Al-Khwarizmi Engineering Journal

This study is concerned with the adsorption of Diclofenac (DIC) from polluted water using modified bentonite. Hexadecyl trimethyl ammonium bromide (HTAB) was used to alter the hydrophilic nature of the bentonite to a hydrophobic one. Various effecting parameters were investigated such as: contact time, pH, agitation rate, adsorption dosage,acidity pH, mixing time time, and initial adsorbate concentration (Co). The best result obtained for the removal of Diclofenac is 98% which occurred at 30 ppm initial concentration, acidity pH 7, 100 min mixing time, and o.3g/100ml (DIC) dosage. Langmuir isotherm model eas found the more suitable model to represent the adsorption process.On examining the kinetics of the adsorption process, the pseudo-second-order kinetic model well describes the experimental data. Finally, thermodynamic investigations show that the adsorption process is endothermic, spontaneous as well as exhibited characteristics of physisorption.

Implantation via an intercalary prosthetic method is a reliable solution for reconstructing a long defect in bones that has been damaged by severe disease or accidents. In the current study, using data from computed tomography (CT) scans, a novel costumed intercalary prosthesis design has been created. To achieve this purpose, the CT scan data of a patient in (DICOM) file format was converted into computer-aided design models and saved in stereolithography (STL) format using the 3D slicer (5.0.3) software. The STL files were loaded into Meshmixer software to design the models of the intercalary prosthesis. Finite element analysis (FEA) was applied to validate the strength of the prosthesis with impact, tensile testing, and torsional testing. According to the results of the impact tests, the highest recorded deformation was 8.8485e-002 m in the area where the implant body interfaces with the bone intramedullary canal, due to the high-stress (Von Mises Stress) value of 3.78 E9 PA. In the torsional loaded the highest deformation recorded was 1.3871e-008 m when the Von Mises stress reached 49006 PA, and with the application of the tensile test the largest deformation measured as 1.0458e-006 m at maximum Von Mises stress seen was recorded as 6.6012e+006 PA which caused that. A solid rod of Ti6Al4V alloy was selected. Finally, the analysis proved that the implant had enhanced mechanical properties. Based on the findings, it can be inferred that the prosthesis was successfully implanted, and a satisfactory result was obtained by using this design method.

This research offers an extensive study of different machine learning issues for predictive maintenance applications for motor condition ranging from AC motors. The research establishes a scenario that represents the real world to identify which algorithms can be trusted to predict both the time of failure and the actual type of failure in the AC motors. The employed machine learning algorithms include the Random Forest (RFC), Support Vector Classifier (SVC), K-Nearest Neighbor (KNN), Logistic Regression (LR), and XGBoost (XGB) in our work. The assessment includes the comparison of algorithms in terms of the predictive accuracy educated with different size training data. Before the model is developed, thorough data preprocessing methods will be applied that will allow the breaking down of the model assumptions and the optimization of the performance. For preprocessing step the following two steps are made including the removal of unclear samples, label encoding used for categorical columns, and column scaling. Intriguingly, the identification of seemingly outlier data points is revisited, revealing their integral role in capturing the natural variance of the dataset and enhancing classification tasks. These identified features are observed to be pivotal contributors to predictive models. The study shows that in both algorithm failure cases and failure type identification, their performances are comparable. Regarding training time, K-Nearest Neighbors (KNN) algorithm yields the top-performing model for both datasets ( 4 sec and 3 sec) respectively, whereas Random Forest (RFC) performs the worst training time (151 sec) which belong to the binary failure prediction task and XGBoost (XGB) in multi-class failure prediction task (276 sec), which is contributed. Finally, this paper emphasizes that deciding on which machine learning model is appropriate for predictive maintenance can be quite a challenge due to the necessity to balance between accuracy and training time. The findings constitute important tipping point for those companies that aim to implement a solution for predictive maintenance with the KNN model being faster and efficient at the same time.

Friction stir welding (FSW) is an innovative solid joining operation that has recently been intensively adopted in welding various similar and dissimilar metallic materials, including aluminum, steel, titanium alloys, and others. The success of FSW relies on a set of parameters like rotational speed, travel speed, axial force, tool geometry, etc. The role of tool geometry (involving both pin and shoulder design) is highly important in producing sound and high-strength weld joints. Therefore, this research aims to review the latest published works regarding the performance of different tool geometries. The discussion of the findings of the cited works revealed that each tool pin design has shown different behavior due to the various stirring efficiencies of the materials being welded. All in all, the square threaded pin had the best geometry in terms of its mechanical properties compared to other pin designs. The threaded cylinders and threaded taper are most commonly utilized and offer good joints, while the maximum joint efficiency was achieved by the square pin profile and it reached 94% in some investigations.

Numerous applications have been developed, primarily focusing on achieving platform equilibrium. The system may employ electric motors or hydraulic cylinders, conventional valves, or a control mechanism that utilizes pressure sensors. The majority of these sources exhibit inaccuracies, demonstrate a sluggish response time, and may require periodic reorganization. Before implementing actual systems, it is imperative to develop a virtual system through the utilization of simulation techniques. This research aims to develop and evaluate a platform comprising two hydraulic cylinders and two proportional valves. The design of a hypothetical hydraulic system involves an analysis of the system and the derivation of its transfer function. Subsequently, the performance of the system is assessed by identifying the optimal approach for acquiring the parameters kp, ki, and kd, an examination is conducted to optimize the existing genetic algorithm (GA) PID controller and the particle swarm optimization (PSO) algorithm through the utilization of MATLAB simulation. The results indicate that the genetic algorithm (GA) PID algorithm outperforms the particle swarm optimization (PSO) PID controller in enhancing system performance. The chosen error standard for the self-balance platform control system is the Integral Time Absolute Error (ITAE) type. The system exhibits an improved rate of convergence.

The primary focus of this project is developing a cutting-edge remote monitoring of patients device for use in an intensive care unit (ICU). The gadget is designed to capture and transmit vital health data from the patient\\'s body, including oxygen saturation levels, heart rate, and temperatures. It is also capable of monitoring a pacemaker. Information is then sent to a remote location after that. The suggested system receives the patient\\'s vital signs and uses them as input on a computer in the nursing room. This transmission is made possible via a transmitter-receiver system consisting of two Arduino kits connected to Bluetooth devices. If any anomalous numbers are discovered as the data is processed, the computer notifies the doctor via the Global System for Mobile Communications (GSM) and the doctor\\'s mobile device. The primary objective of this device is to enable remote monitoring of a patient\\'s medical parameters and prompt alerting of doctors who are situated in remote places of any concerning circumstances. The doctor\\'s mobile device receives the collected values from the workstation. The entire system\\'s structure is implemented using the Visual Basic programming dialect.

Foot morphology evaluation techniques are commonly used to evaluate foot abnormalities. The foot is essential for keeping the biomechanical performance of the lower extremities. Knee osteoarthritis is a pathological condition that could lead to foot deformities. The major goal of this study was to investigate the prevalence of foot deformity in patients with Knee osteoarthritis. Another goal was to quantify the impact of pain and the severity of osteoarthritis foot morphological changes in Knee osteoarthritis patients.A total of seventy-eight participants took part in this study, including forty-six non-pathological subjects and thirty-two subjects diagnosed with knee osteoarthritis. Foot characteristics were assessed by a podoscope that can automatically estimate foot morphological parameters including Arch Index, Chippaux-Smirak Index, Staheli Index, Weisflog\'s index, Clarke’s angle and Hallux valgus angle. Numerous foot-related variables have been connected in a direct manner to knee osteoarthritis. Particularly, those who had knee osteoarthritis had substantially higher values for the Arch index (0.29±0.018; P=0.00), Chippaux-Smirak index (0.55±0.5; P=0.00), Staheli index (0.77±0.7; P=0.00), and Weisflog\'s index (2.7±0.25; P=0.00) than those who did not have this disease. Furthermore, their Clarke\'s angle and Hallux valgus angle exhibited high values of (30.27±2.7) and (13±1.8), respectively. There were also significant correlations found within the knee osteoarthritis group. There was, for example, a substantial positive correlation between the Arch Index and the Chippaux-Smirak Index (0.767; p=0.00), the Arch Index and the Staheli Index (r=0.35; p=0.04), and the Chippaux-Smirak Index and the Staheli Index (r=0.44; p=0.01). In terms of foot abnormalities, the Midfoot (333.528.6; p=0.00) was more significant than the Rearfoot (604.25±31.2; p=0.85) and Hindfoot (433.3±35.2; p=1.66). With association values of (r=0.4; p=0.02) and (r=0.4; p=0.04), the Arch Index and Chippaux-Smirak Index were both significantly linked with the severity of knee osteoarthritis.Foot abnormalities are substantially more prevalent in persons with Knee osteoarthritis. Collapsed medial longitudinal arch, transverse arch, and hallux valgus are all variables to consider in the management of Knee osteoarthritis. Healthcare providers may target Knee osteoarthritis effects on midfoot morphology with focused therapies. Custom orthotic devices, footwear adjustments, and particular workouts may enhance midfoot stability and alignment. Assessing foot morphology allows healthcare providers to enhance knee osteoarthritis therapy and patient results.

The limited mobility of lower limb amputees highlights the need for advancements in prosthetic control strategies to restore natural locomotion. This paper proposes an information fusion approach for gait phase recognition using surface electromyography (sEMG) and kinematics data. Time-domain (TD) features were extracted from the myoelectric data and three data-driven models, specifically Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN), were compared in three different input conditions i.e. sEMG features, hip angle, and their fusion. Gait phase estimation results averaged from 40 healthy participants during normal walking with 10 strides per each demonstrated that the proposed fusion approach has consistently outperformed (p<0.0001) the other two conditions achieving a maximum accuracy of 85.48% with SVM. The findings suggest promising applications in prosthetic motion control and rehabilitative exoskeletons, highlighting the potential for improved user-driven strategies in lower limb prostheses.