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This study investigates the soliton solutions, stability, and chaotic characteristics of the M fractional (3+1)-dimensional generalized B-type Kadomtsev–Petviashvili (gBKP) equation, where a Galilean transformation is performed to get the related system of equations. Advanced mathematical and analytical techniques are utilized to explore the soliton solutions and bifurcation analysis in a fractional-order nonlinear system, which helps to understand the functioning of complex systems. Perturbations are introduced to those systems to enable the observation of bifurcation analysis, including phase portraits. We also apply analytical technique the unified method to derive soliton solutions for the M-fractional gBKP model. This work reveals various soliton solutions, including kink, anti-kink, periodic waves, kinky periodic waves, and periodic lump waves. The solutions are graphically analyzed to explore their dynamic properties for fractional parameters. Moreover, we examine the suggested model's stability. We visualize the diverse range of soliton-like solutions to demonstrate the significance of the findings and the effectiveness of the proposed methodology. These results enhance the understanding of soliton behaviors in M fractional gBKP equation and portray how effective the medium approach is for solving complicated nonlinear systems.

Probabilistic roadmaps (PRM) are a cornerstone of autonomous motion planning, yet they face a persistent trade-off: efficient sampling often yields jagged, kinematically infeasible paths, while existing solutions, such as Gaussian biasing or post hoc spline smoothing, either compromise global connectivity or impose significant computational overhead. This study introduces PRM-Star, a novel unified planning framework that overcomes these limitations by embedding adaptive obstacle-aware sampling and curvature-based optimization directly into the roadmap construction pipeline. Unlike traditional two-stage approaches that treat smoothing as an afterthought, PRM-Star dynamically adjusts sampling density in narrow passages and utilizes a curvature-minimizing connection strategy to generate inherently smooth, production-ready trajectories in real time. The algorithm leverages k-d tree data structures and grid-based operation tracking to maintain high computational efficiency without sacrificing path quality. The proposed method was rigorously benchmarked against Standard PRM, Gaussian-PRM, PRM-Lite, PRM-RRT, PRM-B-Spline, and PRM-Cubic-Spline in complex simulated environments. Empirical results demonstrate that PRM-Star significantly outperforms state-of-the-art variants, reducing total path curvature by approximately 82% (from > 1000° to ˜181°) and waypoint redundancy by 76% compared to standard approaches. Statistical validation using one-way ANOVA, Tukey's HSD, and Cohen's d effect size analysis confirms that these improvements are statistically significant (p < 0.05) and yield large effect sizes. By harmonizing the conflicting goals of kinematic smoothness and computational scalability, PRM-Star offers a robust, statistically validated advancement suitable for deployment in resource-constrained autonomous systems.

Breast cancer remains a leading cause of cancer-related deaths among women worldwide, with early and accurate diagnosis being critical to improving survival rates. While deep learning has revolutionized medical image classification, current models often face significant challenges in balancing intricate local features with global features. This study presents a hybrid multi-class classification framework using the Swin Transformer and ConvNeXt model. The proposed SwinTConvNeXt-LDGF model dynamically fuses local-global features within a learnable dynamic gating network and classifies pathological images into different categories. The model was evaluated on an eight-class histopathology BreakHis dataset, achieving a test accuracy of 95.53%, a precision of 94.74%, a recall of 96.32%, and an F1 score of 95.47%. These results demonstrate the effectiveness of combining the Swin Transformer and ConvNeXt backbones within a unified, learnable dynamic training framework. The proposed approach emphasizes the strong potential of SwinTConvNeXt-LDGF to support pathologists in the real-world classification of breast cancer subtypes.

The emergence of wireless technology brought about enhanced communication across various devices, resulting in the demand for efficient and reliable wireless networks, like wireless mesh networks (WMNs) and mobile Ad-hoc Networks (MANETs). MANETs are known for their decentralized nature, rapid deployment, infrastructure-less operation, adaptability, and ease of use in several applications and outdoor events. Despite their flexibility, they often face challenges relating to security vulnerabilities, together with blackhole and grayhole attacks, and trade-offs in terms of performance relating to reliability and integrity. This paper proposes an improved, innovative routing protocol for Ad-hoc On-Demand Distance Vector (AODV) by infusion of blockchain's proof of stake (PoS) consensus mechanism named PoSAODV, whose objective is to enhance security, energy-efficiency, and adaptability while reducing packet loss rate, routing overheads, and increasing throughput. Smart contract-based validator selection was utilized to ensure fairness and reduce blackhole and grayhole attacks. The result obtained through simulation demonstrates that PoSAODV outperforms the original AODV by reduced latency of 0.79 ms, average throughput of 45 Mbps, and packet delivery ratio of 80%–100% in both unsafe and safe environments. This makes PoSAODV suitable for resource-constrained ad-hoc networks with dynamic topologies.

The field of electrical power systems, particularly power conversion systems, is a domain that is undergoing continuous development to enhance efficiency and performance. Inverters are extensively utilized in the conversion of direct current to alternating current in a multitude of applications, necessitating precise control to optimize their performance. Among the most prevalent control strategies is proportional integral differential control (PID), which relies on a linear response to error, directs it, and is efficacious in numerous applications. However, it encounters challenges in non-linear or variable systems. In contrast, Fuzzy logic control (FLC) offers a more adaptable response to nonlinear and complex systems, such as inverters. This paper aims to compare the system performance of PID and FLC for controlling an H-bridge inverter by analyzing their performance in improving the system response. The proposed FLC alleviates the performance trade-offs, reducing RMSE by $��\cong �60.4�\%�$ (nominal) and $��\cong �61.3�\%�$ (variable load) and lowering THD by $��\cong �43.4�\%�$ and $��\cong �30.4�\%�$, while maintaining voltage regulation within ±0.47 V (0.55%) in the nominal case and ±0.38 V (0.45%) in the variable-load case around 85 V.

To improve optimization efficiency in the conceptual design of Blended Wing Body (BWB) Unmanned Aerial Vehicles (UAVs), an adaptive Kriging-based surrogate modeling framework is established. This framework addresses the high computational cost inherent in repeated numerical simulations in multidisciplinary analysis (MDA). First, a streamlined MDA model integrating geometry, mass, aerodynamics, and flight performance was developed. Subsequently, three optimization strategies, comprising a baseline non-adaptive Kriging model, an exploration-oriented strategy, and an exploitation-oriented strategy, were implemented and compared for the objective of maximizing flight range. To ensure robustness and mitigate stochastic bias, 15 independent randomized trials were conducted for each strategy. Statistical results demonstrate that the exploitation-oriented strategy delivers superior performance, with the median optimization result achieving a 14.5% range enhancement over the baseline design. Notably, for this representative optimal configuration, the discrepancy between the Kriging prediction and the numerical simulation is a mere 0.48%. Finally, a dual-method parameter sensitivity analysis identifies fuel mass as the most influential parameter, followed by the center wing span. The proposed framework provides an efficient and robust methodology for the global optimization of BWB UAVs.

Heart disease is still one of the top causes of death globally, demanding early and effective prediction tools. Traditional predictive models frequently have limitations because they rely on single-source statistics, which may not represent the entire scenario of a patient's complex health profile. Feature selection is an essential preprocessing step in machine learning that improves model efficiency by eliminating irrelevant or redundant features. This study evaluates the impact of feature selection on six classification modelsNaïve Bayes, Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forestacross four datasets. Performance was assessed using accuracy, precision, recall, F1-score, and area under the curve (AUC) before and after feature selection. The results indicate that feature selection had varying effects across different models and datasets. In Dataset-01, Random Forest maintained its strong performance (98.70% accuracy) even after feature selection, while SVM and KNN saw noticeable drops, with accuracy decreasing from 88.96% to 79.87% and 87.01% to 81.17% respectively. For Dataset-02, SVM showed improvement, increasing from 80.00% to 85.00%, while Naïve Bayes and KNN also improved, reaching 82.50% and 82.50% accuracy, respectively. In Dataset-03, Logistic Regression and KNN experienced declines, with accuracy dropping from 88.76% to 83.71% and 91.57% to 87.08%, respectively, while Random Forest fell from 97.75% to 91.57%. Dataset-04 showed mixed results, with Logistic Regression improving from 86.67% to 91.11%, but KNN and Random Forest dropping from 97.78% to 84.44% and 91.11% to 82.22%, respectively. These findings suggest that while feature selection enhances performance for some models, it can negatively impact others, particularly those reliant on a larger feature set. Future research should explore adaptive feature selection techniques to optimize model performance across diverse datasets.

In Bangladesh's rapidly expanding e-commerce sector, selecting sustainable suppliers is crucial for ensuring long-term business viability and environmental accountability. This study develops an integrated decision-making framework by combining the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and rank suppliers based on cost, lead time, environmental impact, labor practices, and social responsibility. Expert judgment was used to assign weights to these criteria using AHP, with environmental impact emerging as the most significant factor. The AHP consistency ratio (CR) was found to be 0.02, indicating acceptable consistency. TOPSIS was then applied to assess 15 suppliers, identifying Supplier K as the most sustainable option, followed by Suppliers C and I. Sensitivity analysis showed that Supplier K consistently ranked #1 in 9 out of 10 different weight scenarios. The final criteria weights were: environmental impact 42.8%, lead time 7%, cost 4%, labor practices 23%, and social responsibility 22%.

This paper briefly describes the flexible movement training. Fifty male beginners from the basketball department of the Department of Public Sports at Soochow College of Soochow University were selected as the subjects of case study. They were divided into the control group using the traditional training method and the experimental group using the flexible movement training. The training lasted for 6 weeks, and the players' gait sensitivity was tested before and after the training. The results showed that the traditional training method could enhance the players' gait sensitivity, but the improvement effect was not obvious. Moreover, this method had a good effect on the players' linear acceleration and direction changing ability but had no significant effect on the gait sensitivity under the offensive and defensive state in the court. Flexible mobile training included part of the content in the traditional training method in the process of implementation, so it improved the players' gait sensitivity more comprehensively.

Rehabilitation robots are machines that allow patients to perform practice movements and ought to be sufficiently compliant for safe human interaction. Most robots today use hydraulic systems and continually rotating servo motors, making them less suitable for rehabilitation. The Pneumatic Muscle Manipulators (PAMs), however, offer an intriguing substitute for electric actuators due to their inherent compliance, but their nonlinear properties among force, displacement, and pressure make precise position control impossible in the classical controllers that neglect the significant phenomena of nonlinearities and handle the unmodeled dynamics of a pneumatic artificial muscle (PAM) manipulator. Hence, this paper presents adaptive fuzzy PID control for two-degree-of-freedom (2-DOF) PAM manipulators for leg rehabilitation to alleviate this problem. A 2-DOF robotic manipulator is designed to freely rehabilitate the upper and lower leg arms. MATLAB/Simulink software was used to perform the simulation. The results demonstrate that the fuzzy PID controller exhibits excellent transient performance, effectively handles nonlinearities, and efficiently rejects disturbances and parameter variations.