Al-Iraqia Journal for Scientific Engineering Research

Fog Computing (FC) acts as an intermediate computational layer between the cloud and Internet of Things (IoT) devices, designed to enhance service quality by processing tasks closer to the data source. However, effectively managing energy consumption (EC) remains a critical challenge due to the complexities of task scheduling. This paper proposes an enhanced task scheduling approach based on learning automata (LA) and neural network modeling to minimize fitness, makespan (MK), and associated costs in fog environments. Furthermore, an additional radial basis function (RBF) model is introduced to predict interdependencies among MK, fitness, and cost relative to virtual machine (VM) configurations. A Comparative analysis demonstrates the superior performance of the proposed LA-driven scheduling model over existing methods, achieving more efficient resource allocation and environmental impact reduction across key metrics. This study advances FC task scheduling techniques, highlighting the potential of integrated neural network models to optimize energy-aware computation.

The design and analysis of a small hexagonal ring microstrip antenna optimized at 5.5 GHz are presented in this work. The FR4 substrate, which has a dielectric constant of 4.3 and a thickness of 1.6 mm, is used to fabricate the antenna because it is inexpensive and compatible with printed circuit board technology. By extending the effective current path and allowing for multiple resonant modes, the hexagonal ring geometry improves radiation performance and impedance bandwidth when compared to traditional rectangular patches. Using CST Microwave Studio, Characteristic Mode Analysis (CMA) is used to analyze the antenna\'s modal behavior. According to the CMA results, higher-order modes aid in stability, but the dominant resonant mode around 5.5 GHz largely controls the radiation mechanism. With an impedance bandwidth of 126 MHz (5.392–5.518 GHz), the simulated response exhibits a resonance of –20 dB at 5.5 GHz. The antenna produces strong current distribution along the hexagonal edges, broadside radiation, and a steady gain of 6.11 dBi. The design is appropriate for WLAN and Wi-Fi 5 (IEEE 802.11ac) applications because of these features. The hexagonal ring structure is an efficient technique for increasing bandwidth and performance in wireless systems, as demonstrated by the combination of CMA and full-wave simulations

The reductionof the aerodynamic drag, a fundamental factor for the optimization of the performance of modern vehicles, is also because it has clear values of influence on power consumption, dynamic stability, and driving autonomy, particularly for electric vehicles. This paper, being a work of review, provides a state-of-the-art of the main principles and methods of air resistance reduction and focuses on the application of CFD to the analysis and optimization of the flow around the vehicle body. This research focuses on the fundamentals of pneumatic traction, design, and technology to mitigate pneumatic traction (i.e., spoilers, baseboards, active ventilation) as well as a literature review on the most noteworthy previous studies that applied CFD to analyze and improvepneumatic behaviour of conventional and electric vehicles. It also talks about drag coefficient and energy efficiency correlation, and the potential of applying artificial intelligence and generative design in the process of creating more efficient and sustainable results. The obtained results prove that the application of CFD tools to the preliminary design definition of the vehicle can widely improve the performance of the system, thanks to the reduction of the drag coefficient. The paper emphasizes the necessity for combining numerical simulations and laboratory work, and promotes the utilization of advanced analysis tools to enhance sustainable and efficient energy transfer in the vehicular environment

Accurate estimation of solar irradiance on tilted photovoltaic (PV) surfaces is central to performance prediction, system sizing, and financial assessment of solar energy projects. This study conducts a comprehensive comparative evaluation of five widely used irradiance transposition models—Liu & Jordan, Hay & Davies, Klucher, Perez, and Reindl—using 20 years (2001–2020) of climate data for Durban, South Africa. The models were assessed based on their ability to estimate global tilted irradiance (GTI) and the downstream impacts on PV system performance, including energy yield, performance ratio (PR), capacity factor (CF), solar fraction (SF), and avoided CO₂ emissions. A 7.2 kWp rooftop PV system with typical system losses, climatic inputs, and realistic operating assumptions was modelled to quantify differences across models. Results show a 7% variation in annual energy output and CO₂ mitigation potential, driven primarily by differences in diffuse irradiance treatment. Perez and Reindl consistently produced the highest GTI and PV output due to their robust handling of anisotropic sky conditions, while Klucher and Liu & Jordan delivered conservative estimates. Statistical error analysis (MBE, RMSE, nRMSE) confirmed Perez as the benchmark and Reindl as the closest-performing model. Overall, findings highlight the importance of transposition model selection for accurate PV performance evaluation, system bankability, and climate impact projections in diffuse-dominant subtropical climates.