Nanoparticles of Pb_1-xCd_xS within the composition of 0≤x≤1 were prepared from the reaction of aqueous solution of cadmium acetate, lead acetate, thiourea, and NaOH by chemical co-precipitation. The prepared samples were characterized by UV-Vis spectroscopy(in the range 300-1100nm) to study the optical properties, AFM and SEM to check the surface morphology(Roughness average and shape) and the particle size. XRD technique was used to determine the crystalline structure, XRD technique was used to determine the purity of the phase and the crystalline structure, The crystalline size average of the nanoparticles have been found to be 20.7, 15.48, 11.9, 11.8, and 13.65 nm for PbS, Pb_0.75Cd_0.25S,

It has been shown in ionospheric research that calculation of the total electron content (TEC) is an important factor in global navigation system. In this study, TEC calculation was performed over Baghdad city, Iraq, using a combination of two numerical methods called composite Simpson and composite Trapezoidal methods. TEC was calculated using the line integral of the electron density derived from the International reference ionosphere IRI2012 and NeQuick2 models from 70 to 2000 km above the earth surface. The hour of the day and the day number of the year, R12, were chosen as inputs for the calculation techniques to take into account latitudinal, diurnal and seasonal variation of TEC. The results of latitudinal variation of TE

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Objective: The aim of this study is to evaluate anemia among lactating women and their children less than 2 years of
age.
Methodology: The study was done on (148) lactating mothers and their children under 2 years of age in the primary
health care center at AL-Salam Quarter/Baghdad from l/10/2009 to 15/10/2010.
Result: This study recommends that there is a significant relationship between anemic mothers and their children. The
study also revealed that there is (77.8%) from nursing mothers in the age groups (25-29) who suffered from anemia,
while (23.1%) for the age group (20-24) did not have anemia
Recommendation: We encourage the use of breast milk or iron-fortified infant formula only for any milk-based part
of

SIFCON is characterized as a construction material of high ductility and very high strength. It is suitable for concrete structures used for special applications. However, the density of SIFCON is much higher than that of Fiber Reinforced Concrete (FRC) due to the need for a large amount of high-density steel fibers. This work examines the split tensile behavior of modified weight slurry infiltrated fiber concrete utilizing a mixture of two types of fibers, steel fiber, and polyolefin fiber. For the investigation, 30 cylinders and 15 cubes were poured. The used volume fraction (V.F) is (6 %) and the use of five series once as each type separately and once a hybrid in proportions of 2/3 polyolefin with 1/3 steel fiber and

This experiment was conducted in the season 2001-2000 in station Ishaqi the company's general industrial crops to plant livestock Vigna radala deleted (Khadrawi) carried out the experiment design panels splinter and order in RCBD with three balls two factors are levels nitrogen fertilizer (120 and, 100.0 kg urea / ha)nitrogen ratio of 46%, which put in the main panels mAIN PLOT and Alkiavat three levels that were placed in secondary panels .....

This article investigates the relationship between foot angle and jump stability, focusing on minimizing injury risk. Here are the key points: Importance: Understanding foot angle is crucial for improving jump stability, athletic performance, and reducing jump-related injuries like ankle sprains. Ideal Foot Angle: Research suggests a forward foot angle of around 15 degrees might be ideal for many people during jumps. This angle distributes forces evenly across the foot, lowers the center of gravity, and provides more surface area for pushing off the ground. Factors Affecting Ideal Angle: The optimal angle can vary depending on the type of jump (vertical vs. long jump), fitness level, and personal preference. Incorrect Foot Angles: Landing w

I n  this  paper ,we 'viii  consider  the density  questions  associC;lted with  the single  hidden layer feed forward  model. We proved  that a FFNN   with   one   hidden   layer  can   uniformly   approximate   any continuous  function  in C(k)(where k is a compact set in R11 ) to any required accuracy.

However, if the set of basis function is dense then the ANN's can has al most one hidden layer. But if the set of basis function  non-dense, then we  need more  hidden layers. Also, we have shown  that there exist  localized functions and that there is no t

Left ventricular hypertrophy (LVH) caused by high blood pressure is linked to increased mortality and arrhythmia risk. This study aimed to evaluate arrhythmia in hypertensive patients due to left ventricular hypertrophy (LVH). A cross-sectional study was performed, assessing participants' blood pressure, echocardiography and electrocardiography, and Holter monitoring in certain cases. There were 300 hypertensive patients >18 years attending the cardiology unit of Baghdad medical city. The study was conducted between January–June 2022. The electrocardiograms at rest for 300 adults with hypertension were investigated. 130 (43.5%) were females, and 170 (56.5%) were males. The mean age of participants was 58 years. Forty-nine (16.3