The University of Misan is an Iraqi university located in Amarah, Maysan, Iraq. It was established in 2007. Originally, the university consisted of the College of Basic Education and the College of Education, which were part of University of Basrah..
Doping CZTS (copper-zinc-tin-sulfide) by replacing zinc with cadmium atoms is a crucial process for improving its electrical and optical properties. The primary goal is to modify the bandgap and increase the electrical conductivity of the material to enhance its efficiency in solar cell applications. This substitution induces a phase transition in the crystal structure from kesterite (favorable for zinc) to stannite (stable for cadmium). Experimental results showed that the pure sample (x = 0) was unstable, with a large dispersion in the conductivity measurements. Adding cadmium at a low ratio (x = 0.01225) improved the stability of the measurements while the conductivity decreased to similar to 10 S/m due to distortion stress in the crystal lattice. Increasing the ratio to x = 0.0269 resulted in a dramatic jump in the conductivity (similar to 56 S/m) which is an indication of the onset of the phase transition. A computer code based on the K-Means algorithm was used to analyze the dispersion of measurements and isolated the most statistically reliable group.
To enhance implants' biocompatibility, bone integration, and corrosion resistance, this research aims to coat a stainless steel 316L substrate with a bio-composite composed of chitosan (Chi) and polycaprolactone (PCL) polymers, strengthened by pumpkin and hydroxyapatite (HA). Surface characteristics were examined using SEM, FTIR, hardness, surface roughness, wettability behavior, and antimicrobial activity. Based on the test results, the (CHI-PCL-HA-Pumpkin) coating showed the best bacterial resistance against Staphylococcus aureus and Pseudomonas aeruginosa and the highest hardness and surface roughness. SEM results demonstrated a uniform structure, a good border of reinforcement with the matrix, and coating layers free of cracks. The incorporation of nanoparticles gave the coating its high wettability. The blended matrix's superior hydrophilia and reinforced particles gave coatings containing CHI, PCL, HA, or Pumpkin exceptional wettability. The FTIR data demonstrated the apparent affinity between the matrix and nanoparticles with no noticeable wavelength changes. These results highlight the intriguing possibilities of biofilm coatings for use in orthopedics and hold promise for advancing biomedical engineering.
This research introduces a Machine Learning-Powered Intrusion Prevention System (ML-IPS) as a robust solution to address the challenges posed by evolving cyber threats. The ML-IPS combines timely threat detection with enhanced accuracy for real-time attack prevention, offering a resilient defense against a broad spectrum of cyber-attack. This study delves into the comprehensive evaluation of a machine learning-powered IPS that ingeniously harnesses the power of advanced algorithms to facilitate real-time threat detection and significantly enhance the overall accuracy of the system. The efficacy of intrusion prevention systems (IPS) that employ machine learning (ML) is greatly dependent on the choice of suitable ML algorithms and the evaluation of their precision and inference duration. This research embarks on an in-depth evaluation of ML models for IPS applications, focusing on a comprehensive comparison of their accuracy and inference time metrics. Additionally, the methodology employs a supervised learning approach using a labeled dataset (CICIDS2017) containing both benign and malicious network traffic, providing a realistic and practical approach. The simulation results demonstrate that Decision tree and random forest algorithms can improve the prevention of attack in real-time by achieving 99.88% accuracy and about 10ms for time of detection. The findings demonstrate that the Intrusion Prevention System (IPS) is adept at promptly identifying and reacting to assaults, thereby furnishing a stronger and more durable safeguard against the ever-changing landscape of cyber hazards.
Background: The rapid growth in the number of renewable energy sources (RES) connected to the electrical grid has created a significant power quality (PQ) issue with voltage instability at the Point of Common Coupling (PCC) due to their intermittency. Although several controllers have been proposed to address these issues, an artificial neural network (ANN) based on a DSTATCOM controller for the hybrid grid is commonly used. However, a standalone ANN typically loses its ability to maintain DSTATCOM control due to premature convergence caused by suboptimal weight tuning during sudden changes in weather conditions. Aim: The goal of this research project is to develop an improved method for tuning the weights of an ANN to enhance its response to rapid changes in renewable energy output while ensuring the controller operates successfully under these conditions. Methods: This study proposes using a Particle Swarm Optimization (PSO) algorithm to optimize the ANN-DSTATCOM controller's tuning weights to regulate the DC-link voltage of a Hybrid Power System comprising Photovoltaics (PV), Wind Turbines (WT), and conventionally generated electricity. The proposed controller is implemented and evaluated using MATLAB/Simulink simulations. Results: Our results were based on simulations comparing the proposed PSO-ANN controller with existing PI and standalone ANN controllers. The PSO-ANN controller performed far better than the other two controller types in terms of system stability and oscillation reduction. Total Harmonic Distortion (THD) is measured at 2.74% for voltage and 3.37% for current, which meets IEEE 519 compliance while providing a record-breaking, superior response speed that restores system stability after disturbance or fault events. Discussion: The improved performance of the PSO-ANN controller compared to other solutions is attributed to the PSO algorithm's ability to efficiently optimize ANN weights so that the PSO-ANN controller can be more adaptable to varying conditions resulting from the use of renewable energy sources. Due to the limitations imposed by the simulation scenarios used, future work will include physical validation and expansion to larger hybrid systems. Conclusions: The PSO-ANN-based DSTATCOM controller described here represents an efficient means of enhancing both the power quality and the of renewable
Background: Solar air heaters (SAHs) represent a promising passive solar technology for space heating and drying applications. Despite their potential, the combination of thermal (first-law) and exergy (second-law) performance evaluation for SAHs fabricated from low-cost, locally available materials remains largely unexplored. Aim: This study aimed to design, build, and experimentally evaluate three low-cost SAH absorber configurations. Methods: Three absorber configurations (i.e., aluminium tube, corrugated aluminium foil hose, and trapezoidal galvanized steel plate) were constructed at the University of Misan, Maysan, Iraq. The SAH was tested under natural and forced convections at tilt angles of 25 degrees, 30 degrees, and 35 degrees. Thermal efficiency was calculated using the first law of thermodynamics, while exergy efficiency was determined using the Petela factor model. Results: Under natural convection, the trapezoidal steel absorber achieved the highest air temperature increment (similar to 65 degrees C near solar noon). The optimal tilt angle was 30 degrees, yielding an outlet air temperature of approximately 90 degrees C. Under forced convection, the trapezoidal absorber achieved thermal efficiency of 22.9% and exergy efficiency of 0.14%, both superior to the aluminium foil hose (21%; 0.13%) and aluminium tube (20%; 0.12%) configurations. Total fabrication cost ranged from USD 120 to 150. Discussion: Forced convection improved heat transfer by increasing the convective coefficient and reducing thermal boundary layer resistance, resulting in superior first-and second-law efficiencies despite a lower temperature increment. The low exergy efficiencies are consistent with the literature for low-temperature solar thermal systems and reflect the inherent irreversibility of converting high-grade solar radiation into low-grade heat. Conclusions: The trapezoidal galvanized steel absorber, oriented at 30 degrees and operated under moderate forced convection, constitutes the most thermodynamically efficient and cost-effective SAH configuration among those evaluated. The findings support the viability of locally manufactured SAHs for domestic and agricultural applications in rural and high-irradiance regions.
