Al-Khwarizmi Engineering Journal

Earth–air heat exchangers (EAHE) hold great promise for reducing typical air conditioning systems’ energy consumption whilst preserving high indoor comfort. The present analyses a 3D model using COMSOL Multiphysics software for a geothermal heat exchanger to examine the thermal behavior along the piping system. The experimental findings of the real EAHE in Baghdad City during January and June are transferred into the nonisothermal pipe flow interface for modeling temperature, velocity and pressure distributions along the piping system. The temperature variation of subsurface soil and the radial temperature distribution around the pipe are modeled into the heat transfer interface. The magnitude of heat flux is also computed in different times along the pipe. The effects of the continuous operation of EAHE on the output temperature and on the soil temperature around the pipe are also considered. The model’s output demonstrates that the air temperature rise in January is 10 °C, whereas the air temperature drop is 14 ℃ in June. The effect of extracted/absorbed heat transfer from/to air in pipes is extended up to 0.7 m in the radial direction of the soil surrounding pipes due to continuous airflow in EAHE.

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated using precision, sensitivity, specificity, accuracy, and F-measure to classify CXR images into COVID-19, non-COVID-19 lung opacity, and normal control. Results showed a precision of 92.91%, sensitivity of 90.6, specificity of 96.45%, accuracy of 90.6%, and F-measure of 91.74% in COVID-19 detection. Indeed, the suggested MobileNetV2 deep-learning CNN model can improve classification performance by minimising the time required to collect per-image results for a mobile application.

Humans experience impact peaks due to the repetitive pressures placed beneath the heel during walking, and these peaks are marked by high rates and magnitudes of loading. Momentum is transferred from the ground to create impact peaks at the effective mass to the part of the body that stops moving at that point. The stiffness of the heel affects impacts generated by that momentum. This study aims to improve our understanding of how the body produces impact peaks and how the stiffness and features of the shoe heel affect parameters such as peak magnitude (Fmax) and impact loading rate (F\'). A shoe heel model is presented, and walkers wearing less stiff foot heels are expected to have less effective mass and vertical impulse. A human amputee adult male participated in evaluating the model by walking in 15 different heel designs. Minitab software, which applies response surface approach, was used to acquire the shoe’s design properties. The subject walked on a force plate while 3D kinematic data were collected. Design of Experiment was carried out using Minitab software to determine the optimal shoe heel design (depending on material and shape) that reduces the impacting effect. Statistical results show that heel height has a more significant effect (p=0.053) on impact loading rate than elastic modulus and cross-sectional area. According to the optimisation results based on the response surface design, wearing a heel with a modulus of elasticity of 0.5864 mPa, an area of 60.0 cm2 and a height of 0.50 cm may help improve the amputee\'s gait.

The integration of the Internet of Things (IoT) in healthcare has transformed the monitoring of physiological parameters, significantly enhancing the capabilities of medical diagnostics and patient care. This study explores the use of advanced cryptographic techniques to secure IoT healthcare systems, particularly through block chain (BC) technology. Moreover, this study evaluates the effectiveness of two cryptographic key generators—bee swarm key generator and snake key generator—in enhancing data security within a BC-based framework for remote patient monitoring. Our study conducts a series of NIST randomness tests to compare the performance of these key generators in terms of randomness, resilience to cryptographic attacks and computational efficiency. Results show that the snake key generator outperforms the bee swarm key generator, demonstrating higher randomness in key generation and better secure coverage of the orientation space. This finding is crucial for securing important healthcare data, especially given the complexity of IoT devices, which transmit vital patient health information. Furthermore, this study examines the double-layer encryption architecture used to secure data in at rest and during transmission from a sensor’s gathering location to long-term storage. This approach is essential to preserve the privacy and integrity of patient data, which is critical for maintaining trust in IoT healthcare systems. This study aims to provide a comprehensive and comparative analysis to support the continued evolution of secure, efficient and scalable cryptographic solutions within IoT healthcare, demonstrating the importance of selecting the most effective key generation technique to address the unique security challenges of this new field.

The energy business has grown explosively over the past few decades because of increased global energy consumption, which has had catastrophic effects on the environment. As a result, the entire planet has begun transitioning to green energy generation, which has zero negative effects on the natural world. Solar electricity has the highest efficiency amongst all forms of renewable energy. This study examines the monthly grid performance of a hypothetical 100 MWp solar facility linked to the Al-Khwarizmi College of Engineering system. Meteonorm 8.0 data are utilised in the simulation, which is run in PVsyst 7.2 and HelioScope software. Maximum energy production is the goal of the simulation, which is implemented at a constant tilt angle throughout the year. The building’s latitude is used to set the best angles or an east–west angle that is fixed 10 degrees to the south azimuth. The 3D display in HelioScope allows for a natural, insightful design process, enabling the selection of highly effective design options and the analysis of shading and panel orientation effects. The use of HelioScope and PVsyst software can provide insights into the amount of energy production from solar panels in a specific area. In this work, the amounts of energy generated using PVsyst and HelioScope are 1,500.303 and 849.7653 MWh/year, respectively.

The challenges of steering-by-wire (SBW) systems in vehicles are due to the absence of a direct mechanical link between the steering wheel and the wheels on the road. This limitation imposes the necessity of employing sophisticated control systems to attain the highest accuracy and stability during operation. In such systems, the responsibility rests completely on the utilised controller to change the wheel’s angle on the road swiftly and accurately in response to the steering wheel changes by the driver. However, conventional control systems suffer slowly in responding to instructions and some fixed errors in their steady-state phase. The current study introduces an innovation of a model that integrates model predictive control (MPC) with particle swarm optimisation (PSO) to improve the performance of SBW systems. The MPC procedure is typically employed to control system responses over a timeframe and eliminate unnecessary and ineffective actions according to the specified objectives. The PSO algorithm is used to manage the ineffective parameters within the MPC. Results revealed that the proposed approach remarkably and effectively shortens response time, enhances wagon stability and reduces the settling error to nearly null. In addition, the integration of PSO with the overall system performance enhances the tuning of the response time, hence augmenting the system efficiency and responsiveness. The study outcomes support the proposal that the control strategy can improve the efficiency of SBW systems with high operational goals.

In this work, manganese and niobium were added to high-chromium white cast iron, from which drilling and grinding tools were made. Two melts with different proportions of manganese and niobium (manganese 2.75% and niobium 0.49%, manganese 3.5% and niobium 1.1%) were used. High-chromium white cast iron is considered one of the most important alloys in heavy-duty applications, and its properties are affected by the added alloying elements and heat treatment. Niobium can increase corrosion resistance and hardness, form carbides, enhance thermal conductivity and improve response to heat treatment. As for manganese, it can increase impact toughness, remove oxygen from iron oxide and reduce flexibility. The heat treatment was carried out in accordance with two different programmes: 1- heating to 950 °C with a holding period of 2 h and cooling in atmospheric air; 2- heating to 960 °C with a holding period of 20 min, then cooling in the oven and returning to 425 °C for 3 h. The mechanical properties, such as wear resistance, impact durability and hardness, before and after heat treatments 1 and 2 were compared.

Multiple-input multiple-output (MIMO) antenna topologies in wireless communication systems can increase channel capacity and dependability. The use of millimeter (mm)-wave frequency bands in conjunction with other technologies is a substantial step in achieving high data rates for sophisticated AI and multimedia devices. However, transmitting mm wave frequency signals presents challenges such as fading, time delay, propagation and scattering loss. Therefore, optimising propagation parameters is critical to improving performance. This paper examines the influence models for path loss (PL): ABG and CI on the design and analysis of a 4×4 MIMO W-OFDM system employing 16-QAM modulation technique for 5G wireless communication. This paper examines the system using the CI and ABG PL models to assess PL in dB, normal distribution and standard deviation. This paper uses 16-QAM digital modulation, and the simulation results show that the CI PL model-based MIMO W-OFDM system is very robust and successful in signal recovery.