The efficient behavior of a low-concentrating photovoltaic-thermal system with a micro-jet channel (LCPV/T-JET) and booster mirror reflector is experimentally evaluated here. Micro-jets promote the thermal management of PV solar cells by implementing jet water as active cooling, which is still in the early stages of development. The booster mirror reflector concentrates solar irradiance into solar cells and improves the thermal, electrical, and combined efficiencies of the LCPV/T-JET system. The LCPV/T-JET system was tested under ambient weather conditions in the city of Bangi, Selangor, Malaysia, and all data was recorded between 10:00 a.m. and 4:00 p.m. Parametric studies were conducted to compare the performance of the LCPV/T-JET system

The energy aimed at examining the mode of energy drinks consumption among athletes in
Baghdad and assessing their drinks were spread greatly among the athletes and students. This
study impression toward such drinks. The study sample comprised of 102 mal athletes aged
between 19-27 years and selected randomly .The obtained results showed that football was
most practiced among the test samples at 40.54% based on twice daily .The athletes
consumed one can each day at 41.18% .As the data on energy drinks was supplied from
friends .The prefared period for drinking was before or during exercise .The athletes thought
that there products can provide energy ,vitamins ,tell ale materials ,does not affect
appetite.The most f

The Plerion nebula is characterized by its pulsar that fills the center of the supernova remnant with radio and X-ray frequencies. In our galaxy there are nine naked plerionic systems known, of which the Crab Nebula is the best-known example. It has been studied this instance in order to investigate how the pulsar energy affect on the distribution and evolution of the remnant as well as study the pulsar kick velocity and its influence on the remnant. From the obtained results it's found that, the pulsar of the Crab Nebula injects about (2−3)𝑥 1047 erg of energy to the remnant, although this energy is small compared to the supernova explosion energy which is about 1051 erg but still plays a significant role in the distribution and the m

A condense study was done to compare between the ordinary estimators. In particular the maximum likelihood estimator and the robust estimator, to estimate the parameters of the mixed model of order one, namely ARMA(1,1) model.

Simulation study was done for a varieties the model.  using: small, moderate and large sample sizes, were some new results were obtained. MAPE was used as a statistical criterion for comparison.

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par