Radiation measuring devices need to periodic calibration process to examine their sensitivity and the extent of the response. This study is used to evaluate the radiation doses of the workers in the laboratories of the Directorate of Safety as a result of the use of point sources in calibrating of the devices in two ways, the first is the direct measurement by the FAG device and the others using RESRAD and RAD PRO programs. The total doses values using FAG were (2.57 μSv/y, 102.3 μSv/y and 20.75 μSv/y for TLD laboratory, Gamma spectroscopy analyses (GSA) laboratory and equipment store respectively, and the total doses that calculated using RESRAD and RAD PRO were 1.518 μSv/y, 76.65 μSv/y and 21.2 μSv/y for the above laboratories. t

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

In this work, a novel design for the NiO/TiO2 heterojunction solar cells is presented. Highly-pure nanopowders prepared by dc reactive magnetron sputtering technique were used to form the heterojunctions. The electrical characteristics of the proposed design were compared to those of a conventional thin film heterojunction design prepared by the same technique. A higher efficiency of 300% was achieved by the proposed design. This attempt can be considered as the first to fabricate solar cells from highly-pure nanopowders of two different semiconductors.

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

We report on using a CO₂ (10.6 µm) laser to debond the lithium disilicate veneers. Sixty-four sound human premolar teeth and 64 veneer specimens were used in the study. The zigzag movement via CO₂ laser handpiece along with an air-cooled jet to prevent temperature elevation above the necrosis temperature limit (5.5 C°) was applied. The optimal deboning irradiation time was super-fast, at about 5 seconds at 3 Watt CO₂ laser power. It is 20 times less than any previously published work for veneers debonding. The enamel beneath the debonded veneers has been assessed by atomic force microscopy (AFM) and shear stress technique as criteria for the easiness of debonding. The

Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference

Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtaine

Wireless Multimedia Sensor Networks (WMSNs) are a type of sensor network that contains sensor nodes equipped with cameras, microphones; therefore the WMSNS are able to   produce multimedia data such as video and audio streams, still images, and scalar data from the surrounding environment. Most multimedia applications typically produce huge volumes of data, this leads to congestion. To address this challenge, This paper proposes Modify Spike Neural Network control for Traffic Load Parameter with Exponential Weight of Priority Based Rate Control algorithm (MSNTLP with EWBPRC). The Modify Spike Neural Network controller (MSNC) can calculate the appropriate traffi