A time series analysis can help to observe the behavior of the system and specify the system faults. In addition, it also helps to explain the various energy flows in the system and further aid in reducing the thermodynamic losses. The intelligent supervisory LabVIEW software can monitor the incoming data from the system by using Arduino microcontroller and calculates the important parameters. Energy, exergy, and anergy analysis present in this paper to investigate the system performance as well as its components. To accomplish this, a 4-ton vertical split air conditioner based on vapor compression refrigeration cycle charged with refrigerant R-22 was modified for experimental analysis. The results showed that during 540

By optimizing the efficiency of a modular simulation model of the PV module structure by genetic algorithm, under several weather conditions, as a portion of recognizing the ideal plan of a Near Zero Energy Household (NZEH), an ideal life cycle cost can be performed. The optimum design from combinations of NZEH-variable designs, are construction positioning, window-to-wall proportion, and glazing categories, which will help maximize the energy created by photovoltaic panels. Comprehensive simulation technique and modeling are utilized in the solar module I-V and for P-V output power. Both of them are constructed on the famous five-parameter model.  In addition, the efficiency of the PV panel is established by the genetic algorithm

Flat-plate collector considers most common types of collectors, for ease of manufacturing and low price compared with other collectors. The main aim of the present work is to increase the efficiency of the collector, which can be achieved by improving the heat transfer and minimize heat loss experimentally. Five types of solar air collectors have been tested, which conventional channel with a smooth absorber plate (model I), dual channel with a smooth absorber plate (model II), dual channel with perforating “V” corrugated absorber plate (model III), dual channel with internal attached wire mesh (model Ⅳ), and dual channel with absorber sheet of transparent honeycomb, (model Ⅴ). The dual channel collector used for

The Boltzmann transport equation is solved by using two- terms approximation for pure gases . This method of solution is used to calculate the electron energy distribution function and electric transport parameters were evaluated in the range of E/N varying from . 172152110./510.VcmENVcm
From the results we can conclude that the electron energy distribution function of CF4 gas is nearly Maxwellian at (1,2)Td, and when E/N increase the distribution function is non Maxwellian. Behavior of electrons transport parameters is nearly from the experimental results in references. The drift velocity of electron in carbon tetraflouride is large compared with other gases

Background: White spot lesion considered as irreversible tooth demineralization presenting challenge to orthodontists during treatment schedules, fluoride was the most successfully used measure to overcome this challenge. Materials and method: A total of forty sound human permanent premolars were used in the present study and categorized into four groups, in one group the teeth were bonded with stainless steel brackets using Resin-modified glass ionomer cement (RMGIC) and the other three groups the teeth were bonded with light cured composite Resilience® (Ortho technology Co., USA). Group A; Acidulated phosphate fluoride (APF) topical gel (Mfg by DEEPAK PRODUCTS, INC, USA), fluoride ion 1.23% applied on examine area for four minute. Gro

Abstract: This study aims to investigate the backscattering electron coefficient for SixGe1-x/Si heterostructure sample as a function of primary electron beam energy (0.25-20 keV) and Ge concentration in the alloy. The results obtained have several characteristics that are as follows: the first one is that the intensity of the backscattered signal above the alloy is mainly related to the average atomic number of the SixGe1-x alloy. The second feature is that the backscattering electron coefficient line scan shows a constant value above each layer at low primary electron energies below 5 keV. However, at 5 keV and above, a peak and a dip appeared on the line scan above Si-Ge alloy and Si, respectively, close to the interfacing line

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