With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

     In this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used:  local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the

We have investigated the impact of laser pulse wavelength on the quantity of ablated materials. Specifically, this study investigated the structural, optical, and morphological characteristics of tungsten trioxide (WO3) nanoparticles (NPs) that were synthesized using the technique of pulsed-laser ablation of a tungsten plate. A DD drop of water was used as the ablation environment at a fixed fluence at 76.43 J/cm2 and pulse number was 400 pulses of the laser. The first and second harmonic generation ablations were carried out, corresponding to wavelengths of 1064 and 532 nm, respectively. The Q-switched Nd: YAG laser operates at a repetition rate of 1 Hz and has a pulse width of roughly 15 ns. These parameters are applicable to both wavelen

The relation between the output power and wavelengths for a 532nm 3W frequency doubled diode pumped solid state laser pumped Ti:Sapphire crystal is investigated. A 20 femtosecond pulse at 800 nm is obtained. A 320 mW is found to be the highest power at 800nm. Below this wavelength value and above the power was found to deviate from highest output value.

The research has designed for studying the relationship between manufacturing strategy and its flexibility under the flexible manufacturing system with their reflection on the competitive environmental performance of the firm. To interpret and tackle the problem, a hypothesis has formulated stating that “ the competitive performance of a firm is interpreted by the manufacturing strategy and flexibility which are derived from the firm and its business  strategies under the flexible manufacturing system”. Related literatures with their theoretical dissertations, which enhanced the thoughtful content, have analyzed. An illustrative case study on the flexible manufacturing system at Toyota Motors Corporation working at the g

Abstract

Magnetic abrasive finishing (MAF) is one of the advanced finishing processes, which produces a high level of surface quality and is primarily controlled by a magnetic field. This paper study the effect of the magnetic abrasive finishing system on the material removal rate (MRR) and surface roughness (Ra) in terms of magnetic abrasive finishing system for eight of input parameters, and three levels according to Taguchi array (L27) and using the regression model to analysis the output (results). These parameters are the (Poles geometry angle, Gap between the two magnetic poles, Grain size powder, Doze of the ferromagnetic abrasive powder, DC current, Workpiece velocity, Magnetic poles velocity, and Finishi

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification

in this paper copper oxide (cuO thin films were prepared by the method of vacum thermal evaporation a pressure.