In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.
The study aims to investigate the effect of Al2O3 and Al additions to Nickel-base superalloys as a coating layer on oxidation resistance, and structural behavior of nickel superalloys such as IN 738 LC. Nickel-base superalloys are popular as base materials for hot components in industrial gas turbines such as blades due to their superior mechanical performance and high-temperature oxidation resistance, but the combustion gases' existence generates hot oxidation at high temperatures for long durations of time, resulting in corrosion of turbine blades which lead to massive economic losses. Turbine blades used in Iraqi electrical gas power stations require costly maintenance using traditional processes regularly. These blades are made
... Show MoreBackground: Candida albicans is a prevalent commensal that can cause severe health problems in humans. One such condition that frequently returns after treatment is oral candidiasis. Aim: the goal of this research is to evaluate the efficiency of 940 nm as a fungicidal on the growth of Candida albicans in vitro. Material and Methods: In vitro samples (fungal swabs) were taken from the oral cavity of 75 patients suffering from oral thrush. Following the process of isolating and identifying Albicans. The samples are divided into four groups:(Group 1): Suspension of C. albicans was put in a solution of saline as a control group. (Group 2): Suspension of C. albicans that had been treated wit
... Show MoreThe 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
... Show MoreThe 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
... Show MoreThe prepared nanostructure SiO2 thin films were densified by two techniques (conventional and Diode Pumped Solid State Laser (DPSS) (532 nm). X-ray diffraction (XRD), Field Emission Scanning electron microscopy (FESEM), and Atomic Force Microscope (AFM) technique were used to analyze the samples. XRD results showed that the structure of SiO2 thin films was amorphous for both Oven and Laser densification. FESEM and AFM images revealed that the shape of nano silica is spherical and the particle size is in nano range. The small particle size of SiO2 thin film densified by DPSS Laser was (26 nm) , while the smallest particle size of SiO2 thin film densified by Oven was (111 nm).