Irrigation scheduling techniques is one of the suggested solutions for water scarcity problem. The study aims to show the possibility of using practical and applicable irrigation scheduling program which was designed by Water Resources Department at the University of Baghdad by using Spreadsheet Formulas for Microsoft Excel program, version 2007, with some modification to generalize it and made it applicable to various climatic zone and different soil types, as a salvation for the shortage of irrigation water inside the irrigation projects. Irrigation projects which incidence of Tigris River basin will be taken as an applicable example. This program was based on water budgeting and programmed depending on scientific concepts which facili

A novel robust finite time disturbance observer (RFTDO) based on an independent output-finite time composite control (FTCC) scheme is proposed for an air conditioning-system temperature and humidity regulation. The variable air volume (VAV) of the system is represented by two first-order mathematical models for the temperature and humidity dynamics. In the temperature loop dynamics, a RFTDO temperature (RFTDO-T) and an FTCC temperature (FTCC-T) are designed to estimate and reject the lumped disturbances of the temperature subsystem. In the humidity loop, a robust output of the FTCC humidity (FTCC-H) and RFTDO humidity (RFTDO-H) are also designed to estimate and reject the lumped disturbances of the humidity subsystem. Based on Lyapunov theo

Many of the key stream generators which are used in practice are LFSR-based in the sense that they produce the key stream according to a rule y = C(L(x)), where L(x) denotes an internal linear bit stream, produced by small number of parallel linear feedback shift registers (LFSRs), and C denotes some nonlinear compression function. In this paper we combine between the output sequences from the linear feedback shift registers with the sequences out from non linear key generator to get the final very strong key sequence

The study aims to integrate the visually impaired people into the art connoisseur community through producing special print artworks to enable the visually impaired people to use their other senses to feel artworks by using artistic printing techniques through adding some prominent materials to the printing colors or making an impact that visually impaired people can perceive using their other senses. This study also aims to set up art exhibitions that display tangible works that can enable visually impaired people to feel artwork and understand its elements to enable them to feel it through other senses.
The study follows the experimental method, through using artistic printing techniques, which allow printing with prominent textur

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Аннотация

    в статье рассматриваются проблемы преподавания русской литературы в иракской аудитории.. Использование литературы в преподавании иностранного языка, как правило, имеет две цели. Первая-чисто лингвистическая .. Вторая цель, однако, ассоциируется больше с экстралингвистикой  и представляет собой ознакомление студентов с различными аспектами русской жизни, культуры,

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

This paper generalizes and improves the results of Margenstren, by proving that the number of -practical numbers  which is defined by   has a lower bound in terms of . This bound is more sharper than Mangenstern bound when  Further general results are given for the existence of -practical numbers, by proving that the interval contains a -practical for all