Background: Accurate measurement of a patient’s height and weight is an essential part of diagnosis and therapy, but there is some controversy as to how to calculate the height and weight of patients with disabilities. Objective: This study aims to use anthropometric measurements (arm span, length of leg, chest circumference, and waist circumference) to find a model (alternatives) that can allow the calculation of the height and the body weight of patients with disabilities. Additionally, a model for the prediction of weight and height measurements of patients with disabilities was established. Method: Four hander patients aged 20-80 years were enrolled in this study and divided into two groups, 210 (52.5%) male and 190 (47.5%) fe

The global oil market is one of the most important markets in the world and occupies especially for countries consuming and producing countries, and the status of understanding of the mechanism for determining prices in the market help to stand on many factors affecting oil demand and supply of oil and geopolitical factors, climate and alternative sources of energy .. etc. factors, and that the main objective of the research is to study the causes and results left behind by the oil price shocks in the world market, and the movement of these factors be through a cycle of energy that explain the strength of competition between these factors and their effects on prices, when demand increases evolution Large image leads to significan

             The synthesis of nanoparticles (GNPs) from the reduction of HAuCl₄ ^.3H₂O by aluminum metal was obtained in aqueous solution with the use of Arabic gum as a stabilizing agent. The GNPs were characterized by TEM, AFM and Zeta potential spectroscopy. The reduction process was monitored over time by measuring ultraviolet spectra at a range of λ 520-525 nm. Also the color changes from yellow to ruby red, shape and size of GNP was studied by TEM. Shape was spherical and the size of particles was (12-17.5) nm. The best results were obtained at pH 6.

Abstract

 A surface fitting model is developed based on calorimeter data for two famous brands of household compressors. Correlation equations of ten coefficient polynomials were found as a function of refrigerant saturating and evaporating temperatures in range of (-35℃ to -10℃) using Matlab software for cooling capacity, power consumption, and refrigerant mass flow rate.

Additional correlations equations for these variables as a quick choice selection for a proper compressor use at ASHRAE standard that cover a range of swept volume range (2.24-11.15) cm³.

The result indicated that these surface fitting models are accurate with in ± 15% for 72 compressors model of cooling cap

Metallic nanoparticles are increasingly studied for their biomedical applications due to their unique physicochemical and catalytic properties. Here, a broccoli-mediated gold/platinum nanohybrid (Au@Pt NH) was synthesized using an ultrasound-assisted green method with an aqueous extract of Brassica oleracea var. italica for multifunctional biomedical evaluation. XRD and TEM confirmed a crystalline nanohybrid with an average crystallite size of 7.56 nm and a mean particle diameter of 13.08 ± 7.58 nm. The broccoli extract produced no inhibition zones, whereas Au@Pt NH inhibited Staphylococcus aureus (18 mm), Staphylococcus epidermidis (21 mm), Escherichia coli (18 mm), Klebsiella pneumoniae (20 mm), and Candida albicans (21 mm). In vivo,

In this work gold nanoparticles (AuNPs), were prepared. Chemical method (Seed-Growth) was used to prepare it, then doping AuNPs with porous silicon (PS), used silicon wafer p-type to produce (PS) the processes doping achieved by electrochemical etching, the solution etching consist of HF, ethanol and AuNPs suspension, the result UV-visible absorption for AuNPs suspension showed the single peak located at ~(530 – 521) nm that related to SPR, the single peak is confirmed that the NPs present in the suspension is spherical shape and non-aggregated. X-ray diffraction analysis indicated growth AuNPs with PS. compare the PS layer without AuNPs and with AuNPs doped for electrical properties and sensitivity properties we found AuNPs:PS is more

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi