Journal of Techniques

Improving reliability and power quality in Radial Distribution Systems (RDS) is of large significance to ensure the provision of electricity within a reliable and acceptable standard to consumers with increasing load requirements. An Optimal Capacitor Placement (OCP) technique is used in the present work to achieve the highest power quality and system reliability in a balanced manner at the same time. The proposed technique has been tested with 69 typical IEEE RDS buses using the Improved Binary Particle Swarm Optimization (IBPSO) algorithm. The proposed algorithm shows a high ability to find the best location and size of injected capacitors inside the RDS to implement a single-objective function for minimization of Active Power Loss (APL). The simulation results obtained from the MATLAB environment show that the OCP technique has a significant potential to enhance RDS reliability, bus voltage, and loss reduction as compared to other previous work

Epileptic seizures can cause sudden blood pressure changes, requiring continuous monitoring. However, traditional blood pressure monitoring methods are often invasive and uncomfortable for the patient. In addition, it is difficult to measure blood pressure during seizures. This research aims to design a non-invasive, comfortable device to monitor blood pressure during epileptic seizures continuously. Photoplethysmography (PPG) signals from the sole of the patient's foot were used to extract blood pressure data. A smart patch was designed to be worn comfortably on foot for continuous monitoring during seizures. The results show that the average systolic and diastolic blood pressure errors were 2.838 and 4.494 mmHg during epileptic seizures, respectively. These blood pressure changes could be related to the onset of seizures, suggesting that the device and methodology could be combined with other measures to analyze and predict seizure activity. This research offers a non-invasive and comfortable solution for continuous blood pressure monitoring during seizures, which may affect seizure prediction and management.

In the present study, the effect of irregularity of structures of the concrete with special shear walls has been analyzed and modeled by considering the effect of soil-structure interaction by dynamic analysis method with ABAQUS software. The results are presented in the form of diagrams with different soil interaction conditions, the effect of the number of layers was investigated for different soils. The results showed that the bearing system of the flexural frame and the concrete shear wall under the lateral load of the earthquake have good strength and ductility, which makes this issue. This structural system should be used for cities with very high earthquake risk. As the number of floors increases, the effect of the shear walls on increasing the bearing capacity decreases. As the number of floors increases, the final displacement of the frame increases. The results showed that for soil type 1(hard soil which Vs=1000 m/s, v=0.15, q=50000 Kg/m2, P=2000 Kg/m3, r=0.5 m) structural system of concrete shear walls with better displacement, maximum structural stiffness, better behavior was observed in the structure. Three-story frame with concrete shear wall has a significant increase compared to similar frames with more floors, which shows the good performance of this system in structures with fewer floors

Facial diseases lead to noticeable changes on the human face, and some of them extend beyond internal effects or organ-based disorders. Indeed, certain types of facial diseases result in visually noticeable abnormalities on the human face. These alterations in facial patterns can serve as potential indicators for corresponding diseases, particularly in the fields of endocrinology and metabolism, Muscles-Nervous disorders, Chromosomes, and Genetic disorders, among others. Technologies used in Face Recognition (FR) have been developed over the past few decades; however, only a limited amount of research has been applied in recent years to FR-based disease detection for clinical purposes. FR applications relying on Artificial Neural Network (ANN) techniques have recorded higher accuracy rates in diagnosing facial diseases. This field of recognition holds promising potential for optimizing facial diagnosis approaches and supporting medical staff in evaluating detections. In practice, only a few research ideas have been translated into medical products, emphasizing the need to identify and integrate future applications. As a primary focus, this paper centers on the key applications and technologies for detecting various types of facial diseases, along with a discussion of prospects.

Resistance spot welding (RSW) was used to fabricate Similar joints of low-carbon steel AISI 1005 in lap joint design RSW. Evaluate the welding current, squeeze time, welding time, and hold time, which affects resistance spot welding. A tensile-shear force fracture test was used to assess the mechanical properties Using a universal tensile testing machine. The microstructure of the fusion zone was examined using a scanning electron microscope (SEM). According to the data, the shear strength values increased as the welding current increased. Additionally, it was shown that most failures occurred closer to the fusion zone (FZ). The primary cause is the crystal lattice's strained distortion, which weakens the nearby region of the fusion zone.

In this paper, the literature will be reviewed, and various research works conducted to improve the thermal performance of flat panel solar collectors will be summarized. It will be summarized in more than one way. Firstly, by design using different methodologies and methods to improve the efficiency and the thermal performance of the solar collector by introducing twisted strips that cause increased mixing. Fluids and Friction for FPSCs. To increase heat transfer as well as use porous materials to enhance heat and improve the effectiveness of absorption panels to absorb as much solar radiation as possible as well as thermal insulation methods to reduce losses to surrounding areas. And improve the permeability of the glass cover. Secondly, the use of nanofluids enhances the performance of flat solar collectors instead of the core fluid, and its effect is to improve the thermophysical properties such as thermal conductivity by summarizing previous research using mono nanofluids and hybrid nanofluids. Through studies and research related to the use of mono nanofluids, it was noted that the best nanofluids are those that use CuO and Al₂O₃ particles due to their ease of availability and high thermal conductivity. As for hybrid nanofluids, the best fluids are (CuO + Al₂O₃/water) for the same reason above. As a result of design improvements and the use of nanofluids, temperatures up to (75C°) were obtained.

In this work, an Artificial Neural Network (ANN) with a backward-propagation technique was used to predict the power generation of the Photovoltaic (PV) module in weather conditions of Baghdad city-Iraq. Experiment tests were investigated in the summer of 2022. Three weather parameters, including: (solar radiation, ambient temperature, and wind speed), the output electrical characteristics of the PV module (voltage, current, power), and module temperature (were measured. Therefore, the dataset of the ANN system consists of four input and one output parameter. Furthermore, the structure of ANN includes a single hidden layer with a backward propagation technique. The main goal of this study was to optimize the number of neurons in the training process. The evaluation of the ANN model depended on the determination coefficient (R) and Root Mean Squared Error (RMSE). The obtained results show that the architecture of ANN is appropriate for predicting the power generated from the PV module. The developed ANN model has good accuracy. Where the MSE is 0.002747 at epoch 9 in the model. Furthermore, the R is recorded as 0.99078, 0.98254, 0.99125, and 0.99005 for training, testing, validation, and all respectively in the proposed model. In addition, the optimization number of neurons in the hidden layer gave sufficient accuracy without referring to the choice of the number of neurons by using the trial-and-error method that most researchers relied.

The service sector, including the sanitation service, currently occupies a prominent part of the economic and social development plans. The scale of development of this sector has become a criterion for measuring the progress and development in various countries of the world. Infrastructure services management has been introduced in many countries of the world including some of the leading experts in this field to ensure the sustainable development of the sanitation sector. The aim of this study is to systematically review the literature on sanitation to provide an overview of how researchers approach problem solving to achieve sustainability in sanitation services, it was also found that recent studies tend to use geographic information systems (GIS) to analyze sanitation services. Studies using GIS technology have provided maps that can be used as a decision support tool for governmental and non-governmental organizations to prioritize sanitation in underserved areas.

Dyslexia is a common learning disorder that affects children’s reading and writing skills. Early identification of Dyslexia is essential for providing appropriate interventions and support to affected children. Traditional methods of diagnosing Dyslexia often rely on subjective assessments and the expertise of specialists, leading to delays and potential inaccuracies in diagnosis. This study proposes a novel approach for diagnosing dyslexic children using spectrogram analysis and convolutional neural networks (CNNs). Spectrograms are visual representations of audio signals that provide detailed frequency and intensity information. CNNs are powerful deep-learning models capable of extracting complex patterns from data. In this research, raw audio signals from dyslexic and non-dyslexic children are transformed into spectrogram images. These images are then used as input for a CNN model trained on a large dataset of dyslexic and non-dyslexic samples. The CNN learns to automatically extract discriminative features from the spectrogram images and classify them into dyslexic and non-dyslexic categories. This study’s results demonstrate the proposed approach’s effectiveness in diagnosing dyslexic children. The CNN accurately identified dyslexic individuals based on the spectrogram features, outperforming traditional diagnostic methods. Spectrograms and CNNs provide a more objective and efficient approach to dyslexia diagnosis, enabling earlier intervention and support for affected children. This research contributes to the field of dyslexia diagnosis by harnessing the power of machine learning and audio analysis techniques. Facilitating faster and more accurate identification of Dyslexia in children, ultimately improving their educational outcomes and quality of life

To develop novel optoelectronic devices, controlling and predetermined absorption is necessary. In the current work, the gold nano-islands were sputtered onto quartz surface substrates using a DC sputtering unit with an optimized chamber. The effects of the sputtering time (10, 15, and 20 seconds) on the characteristics of the golden layers deposited on quartz substrates were studied. Au nanofilms were investigated by XRD (X-ray diffraction), UV-Vis (ultraviolet-visible light) diffractometer, and AFM (atomic force microscopy) techniques. The thicknesses of the three films prepared at different sputtering times (10, 15, and 20 seconds) were calculated using the theoretical deposition formula method. The average layer thickness, and hence the size of the golden crystallites, rose from 6.8 to 13.6 nm when the sputtering period was increased. The X-ray spectra revealed a very distinguish peak at (111), pointing that the gold single crystal has fully oriented along [111] and the gold film has a pure crystalline Fcc structure. When deposition times are lengthened, the color of the resulting films changes from blue to green. Nano-films have seen important changes in their surface shape and roughness. The structural layer of Au's surface is remarkably semi-spherical, giving it the appearance of spherolytic and hummock-like. Analyzing the UV-VIS spectra of the precipitated structures using Tauc's paradigm revealed the optical energy gap (non-zero Eg in the range of 2.36 to 2.38 eV) that is associated with the nanostructure's semiconducting properties. In addition, as the sputtering duration increases from 10 to 20 seconds, the wavelengths at which peak values of the surface plasmon resonance occur shift from 610 to 650 nm, and the widths of the peaks rise. The AFM images of the ultra-thin Au layers showed smooth surfaces whose roughness decreases from 1.82 to 0.673 nm with increasing sputtering time from 10 to 20 nm. Solar cells can take advantage of the higher absorption in the blue spectrum region by using a set of depositing parameters and prescribed thicknesses. In addition, the formation of nano-islands can also be used as nucleation sites (seed layer) to promote the growth of various nanostructures to obtain a good aspect ratio and improve the performance of various optoelectronic devices and gas sensors.