Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
Urban stormwater systems face escalating challenges due to climate change, aging infrastructure, and increasing impervious surfaces, necessitating holistic frameworks that integrate hydraulic, structural, and operational factors. This study proposes a fuzzy logic-based controller to evaluate the performance of stormwater drainage systems through three linguistic variables: Hydraulic Performance, Technical Condition, and Operational Condition. The model synthesizes expert knowledge into 125 inference rules, enabling a unified assessment of system reliability. Validated against empirical datasets from European and Chinese drainage networks, the framework demonstrates robust performance across diverse geospatial and operational contexts. Unlike traditional deterministic methods or single-criterion fuzzy systems, the controller addresses interdependencies between hydraulic efficiency, material degradation and external stressors. By translating multi-dimensional uncertainties into actionable maintenance priorities—from “Immediate Replacement” to “No Action Required”—the model enhances decision-making for utilities balancing flood resilience and infrastructure longevity....
In this study, we analyze the winning percentage of 16 teams that have participated in Major League Baseball since 1901. First, 69 variables for each team are classified into pitching, batting, and fielding using factor analysis, and then the effect of the newly classified variables on the winning percentage is analyzed. In addition, after expressing each team’s winning rate as a fuzzy number using a fuzzy partition, the linear relationship between the previous year and the next year using the fuzzy probability is investigated, and we estimate the fuzzy regression model and Markov regression model using the Double Least Absolute Deviation (DLAD) method. The proposed fuzzy model describes variables that affect the winning percentage of the next year according to the winning rate of the previous year. The estimated fuzzy regression model showed that the on-base percentage allowed by the pitcher and the on-base percentage of the batter had the greatest effect on the winning percentage....
The main limitation of the Multidimensional Fuzzy Transform algorithm applied in regression analysis is that it cannot be used if the data are not dense enough concerning the fuzzy partitions; in these cases, less refined fuzzy partitions must be used, to the detriment of the accuracy of the results. In this study, a variation of the Multidimensional Fuzzy Transform regression algorithm is proposed, in which the inverse distance weighted interpolation method is applied as a data augmentation algorithm to satisfy the criterion of sufficient data density concerning the fuzzy partitions. A preprocessing phase determines the optimal values of the parameters to be set in the algorithm’s execution. Comparative tests with other well-known regression methods are performed on five regression datasets extracted from the UCI Machine Learning Repository. The results show that the proposed method provides the best performance in terms of reductions in regression errors....
This paper investigates the fixed-time (FXT) synchronization issue of fuzzy memristive neural networks (MNNs) via using incomplete Beta functions from the view of improving the estimate accuracy of settling time (ST). First, the parameter mismatching issue brought by the switching characteristics of the memristor is handled through the convex analysis method. Then, a new FXT stability theorem that provides a more accurate ST estimation is derived by using incomplete Beta functions. Furthermore, based on this result, some new sufficient conditions are obtained to ensure the FXT synchronization of considered fuzzy MNNs via designing a class of control schemes by introducing a new saturation function as well as using some inequality techniques. Significantly, the introduced FXT controller can achieve synchronization aim at bounded ST and it is not affected by the system’s initial values. Finally, a numerical example is provided to verify the affectivity of introduced results....
Interdisciplinary teaching is a pivotal strategy for deepening disciplinary theory and broadening students’ cognitive boundaries, crucial for the sustainability of education. By considering scientific knowledge’s humanistic background and technological evolution, this study proposes a novel interdisciplinary teaching framework based on the Source–Knowledge–Use (SKU) paradigm. Then, taking fuzzy mathematics as a case, the Humanities–Science–Technology Model (HSTM), based on a tripartite progression from humanistic foundations to scientific principles and then to technological applications, was established. This study systematically expounds the HSTM’s framework, contents, and implementation design, while critically examining potential challenges and corresponding mitigation strategies. The proposed SKU-based interdisciplinary teaching framework not only provides methodological guidance for interdisciplinary instruction in fuzzy mathematics but also offers transferable insights for cognate disciplines seeking to implement sustainable educational practices....
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