Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.

The classification of certain agricultural species poses a formidable challenge due to their inherent resemblance and the absence of dependable visual discriminators. The accurate identification of these plants holds substantial importance in industries such as cosmetics, pharmaceuticals, and herbal medicine, where the optimization of essential compound yields and product quality is paramount. In response to this challenge, we have devised an automated classification system based on deep learning principles, designed to achieve precision and efficiency in species classification. Our approach leverages a diverse dataset encompassing various cultivars and employs the Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning model (P-AMIS-E). This model integrates ensemble image segmentation techniques, including U-Net and Mask-R-CNN, alongside image augmentation and convolutional neural network (CNN) architectures such as SqueezeNet, ShuffleNetv2 1.0x, MobileNetV3, and InceptionV1. The culmination of these elements results in the P-AMIS-E model, enhanced by an Artificial Multiple Intelligence System (AMIS) for decision fusion, ultimately achieving an impressive accuracy rate of 98.41%. This accuracy notably surpasses the performance of existing methods, such as ResNet-101 and Xception, which attain 93.74% accuracy on the testing dataset. Moreover, when applied to an unseen dataset, the P-AMIS-E model demonstrates a substantial advantage, yielding accuracy rates ranging from 4.45% to 31.16% higher than those of the compared methods. It is worth highlighting that our heterogeneous ensemble approach consistently outperforms both single large models and homogeneous ensemble methods, achieving an average improvement of 13.45%. This paper provides a case study focused on the Centella Asiatica Urban (CAU) cultivar to exemplify the practical application of our approach. By integrating image segmentation, augmentation, and decision fusion, we have significantly enhanced accuracy and efficiency. This research holds theoretical implications for the advancement of deep learning techniques in image classification tasks while also offering practical benefits for industries reliant on precise species identification.

A new computer geometric modeling approach for 2×2 twill woven fabrics structures has been presented. A 3D model considering their inherent skewness is proposed and can be used in precise geometrical description. Balanced fabric geometry occurs in 2×2 twill weaves where the fabrics are defined by floating and intersecting segments in weaves, which create symmetric floatings on the front and back surfaces of the fabrics. The yarns cross-section of twill woven fabrics uses the concept of virtual locations and each cross-section is rotated along a single yarn path. The curve of each filament in each of two successive cross-sections is approximated by NURBS. The approach described is demonstrated in 3D CAD for twill woven fabrics structures by using the geometric parameters considering their inherent skewness. The simulated twill woven fabrics structures must be improved so that they are a true visual simulation of real twill woven fabrics and can demonstrate wide varieties of form.

This study presents the development of a three-dimensional (3D) filament assembly model for predicting the air permeability of woven fabrics composed of spun yarns. To address the limitations of conventional single-line yarn models, the proposed framework incorporates fiber-level geometric representations using non-uniform rational B-splines (NURBS) and simulates multiple weave patterns—including plain, basket, twill, and rib—under various set density configurations. Each yarn was modeled with accurate filament distribution and cross-sectional layering, enabling the construction of realistic unit-cell-based CAD geometries. Computational fluid dynamics (CFD) simulations were performed using the k-ε turbulence model in SolidWorks Flow Simulation and validated against experimental measurements conducted under ISO 9237:1995 conditions. The filament assembly model achieved high predictive accuracy, exhibiting a lower of percentage prediction errors than the single-line yarn path model, thereby more effectively capturing airflow behavior through inter-yarn and intra-yarn pores. These findings highlight the capability of integrated CAD/CFD methodologies for virtual prototyping of breathable textiles and provide a robust foundation for high-precision performance prediction in functional and technical fabric design.