Non Revenue Water (NRW) refers to the volume of water that is distributed from the water plant but does not get billed to customers, which is a major challenge for water bodies.It represents the difference between the total volume of water pumped into the water distribution system (WDS) and the volume actually billed to customers.NRW is composed of three components: physical losses, commercial losses, and unbilled authorized consumption.These losses cause financial deficits, increased operational costs, and infrastructure deterioration, making NRW reduction a critical challenge for water bodies globally. This study reviews optimization strategies for minimizing NRW, focusing on advanced metering infrastructure (AMI), remote leak detection (acoustic, pressure, and flow sensors), Geographic information systems (GIS), data analytics, machine learning, and digital twin modeling. Findings suggest that integrating emerging technologies, predictive analytics, and data-driven decision-making can significantly enhance water distribution efficiency. Future research should focus on AI-driven optimization, predictive maintenance, and sustainable water management strategies to optimize non revenue water.

In this study, we used a particle swarm optimization (PSO) algorithm to address a variation of the non-deterministic polynomial-time NP-hard traveling tournament problem, which determines the optimal schedule for a double round-robin...

This article introduces a novel three-phase angular perturbation technique for solving the School Bus Problem (SBP), an NP-hard combinatorial optimization challenge. The three phases—initial solution, global and local search, and solution exploration—focus on optimizing school bus routes by minimizing sharp turns and reducing travel distances. The performance of three metaheuristic algorithms (GAACOSA-AP, BFSPSO-AP, RL-AP) is evaluated, and significant improvements in routing efficiency are observed. we evaluated the performance of our hybrid algorithm against the industry-leading optimization software, CPLEX, and other optimization techniques across multiple school scenarios. The results show that the GAACOSA-AP algorithm consistently outperforms its counterparts, achieving substantial reductions in travel distances, particularly for complex routes with numerous stops. The integration of Angular Perturbation was key to improving route geometry by, reducing sharp turns, and enhancing overall path optimization by an average reduction of 27.12% compared to 22.37% without Angular Perturbation.

This paper introduces a novel Concave Matrix Generative Adversarial Network (CM-GAN) designed for advanced cybersecurity threat detection and contextual awareness modeling in digital infrastructures. The proposed framework integrates a concave matrix regularization mechanism to embed non-linear structural dependencies between independent (attack, defense, response) and intervening (user behavior, network load, system vulnerability) variables within the GAN learning process. Unlike traditional GAN-based models, CM-GAN enhances interpretability, training stability, and detection precision. The model is evaluated using the CSE-CIC-IDS2018 dataset and benchmarked against two customized baselines: the Matrix GAN with Awareness (MGAN) and the Matrix-Based GAN (MB-GAN). CM-GAN demonstrates superior performance across binary and multiclass classification tasks, achieving accuracy, recall, and F1-scores exceeding 99%, and demonstrating higher anomaly realism with robust detection fidelity. These results confirm the efficacy of CM-GAN as a structure-aware, context-sensitive solution for real-time cyber-threat intelligence, particularly in resource-constrained environments such as academic networks.