Fault detection of electric vehicle motor based on flux performance using FEM

This paper presents the early faults detection in electric vehicle motor based on flux performance examination in defective electrical machine using finite element methods (FEM). Depend on time step, the proposed technique has been designed and examine to produce efficient method under high accuracy and short time to detect the faults in Electric Vehicle motors. To decrease the probability and time of electric motor faults, the early detection of these faults will give enough time to prevent many problems during the motion. The different waveforms timing of motor torque in every situation associated with the waveforms of stator current provide spreading in the proposed method. The results show fast fault detections and a Novel technology was established to extort the fault of induction motor

Low power consumption and high thermal capability of electric car battery design

This paper presents the design of low power consumption and thermal capability of electric car battery. Different drive cycle, lawmaking, official real word and measurements have been study and investigated depend on their acceleration and velocity contents. The power consumption, acceleration performance, and power consumption of power train, comprise traction motors, batteries, and power electronic module were analyzed and determined for drive cycle. Additionally, the consequence on drive cycles fulfillments, acceleration performance, and power during scaling of electric drive system has study. Through the power train sizing regard power and torque, the requirements of acceleration turned out to dominate over requirement of top velocity, and grade level. The electrical powers trains down scaling resulting in energy consumptions downward to 80% of unique power train sizes. The little slot geometries have high speed loss through drive cycle and average cycle had low loss for many cycles. This results in amalgamation with high velocity torque lower material cost and very involved options as electric car traction motors

Characteristics and Motives of Suicide Attempt Survivors: A Case Study in Hebron Governorate

The study aimed to identify the characteristics of suicide attempt survivors and their motives from their viewpoint and from the professionals’ viewpoint in Hebron Governorate. To achieve this end, the study adopted the descriptive approach design using a 59-item questionnaire, which was appropriate to the exploratory nature of the research. The random purposive method was used which comprised of a sample size of 127 subjects. The findings showed that the participants experienced a high level of emotional and moral characteristics, whereas the economic, social and health characteristics were moderate. In light of the study results, the researchers report that there is a need to identify some of the characteristics and reasons that lead to suicide attempts and the most adopted methods used during the process of committing suicide

The Cognitive Style (Focusing-Scanning) among Al-Quds University Students

This study aims to explore the cognitive style (focusing - scanning) among Al-Quds University students. A descriptive approach was used to achieve this objective. The study population included the first-term student of Al-Quds University for the academic year (2019/2020). The sample of the research consisted of (262) students who were selected using the Stratified Random Method. The validity and reliability of the study instrument were assessed. The findings showed that there were differences due to the gender in favor of females towards focusing, and to the faculty variable in favor of the faculty of Science towards focusing, the findings also revealed differences attributed to (GPA) in favor of the average (70-80). Importantly, the findings found no differences were attributed to the educational level, and that confirming the need to pay more attention to develop the cognitive styles of focusing for all university students as they affect positively the level of learning and education

The Effect of Fertilization by Humic Acid and Foliar Spraying with Nano-Micro-Nutrients on the Productive Traits of Solanum melongena L.

A factorial experiment was carried out according to the design of the completely randomized sector (R.C.B.D.) in the greenhouse of one of the nurseries in Al-Khalis district, located in the north of Diyala governorate, about 55 km from the capital Baghdad, investigate the effect of humic acid, nano-zinc oxide and nano-iron oxide on vegetative growth characteristics for eggplant, the planting experiment was carried out in the autumn season, on 9/10/2021. The experiment included treating the eggplants with 50 kg. ha-1 of humic acid and 50 to 100 mg. 1 liter of zinc oxide nanoparticles ZnO and iron oxide nanoparticles Fe3O4. The study results showed significant differences at the probability level of 5% between the averages of all the studied traits due to treating plants with humic acid. The study\u27s results also showed that the highest averages were obtained from the treatment with zinc oxide nanoparticles at a concentration of 50 mg. L-1. Significant differences were also obtained in the length of the fruit (cm), the yield of a plot of fruits (kg. plot-1), and the total yield (kg. hectare-1) as a result of treating the eggplant plant with nano iron oxide at a concentration of 50 mg. L-1

Water Sanitation in Egypt: A Focus on the Sewage System in Rural Areas

Water supply and sanitation affects, among other things, people\u27s health, life conditions, life expectancy, infant mortality and overall well-being. Hence, it influences people’s ability to attain proper education as well as their chances for good employment, productivity, income, standard of living, and overall contribution to the country\u27s growth

Review of cloud computing in science, technology, and real life

This paper presents an overview of the general idea and history of cloud computing in theory. The objective of this review is to draw attention to preceding studies about cloud computing that have common characteristics with the theme of this paper. There were some points discussed in general, including the advantages of this technology, its subjects, security, and the effects of adopting cloud computing in an organization

Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters

Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters

An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations

Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions