Scheduling problems under unavailability constraints has become a popular research topic in the last few years. Despite it’s important application in the real world, the uniform parallel machine scheduling problem was the least studied due to its complexity. In this paper, we investigated the uniform parallel machine scheduling problem under deterministic availability constraints. Each machine is subject to one unavailability period. Different versions of the problem regarding the type of jobs (identical and non-identical) and the performance measures (the total completion times and the makespan) were studied. For the case of identical jobs and for both performance measures, we developed linear programming models and optimal algorithms to provide a solution to the problem. For the case of non-identical jobs, we proved that the problem is NP-hard and propose a quadratic program. Because, this later cannot solve problems with very large number of jobs and machines, a heuristic was developed to find near optimal solutions to the problem especially with very large number of jobs and machines. The computational results showed that the heuristic’s performance is very high regardless the dimensions of problem instances.

Small-object detection plays a critical role in real-time aerial, Unmanned Aerial Vehicle (UAV), and satellite vision systems, as well as many other domains, where hardware constraints and deployment environments demand high accuracy, low latency, and energy efficiency. Although numerous lightweight object detectors have been proposed, there exists a lack of rigorous, cross-platform benchmarks that evaluate these models under consistent conditions for small-object-focused applications. This paper presents a comprehensive comparative evaluation of eight recent lightweight You Look Only Once (YOLO)-based models, including YOLOX-Nano and YOLOv6–v12 variants. Models are trained using a unified pipeline and evaluated on three representative datasets: VisDrone2019, DOTA-v1.5, and xView. Performance is assessed on both cloud GPUs (NVIDIA A100) and an edge AI platform (Jetson Orin Nano), using PyTorch and TensorRT (FP16) runtimes. The evaluation metrics include mAP@0.5, mAP@0.5:0.95, F1-score, latency, FPS, memory footprint, and energy consumption (via VDD_IN telemetry). Experimental results demonstrate that YOLOv8-Nano and YOLOv9-Tiny achieve leading accuracy across datasets, while YOLOv6-Nano and YOLOv10-Nano offer favorable trade-offs in speed and memory. TensorRT optimization yields 3×–5× speedup over PyTorch. Energy draw ranges from 7.6 W to 10.4 W, depending on the model and configuration. Pareto frontiers reveal that no single model dominates across all objectives. The study highlights the importance of multi-objective benchmarking in edge AI and provides practical guidance for selecting detectors based on deployment-specific constraints. The findings support theory-driven and application-oriented advancement of efficient detection architectures for real-time, resource-aware computer vision systems.

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems.