University of Thi-Qar Journal for Engineering Sciences

The recent tendency of research is to hybridize two or more metaheuristics algorithms to find superior solutions in the field of feature selection problems. The Firefly Algorithm (FA) is a population-based optimization algorithm that attempts to replicate the normal behavior of firefly insects in seeking food. FA is broadly used in numerous engineering fields, but it endures from certain restrictions. This study emphasis emphasizes hybridizing FA with the Simulated Annealing algorithm (SA) as a strong local search algorithm to overcome FA limits and improve the overall performance in feature selection. In other words, a high-level relay hybrid (HRH) model is proposed in which self-contained optimization (i.e., FA and SA) are implemented in sequence. Obviously, metaheuristic algorithms (like FA) are not suitable for fine adjustment structures that are so near to optimal solutions, whereas local search algorithms (like SA) are the opposite. Accordingly, in the proposed FASA+FS model, the best regions are located by FA and then inputted to SA, respectively.

In this research, trustworthiness is improved, and the probability of malfunctioning in DC fan systems is decreased was addressed. This research suggests a new method that employs artificial intelligence to detect faults by combining Convolutional Neural Networks (CNN) with wavelet analysis for better anomaly detection. Ten fans were initially used in the experiment; three were faulty, and seven operated correctly. Data for all fans was collected using an Arduino. Wavelet features were employed to identify faults, followed by a CNN classifier (. Specifically, a binary CNN classifier) to determine the presence or absence of faults. The results were very promising, demonstrating significant potential for improving the reliability of DC fan systems.

Cold joints are necessary stops in the concrete placement process since pouring concrete in one continuous operation in many structures is impossible. The strength of the formwork and the batching and mixing capacities determine how much concrete can be placed at once. A well-made construction joint should have sufficient shear and flexural continuity across the interface. In this study, the effect of the position of cold joints and the compaction state of the first concrete layer on the flexural strength of reinforced concrete is experimentally investigated. Five beam specimens with dimensions of 160×100×1200 mm were tested. The variables investigated are the position of the cold joints (which were placed horizontally at compression fiber and tension fiber of the beams), and whether the first poured concrete layer is compacted or just left uncompacted until the second pour is placed To study the effect of the cold joint and compaction process on the ultimate load. The research results indicated that the ultimate load, ultimate deflection, ductility, and toughness were all reduced due to such cold joints. These behavior indices were primarily dependent on the location of the cold joint and compacted first layer. The compaction of the first layer and the location of the cold joint has a significant effect on the load-carrying capacity and the corresponding deflection of the tested beams, the decrease in ultimate load, ultimate deflection, ductility, and toughness for the beam, which was compacted, and placed horizontally at tension fiber were 13.78%, 34.95%, 32.1%, and 84.04% respectively. In contrast, for the beam which was uncompacted first layer, the decrease was 31.35%, 29.52%, 31.6%, and 61.03%. The decrease in ultimate load, ultimate deflection, ductility, and toughness for beams which was compacted and placed horizontally at compression fiber were 15.50%, 37.95%, 35.1%, and 85.575%, respectively, while for beam which was uncompacted first layer, the decrease was 40.91%, 43.76%,33.9%, and 72.7% respectively.

Electrochemical Machining (ECM) is an unorthodox technique for the removal of metals, which draws upon principles obtained from the field of electrochemistry. The technique relies on three essential components: an electrolyte, a workpiece, and a cathode tool. When an electric current flows, metal ions dissolve, leading to the elimination of material. (ECM) has been extensively utilized in several industrial sectors, notably in the production of components utilized in medical, aerospace, automotive, and general maintenance applications. The surface texture of the workpiece produced by electrochemical machining is influenced by a wide range of characteristics. The present study employs a research response surface technique to investigate the impact of key parameters, including current, electrolyte intensity, and gap, on the surface roughness of stainless steel 301 as a workpiece. The test utilized Minitab22 and the L27, which were created using the Taguchi orthogonal array. Each experiment utilizes distinct input values, and the outcomes are examined using analysis of variance (ANOVA). The optimal processing configurations and performance parameters are provided, with the conclusive testing outcomes and prediction model. The findings indicated that the predictive model exhibited an R2 coefficient of determination of 92.6%. This coefficient quantifies the capacity of the input variables to accurately forecast the output variables. The current of 80% was the primary factor in establishing the least surface roughness. The concentration of the electrolyte emerged as the subsequent crucial factor in setting the minimum surface roughness threshold of 15%.

Structures erected on soft soil often face challenges related to uncontrollable settlement and critical bearing capacity. The footing of numerous structures is prone to failure and collapse when situated on weak soil. In geotechnical engineering, enhancing the bearing capacity of a shallow footing is imperative. Ground reinforcement techniques are employed to improve the properties of weak soils, particularly addressing issues of compressibility and bearing capacity. One method for achieving this improvement is the use of woven geotextile reinforcement, as soil is proficient in compression but lacks tensile strength. By incorporating woven geotextile, the tensile potential of the soil is increased, consequently enhancing its load-bearing capacity. This paper reviews potential benefits of woven geotextile's (WG) in improvement the bearing capacity of square footing rest on clay soil. The model footing load tests were conducted on woven geotextile layers were added at varying distances. The findings demonstrated that, in comparison to unreinforced soil, the use of a woven geotextile system for reinforcement greatly increased bearing capacity and reduce settlement beneath the square foundation. Compared with unreinforced soil the ultimate bearing capacity increased about 1.91 times when reinforced with three layers of woven geotextile. Furthermore, the study employs advanced modeling techniques, utilizing the Plaxis-3D software program, to rigorously analyze the experimental results obtained from model footing tests. The results of these tests provide valuable insights into the effectiveness of the woven geotextile, contributing to a better understanding of their influence on soil stability and foundation support.

In this paper used a microencapsulated phase change material (MEPCM) suspension and phase change materials (PCMs) in a micro-pin fin heat sink (MPFHS) with three types of fins (circular, triangular and square) are Numerically investigated. The MEPCM suspension used consists of microcapsules composed of PCMs n-octadecane and RT44, and shell materials used poly-methyl-methacrylate (PMMA) and poly-alpha-olefin (PAO). This capsules are suspended in different pure fluids pure oil, pure water and ethylene-glycol at a concentration of (0-20)%. Also, the phase change materials (PCMs) used represented by n-octadecane and RT44. The results obtained display that used MEPCM suspensions as a coolant fluid in micro-pin fin heat sinks in place of pure fluid leads to improved cooling performance of micro-pin fin heat sinks compared with used phase change materials (PCMs) only. Also, unfinned heat sink gives lower pressure drop and high rate of heat transfer coefficient compared with all types of fins used.

Complex concentrated alloys have been thoroughly examined for their excellent mechanical properties. In the present study, phase transitions and mechanisms of dislocation for FeNiCrCoAl and FeNiCrCoTi complex concentrated alloys were examined using classical molecular dynamics simulations under uniaxial tension. The (LAMMPS) code was used to simulate CCAs systems by using EAM potentials. The effect of strain rate change has been taken into account. The results show that at the early plastic stage, the main deformation behavior is the transition from FCC to HCP phase. Moreover, tensile characteristics are negatively affected by strain rate rise. At strain rate value of , for FeNiCrCoAl, elastic modulus and density are computed to be and . Whereas, for FeNiCrCoTi, elastic modulus and density are calculated to be and . It can be noticed that elasticity modulus of FeNiCrCoAl is two times greater than that of FeNiCrCoTi. In contrast, young modulus of complex concentrated alloys decreases succinctly when the strain rate increases to a value of . Under tensile deformation, the movement direction and impact on mechanical properties of the prevalent 1/6 <112> Shockley partial dislocations are analyzed.

This paper reviews power control algorithms through interference management in a variety of network types, such as Device-to-Device (D2D) Networks, Unmanned Aerial Vehicles (UAVs), healthcare systems, and Low Power Wide Area Networks (LPWAN). The growing requirement for effective and consistent wireless communications advanced approaches to mitigate interference, ensuring optimal power usage and network performance. We explore the exceptional interference challenges and requirements of each network type. As wireless networks progress, managing interference becomes essential to maximize power usage and maintain network performance. This paper systematically reviews the latest advancements and methodologies that adjust power levels to mitigate interference. Additionally, we analyze these techniques and discuss their impact on spectral efficiency, supported by recent case studies. Our findings aim to guide researchers and practitioners in developing more effective power control and interference management solutions for next-generation wireless networks.

Wireless body area networks (WBANs) are noteworthy, dependable, and most advantageous development in many health directed applications. WBANs in health system connect tiny sensors to invasive or non-invasive medical equipment for patient examinations utilizing low power wireless technology. When WBANs are utilized in crowded areas or in conjunction with other wireless sensor networks, communication interference can arise. This can lead to unstable signal integrity, which can impair system performance. Thus, interference mitigation needs to be taken into account when designing. In this paper we survey interference mitigation methods used in WBANs, classify them, and finally point to some future research directions in this area. In particular, we start by reviewing sample papers that tackle the interference management problem through classical signal processing and shaping methods. Then we review some works on cooperative communications approaches to encounter this problem. After that we consider approaches that are not centralized through the use of game theoretic formulations. We close our discussion by surveying model free algorithm through learning based approaches.

This research highlights the synergistic impact of including varying steel fiber contents and size of coarse aggregate on the concrete's mechanical characteristics, including compression strength, workability, splitting tensile strength, and stress-strain correlations under compression stress. The study investigated steel fiber percentages ranging from 0% to 2% with increments of 0.5% and coarse aggregate maximum sizes, namely (9.5mm, 12.5mm and 19mm). The study entailed the production and examination of a total of fifteen concrete samples, comprising three cubes and two cylinders for each concrete mixture. The results demonstrated that the compressive strength of concrete mixes lacking steel fibers shows a positive relationship with the largest size of coarse aggregate. However, the inclusion of steel fibers causes a reduction in compressive strength as the maximum size of coarse aggregate increases. Finer coarse aggregate sizes resulted in the highest tensile strength. Moreover, the study showed that including hooked-end steel fibers (SF) enhances the stiffness of concrete cylinders and allows for deformation without fracture. And, the influence of maximum coarse aggregate size on stress-strain behavior is negligible. These findings emphasize the significance of taking into account both the overall size and the inclusion of SF in mixes of concrete to improve the compressive and tensile strength, stress-strain responses, and overall performance.

In this current Investigation, a solar PV system is applied. The Flying Squirrel Search Optimization (FSSO) algorithm for Maximum Power Point Tracking (MPPT) is examined. The flying squirrel's movement serves as inspiration for the FSSO algorithm, which simulates their movements and evaluates fitness to find the optimal voltage and current levels for maximum power output. Through dynamic positioning of "squirrels" (potential solutions), the algorithm strives to find the optimal voltage and current combination, hence optimizing the PV system's total efficiency. The MATLAB script that is included demonstrates the unique optimization method of the FSSO algorithm for MPPT in a solar PV system. The parameters and movement rules of the script can be customized to match the unique features of the PV system, offering application versatility. In general, these methods raise. Before applying the FSSO algorithm, ANN was applied to aid in enhancing MPPT's performance by creating an input-output model. ANN results are based on 8761 datasets collected from (www.kaggle.com). ANN resulted in the best validation performance obtained at epoch 30, which is a total of 36 epochs. The coefficient of determination (R-squared) for the linear regression between the predictions of the neural network and the actual target values is displayed as a regression equal to 0.84666. The results concluded that the neural network has reasonably good predictive performance. Based on these results, applying FSSO using 100 iterations, the results concluded that the maximum power point of tracking was accurately determined based on photovoltaic resources.