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Brushless DC (BLDC) motors have gained popularity because of numerous advantages. This motor application is achievable Since permanent magnet (PM) technology has advanced. This motor has higher efficiency, reliability, and power density and requires less maintenance due to the absence of brushes. A higher torque-to-mass ratio and long life are two of this motor s most attractive characteristics, making it appropriate for highperformance applications. This study describes the hybrid methodology for analyzing a three-phase, four-pole, 1500 W, brushless DC (BLDC) motor with an inner rotor. This paper uses PI controllers and PWM in Matlab /Simulink and the chopped current control in Maxwell 2D. BLDC motor modeled by rotation machine export (RMxprt) analytical software and analyzed with a finite element method (FEM) Maxwell 2D software to calculate motor performance, such as torque, speed, and efficiency. The FEM results were verified by comparing them with the results taken from the Matlab/Simulink program and with the aid of the RMxprt software to supply some missing data. The results from Maxwell 2D and Matlab show good agreement. Despite lacking motor test data, this paper successfully applied a hybrid FEM-analytical approach to study a BLDC motor using three software packages RMxprt, Maxwell 2D, and Matlab/Simulink. The RMxprt program offers missing data like stator resistance, stator inductance, and torque constant. It is provided in the design sheet to help us with motor modeling. The successful adoption of the proposed hybrid FEM-analytical methodology will provide a good starting point for future BLDC motor research work. [ABSTRACT FROM AUTHOR]

In this research, poly terephthalic acid-co-glycerol-g-maleic anhydride (PTGM) graft co-polymer was used as novel water-soluble pore formers for polyethersulfone (PES) membrane modification. The modified PES membranes were characterized to monitor the effect of PTGM content on their pure water flux, hydrophilicity, porosity, morphological structure, composition, and performance. PTGM and PES/PTGM membranes were characterized by field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy (FTIR), and contact angle (CA). The results revealed that the porosity and hydrophilicity of the fabricated membrane formed using a 5 wt.% PTGM ratio exhibited an enhancement of 20% and 18%, respectively. Similarly, upon raising the PTGM ratio in the casting solution, a more porous with longer finger-like structure was observed. However, at optimum PTGM content (i.e., 5%), apparent enhancements in the water flux, bovine serum albumin (BSA), and sodium alginate (SA) retention were noticed by values of 203 L/m2.h (LMH), 94, and 96%, respectively. These results illustrated that the observed separation and permeation trend of the PES/PTGM membrane may be a suitable option for applications of wastewater treatment. The experimental results suggest the promising potential of PTGM as a pore former on the membrane properties and performance.

Mobile applications and devices have played a significant role in boosting global businesses that encompass various domains such as health, education, banking, and transportation. These tools have become indispensable for everyday activities, and its applications have been developing rapidly with diverse features and platforms. However, this has created new problems and security challenges. To ensure the quality and security of these applications, a rigorous and systematic testing using cloud-based environment is required. By employing systematic mapping study (SMS) method, this paper will examine the empirical studies that address the issues on cloud-based mobile application testing. This paper presents a total of 23 primary studies that investigate cloud based mobile application testing and the effect of Testing as a Service (TaaS). The majority of these studies (56.5%) contribute to literature with a number of framework proposals. A large proportion of the studies (60.9%) analyzed Android applications, and usually supported a single type of mobile app testing. Other than that, the majority of the studies (52.2%) have failed to investigate the outcomes of TaaS, despite a plethora of services that offers TaaS. The SMS method conducted in this paper has identified gaps in literature, which are: 1) there is a lack of general and scalable approaches to support the diverse types of mobile app testing for applications using various platforms, and 2) the lack of evaluation methods such as case study to validate the proposed approaches.

Two Rapid, direct, ecological friendly and economical spectrophotometric methods were used for estimation of meloxicam in the market Tablet dosage form. The First method is based on the use the fourth order derivative spectrum (D4) and the second method is depend-ed on the ratio of the area under the curve for the two peaks in the drug (Compensated area under the curve). The linear calibration graphs of the two methods were measured in the con-centration range (5-35)mg/l and the average of recoveries for local market Tablet (AWA)® were 99.8% for D4 method and 100.2% for CAUC method which indicating a good accuracy and precision for these methods. In this study, the results obtained by these suggested meth-ods have been successfully statistically compared by t-test and Mann-Whitney test showed a good agreement.

Context The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns. Objective This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG. Methods Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers. Results After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles). Discussion This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques. Conclusions This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.

The development of computer systems for representation, storage and processing the knowledge related to a domain or specific field is a strong requirement nowadays, mainly for medical diagnosis and computer assisted therapy. The management of imprecision measurements, paradoxical data, and unconventional inference techniques is possible using the neutrosophic paradigm. This paper describes the neutrosophic algebraic numbers and illustrates their usage in developing knowledge representation and processing systems. Finally, some ideas on medical diagnosis systems are presented.

Mycobacterium marinum is an opportunistic pathogen inducing infection in fresh and marine water fish. This pathogen causes necrotizing granuloma like tuberculosis, morbidity and mortality in fish. The cell wall-associated lipid phthiocerol dimycocerosates, phenolic glycolipids and ESAT-6 secretion system 1 (ESX-1) are the conserved virulence determinant of the organism. Human infections with Mycobacterium marinum hypothetically are classified into four clinical categories (type I–type IV) and have been associated with the exposure of damaged skin to polluted water from fish pools or contacting objects contaminated with infected fish. Fish mycobacteriosis is clinically manifested and characterized in man by purple painless nodules, liable to develop into superficial crusting ulceration with scar formation. Early laboratory diagnosis of M. marinum including histopathology, culture and PCR is essential and critical as the clinical response to antibiotics requires months to be attained. The pathogenicity and virulence determinants of M. marinum need to be thoroughly and comprehensively investigated and understood. In spite of accumulating information on this pathogen, the different relevant data should be compared, connected and globally compiled. This article is reviewing the epidemiology, virulence factors, diagnosis and disease management in fish while casting light on the potential associated public health hazards.

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.