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Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases.

Introduction: Wilms tumor is the most common renal malignancy in children. The occurrence of Wilms tumor with various congenital genitourinary anomalies has been reported, particularly in horseshoe kidneys, hypospadias, disorder of sexual development, and double collecting system. However, Wilms tumor with crossed renal ectopia is a rare finding. Case Description: We are reporting a case report of Wilms tumor in a 3-year-old girl who presented with a huge left flank mass with cross-fused renal ectopia. After the initial workup and triphasic computed tomography (CT) scan of the chest, abdomen, and pelvis for confirmation of diagnosis and metastasis, the patient underwent image-guided tissue biopsy followed by neo-adjuvant chemotherapy, left radical nephrectomy with separation of fused right ectopic renal moiety and adjuvant chemoradiation. Practical Implication: This report shows an association of Wilms tumor with cross-fused renal ectopia, a rare combination. Unusual findings on imaging with unilateral flank mass should be considered as part of the differential diagnosis for this rare finding. Adjuvant chemotherapy and modern imaging helped delineate the anatomy and ease the surgery for safe resection, improving the overall outcome.

This article addresses the objective, experimental design and methodology of the tests conducted for battery State of Health (SOH) estimation using an accelerated test method. For this purpose, 25 unused cylindrical cells were aged, by continual electrical cycling using a 0.5C charge and 1C discharge to 5 different SOH breakpoints (80, 85, 90, 95 and 100%). Ageing of the cells to the different SOH values was undertaken at a temperature of 25 °C. A reference performance test (RPT) of C/3 charge-discharge at 25 °C was performed when the cells were new and at each stage of cycling to define the energy capacity reduction due to increased charge-throughput. An electrochemical impedance spectroscopy (EIS) test was performed at 5, 20, 50, 70 and 95% states of charge (SOC) for each cell at temperatures of 15, 25 and 35 °C. The shared data includes the raw data files for the reference test and the measured energy capacity and the measured SOH for each cell. It contains the 360 EIS data files and a file which tabulates the key features of the EIS plot for each test case. The reported data has been used to train a machine-learning model for the rapid estimation of battery SOH discussed in the manuscript co-submitted (MF Niri et al., 2022). The reported data can be used for the creation and validation of battery performance and ageing models to underpin different application studies and the design of control algorithms to be employed in battery management systems (BMS).

أبو السعود العمادي من مفسري القرن العاشر الهجري وصاحب تفسير «إرشاد العقل السليم إلى مزايا الكتاب الكريم» المعروف بـ «تفسیر أبي السعود»، هو کعديدٍ من المفسرین الآخرین اقتبس لتفسیر القرآن، من العلوم المختلفة كالبلاغة، والصرف، والنحو، واللغة، والاشتقاق، والقراءة، والشعر العربي بأسلوب استنباطي . هذا البحث يسعى بطريقة قراءة النص المبنية على الوصف والتحليل أن يدرس ويستقصي أساليب بيان المفسّر وطريقة استدلاله واستنباطه في التفسير المذكور وليعثر لهذا السؤال على إجابة، وهو ما هي مكانة الأدب العربي في تفسير أبي السعود. نتائج الاستطلاع والتحقيق تظهر لنا بأن المفسر كان يستفيد من العلوم الأدبية المختلفة سواء كان من اللغة، والصرف، والنحو، والمعاني، والبيان، والشعر العربي، والحديث، والكلام، ولقد وظّفها المفسر كثيرا في تفسير آيات الله تعالى وبيانها؛ لكن من حيث أن جُلَّ عناية المفسر واهتمامه كان مركزا على الأدب العربي فيعرف تفسيره بتفسير أدبيّ واجتهاديّ.

Transport electrification is a key enabler to reduce fossil fuel depletion and related carbon dioxide emissions. However, critical barriers exist in terms of battery costs and their expected life. Vehicle-to-grid technology can bring benefits to both the electrical power grid and electric vehicle owners, while its practical implementation faces challenges due to the concerns over accelerated battery degradation. This paper presents a comprehensive study on reduced Lithium-ion battery degradation through state-of-charge pre-conditioning strategies that allow an electric vehicle to participate in vehicle-to-grid operations during periods in which the vehicle is parked. Energy capacity reduction of the electric vehicle battery are predicted using semi-empirical ageing models, which have been built and validated to capture the degradation behaviours of the battery with respect to both calendar and cycling ageing. Five charging strategies for battery state-of-charge pre-conditioning have been developed to evaluate the ability to mitigate battery ageing before commencing vehicle-to-grid operations. Simulation studies on battery degradation utilizing such charging mechanisms under two different operational profiles have been undertaken. The analytical results show that the proposed charging strategies do not accelerate battery degradation and are capable of mitigating the total ageing process from 7.3 – 26.7% for the first 100 days of operational life and gradually vary to 8.6 – 12.3% for one-year continual operation compared to the reference standard charging approach.

Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions of disabled people due to their high costs, technical limitations, and safety issues. This paper proposes a gesture-controlled smart wheelchair system with an IoT-enabled fall detection mechanism to overcome these problems. It can recognize gestures using Convolutional Neural Network (CNN) model along with computer vision algorithms and can control the wheelchair automatically by utilizing these gestures. It maintains the safety of the users by performing fall detection with IoT-based emergency messaging systems. The development cost of the overall system is cheap and is lesser than USD 300. Hence, it is expected that the proposed smart wheelchair should be affordable, safe, and helpful to physically disordered people in their independent mobility.

Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In this paper, an automated detection scheme named EMCNet was proposed to identify COVID-19 patients by evaluating chest X-ray images. A convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19. With the extracted features, binary machine learning classifiers (random forest, support vector machine, decision tree, and AdaBoost) were developed for the detection of COVID-19. Finally, these classifiers’ outputs were combined to develop an ensemble of classifiers, which ensures better results for the dataset of various sizes and resolutions. In comparison with other recent deep learning-based systems, EMCNet showed better performance with 98.91% accuracy, 100% precision, 97.82% recall, and 98.89% F1-score. The system could maintain its great importance on the automatic detection of COVID-19 through instant detection and low false negative rate.

Coronavirus (COVID-19) has been one of the most dangerous and acute deadly diseases across the world recently. Researchers are trying to develop automated and feasible COVID-19 detection systems with the help of deep neural networks, machine learning techniques, etc. In this paper, a deep learning-based COVID-19 detection system called COV-VGX is proposed that contributes to detecting coronavirus disease automatically using chest X-ray images. The system introduces two types of classifiers, namely, a multiclass classifier that automatically predicts coronavirus, pneumonia, and normal classes and a binary classifier that predicts coronavirus and pneumonia classes. Using transfer learning, a deep CNN model is proposed to extract distinct and high-level features from X-ray images in collaboration with the pretrained model VGG-16. Despite the limitation of the COVID-19 dataset, the model is evaluated with sufficient COVID-19 images. Extensive experiments for multiclass classifier have achieved 98.91% accuracy, 97.31% precision, 99.50% recall, 98.39% F1-score, while 99.37% accuracy, 98.76% precision, 100% recall, 99.38% F1-score for binary classifier. The proposed system can contribute a lot in diagnosing COVID-19 effectively in the medical field.

ABSTRACT: Background: Visual impairments have become one of the most predominant problems for the last few decades. To keep doing their daily tasks, vision-impaired people usually seek help from others. An automated common object and currency recognition system can improve the safe movement and transaction activity of visually impaired people. Objective: To develop a system that can identify indoor and outdoor objects, notify the users, and send all information to a remote server repeatedly at a fixed time interval. Methods: The proposed system assists the visually impaired to recognize several objects and provides an audio message to aware the user. Four laser sensors are used in the system to detect the objects in the direction of the front, left, right and ground. The proposed system uses Single Shot Detector (SSD) model with MobileNet and Tensorflow-lite to recognize objects along with the currency note in the real-time scenario in both indoor and outdoor environments. Results: Among 375 participants, 82% reacted that the price of the proposed system is reasonable, 13% treated as the cost is moderate and the rest 5% people responded that the cost is relatively high for them. In terms of size and weight, 73% reacted that the size and weight are considerable, 20% treated that the size is not suitable, and weight needs to lessen, and the rest 7% people responded that the system is bulky. Regarding input signal observation, 98% responded that they have heard the sound appropriately and the remaining 2% of individuals missed hearing the signal. Conclusions: This paper represents an IoT-enabled automated object recognition system that simplifies the mobility problems of the visually impaired in indoor and outdoor environments. The overall accuracy of the proposed system in object detection and recognition is 99.31% and 98.43% respectively. In addition, the proposed system sends all processed data to a remote server through IoT.

This paper proposes using engine’s sensors data flow and exhaust emissions information to diagnose engine’s faults, enhancing the accuracy of fault diagnosis. Engine fault diagnosis model is built using both this information and the mature BP neural network and genetic algorithms. In order to verify the method, we build a test platform, which includes South Korea Hyundai fault test vehicle and X-431 diagnosis instrument and AUTO5-1 exhaust gas analyzer and computer. The diagnostic accuracy rate can reach 98.33%, which is higher than using sensors data flow or the exhaust emissions information alone.

The aim of this study was to examine if perceived family violence of victimized children is related to their perceived aggressive behavior. It has been acknowledged that children learn and behave what they observe and practice including violence. A stratified random sample (N =1160) of Omani school students was drawn from grades 6 to 9. The study used perceived family violence and perceived aggressive behavior measures to collect data. CFA was performed to test the proposed factor structure as well as the structural model. The invariance test lent support to the hypothesis that the structure of constructs is invariant across gender. However, the relations between constructs were not invariant. Children (boys and girls) who expressed high verbal violence on them reported they were more verbally and physically aggressive. Boys, but not girls, who reported high physical violence on them reported they were more verbally and physically aggressive. The relation between perceived family violence and perceived aggressive behavior seem to be dependent on gender and types of family violence as well as the kind of children's aggressive behavior.

Techniques for the cavity measurement of the electrical characteristics of the materials are well established using the approximate method due to its simplicity in material insertion and fabrication. However, the exact method which requires more comprehensive mathematical analysis as well, owing to the practical difficulties for the material insertion, is not mostly used while performing the measurements as compared to approximate method in most of the works. In this work the comparative analysis of both the approximate as well as Exact method is performed and accuracy of the Exact method is established by performing the measurements of non-magnetic material Teflon within the cavity.

Correlation and path coefficient analysis among ten agro-morphometric characters of five maize inbred lines (SARA NA, NA 225, NADH 704, NADH 706, and ZM 12) and their ten crosses grown during autumn season of 2021 were conducted in Qlyasan and Kanipanka locations in Sulaimani Governorate, Iraqi Kurdistan Region, Iraq. Correlation analysis revealed that kernel yield showed a highly significant positive association with number of ears.plant-1,number of rows.ear-1,number of kernels.row-1,weight of kernels.row-1, 300 kernels weight, harvest index, and biological yield, while it exhibited a nonsignificant negative correlation with days to 50% tasseling and silking in both locations. Path analysis revealed a maximum positive direct effect of the biological yield of 0.735, and 0.566 on kernel yield in both locations respectively, followed by harvest index of 0.394 and 0.431 for both locations respectively. The maximum indirect effect was 0.528 recorded by biological yield via number of rows.ear-1 in Qlyasan location, while in Kanipanka location maximum indirect effect was 0.456 recorded by biological yield via number of ears.plant-1. This indicated that the selection based on these characters would be more beneficial. [ABSTRACT FROM AUTHOR]

Digital divide is a reality in developing nations. Most of the technological advancements are available only in urban areas and rural community is still deprived of communication technology even in 21 st century. To ensure the availability of Internet, TV (Television) and other high data-rate services to the rural community; use of high power ISM (Industrial, Scientific and Medical) band broadcast should be of interest. The aim of this research work is to design a WiFi based broadcast network that provides broadband access to remote areas and to study the propagation characteristics of this network in a typical rural community in the plains of Pakistan. This paper uses extensive measurements in indoor and outdoor environments of village ?Lower Kot Ratta? to develop a WiFi broadband broadcast propagation model for rural areas of Pakistan. The proposed model is simple, flexible and more suitable for rural areas as compared to existing models