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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. [ABSTRACT FROM AUTHOR]

This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10. [ABSTRACT FROM AUTHOR]

This study presents a mathematical model for resolving the location routing problem of agricultural product transportation in royal projects in Thailand. This work aims to minimize the distance between pickup subcenter point locations and transport route links in each area. The difference in product amount in each area affects the product's capacity in the subcenter and its transport to the royal project's main center under the conditions of weight score classification depot, depot capacity, delivery demand, truck capacity, and transport distance. Due to the similar performance of each royal project, selecting a suitable subcenter is difficult. As a result, the mathematical model is created to solve the decision-making problem of selecting the subcenter for pickup and transportation. The mathematical model assumption is tested by the Lingo V16 program, which uses an exact method to find the appropriate subcenter point selection on the five instants in each size of the problem. The testing revealed that the mathematical model could choose an appropriate subcenter point in a small-scale problem effectively and quickly. However, it is not suitable for problem-solving in a medium or large-scale problem. The increasing number of data points influences solution generation speed and quality. The exact method of problem-solving with the Lingo program is unsuitable for problem-solving when the problem is complex and has several parameters that change depending on the current situation. In order to achieve faster algorithms, the best solution-finding method will be improved in the future with a meta-heuristics approach to algorithm design. [ABSTRACT FROM AUTHOR]

In this article, we address a model predictive control problem for constrained linear systems in the presence of l2 disturbances. In the proposed control method, the feedback gain and the controller state are updated online so that the l2 gain of the system is minimized. The problem of determining the feedback gain and the controller state is formulated as a convex optimization problem with linear matrix inequality constraints. We show that both feasibility of the control algorithm and the dissipation inequality are guaranteed for all times. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]