American Journal of Applied Science and Technology

A Framework for Optimal Sizing and Performance Assessment of Photovoltaic Power Plants Under Extreme Climatic Stress: Evidence from The Sudanian Sahel Region

2026-07-01T11:30:56+00:00

The deployment of photovoltaic (PV) power plants in arid and semi-arid regions is increasingly challenged by extreme climatic stressors such as high temperature variability, dust accumulation, cloud intermittency, and irradiance fluctuations. This study proposes a comprehensive analytical framework for optimal sizing and performance assessment of PV power plants under such harsh environmental conditions, with specific focus on the Sudanian Sahel region. The framework integrates climatic degradation factors, system-level electrical behavior, and optimization-based sizing strategies to enhance energy yield reliability and lifecycle efficiency. Building upon established modeling approaches and performance evaluation techniques in PV systems, the study critically synthesizes empirical and theoretical findings from prior research, including PV temperature response behavior (Ouedraogo et al., 2021), dust impact mechanisms (Ali Omar Mohamed and Abdulazez Hasan, 2012), and hybrid system optimization strategies (Ogunjuyigbe et al., 2016). A key methodological foundation is derived from photovoltaic system emulation and performance modeling approaches discussed by Ayop Razman and Tan Chee Wei (2017), which are referenced throughout the analytical development. Results indicate that ignoring extreme climate-induced degradation leads to significant overestimation of energy output and under-sizing of storage and conversion components. The proposed framework demonstrates improved predictive accuracy for system sizing and enhanced robustness under climate variability. The study contributes a scalable decision-support model for renewable energy planners in Sahelian environments, supporting sustainable electrification and infrastructure resilience.

Detecting Cyber Attacks Using Artificial Intelligence Approaches (DCAIA)

2026-07-20T16:22:22+00:00

Cyber attacks become more advanced and risk to information systems and digital infrastructure. Legacy security mechanisms are often blind to novel and mutating threats. This research examines the deployment of Artificial Intelligence methods for discovering cyber threats. Benchmark datasets as NSL-KDD and CICIDS2017, applied machine learning and deep learning models. Results: The AI-based methods are effective in detection of disorders and significantly outperform conventional techniques with respect to the accuracy of detection and false positives.

Development of An Efficient Screw Conveyor Design and Calculation of Traction Force Along the Working Zones of a Wavy Screw Surface for Fiber Material

2026-07-09T11:51:47+00:00

The article presents the theoretical results of determining the traction force of cotton fiber transported and cleaned using an efficient three-zone vibrating mesh surface system. The system is designed with a wavy screw conveyor, inclined perforations, and elastic supports. Formulas for calculating the traction force across all three working zones of the mesh surface are derived, and corresponding graphical relationships are established. Based on the analysis of these relationships, recommended parameter values are provided.

Collaborative Robots (Cobots): Human-Robot Collaboration in Smart Manufacturing Systems

2026-07-20T10:46:27+00:00

The integration of collaborative robots (cobots) into modern manufacturing environments has become one of the key components of Industry 4.0. Unlike traditional industrial robots, cobots are designed to operate safely alongside human workers within shared workspaces. This study investigates the principles of human-robot collaboration, analyzes safety mechanisms, and evaluates the impact of collaborative robotic systems on manufacturing productivity and operational flexibility. A comparative analysis of traditional industrial robots and collaborative robots was conducted based on productivity, safety, deployment cost, and adaptability indicators. The obtained results demonstrate that cobots significantly improve production efficiency, reduce operator workload, and increase workplace safety. The study also discusses future developments involving artificial intelligence, machine vision, and adaptive control systems in collaborative robotics.