fitting model. Critical indicators of salt tolerance were selected through stepwise regression, and the salt-alkali tolerance ranking was determined using a combined membership function and discriminant analysis. The results indicated that the total score decreased with increasing salt concentration under neutral salt stress. Alkali salt stress was more damaging to bermudagrass seedlings than neutral salt stress, inhibiting germination at 50 mmol/L. Neutral salt tolerance thresholds ranged from 31.7 to 207.7 mmol/L, while alkaline salt tolerance thresholds ranged from 16.9 to 53.3 mmol/L. The six germplasm accessions exhibited different responses to salt and alkali stress. Key indicators for neutral salt tolerance included plumule length, radicle and plumule length ratio, and seedling weight. For alkali salt tolerance, key indicators were germination potential, radicle length, and seedling weight, which can be used to screen for resistant germplasms. Our study demonstrates that alkaline salts inhibit seed germination and seedling growth more than neutral salts, and pH affects root growth and the radicle-to-plumule length ratio in seedlings. This research has significant ecological implications, providing insights into the adaptation strategies of bermudagrass in salt-affected and alkaline environments, which could aid in the restoration and management of degraded ecosystems. [ABSTRACT FROM AUTHOR]

Short-duration heavy rainfall (SHR), as delineated by the National Meteorological Center of the China Meteorological Administration, is characterized by hourly rainfall amounts no less than 20.0 mm. SHR is one of the most common convective weather phenomena that can cause severe damage. Short-range forecasting of SHR is an important part of operational severe weather prediction. In the present study, an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach. The 1.0°×1.0° National Centers for Environmental Prediction (NCEP) final analysis data and the ordinary rainfall (0.1-19.9 mm h-1) and SHR observational data from 411 stations were used in the improved scheme. The best lifted index, the total precipitable water, the 925 hPa specific humidity (Q925), and the 925 hPa divergence (DIV925) were selected as predictors based on objective analysis. Continuously distributed membership functions of predictors were obtained based on relative frequency analysis. The weights of predictors were also objectively determined. Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme. Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0° data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives. This study provides an objectively feasible choice for short-range guidance forecasts of SHR. The scheme can be applied to other convective phenomena. [ABSTRACT FROM AUTHOR]

Previous investigation has not considered potential sex differences for psychopathy facets and executive functioning related to gang membership. Although empirical inquiry has begun to focus on psychopathy's role in youth gang membership, this research is male-focused. The present study uses the baseline of the Pathways to Desistance study to investigate the relationship between psychopathy facets, executive functioning and gang membership across sex. Logistic regression in all boys and girls samples is performed to examine the potential relationship between the constructs across sex. Following this, Long's test of predicted probabilities is implemented to examine potential differences across sex for key variables robustly. The findings suggest the socially/deviant facet of psychopathy is associated with gang status in girls only; the effect was smaller than for both peer delinquency and violent victimization. Further executive functioning and the interpersonal facet were not significantly associated with gang status in boys and girls. The results of Long's test show no robust differences that exist across sex for variables of interest. Although sex differences are not robustly found in the current study it is suggested intervention strategies should consider the needs of girls, because strategies do not make these considerations. [ABSTRACT FROM AUTHOR]