he assignment model represents a mathematical model that aims at expressing an important problem facing enterprises and companies in the public and private sectors, which are characterized by ensuring their activities, in order to take the appropriate decision to get the best allocation of tasks for machines or jobs or workers on the machines that he owns in order to increase profits or reduce costs and time As this model is called multi-objective assignment because it takes into account the factors of time and cost together and hence we have two goals for the assignment problem, so it is not possible to solve by the usual methods and has been resorted to the use of multiple programming The objectives were to solve the problem of

The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

The need for detection and investigation of the causes of pollution of the marshes and submit a statistical study evaluated accurately and submitted to the competent authorities and to achieve this goal was used to analyze the factorial analysis and then obtained the results from this analysis from a sample selected from marsh water pollutants which they were: (Electrical Conductivity: EC, Power of Hydrogen: PH, Temperature: T, Turbidity: TU, Total Dissolved Solids: TDS, Dissolved Oxygen: DO). The size of sample (44) sites has been withdrawn and examined in the laboratories of the Iraqi Ministry of Environment. By illustrating SPSS program) the results had been obtained. The most important recommendation was to increase the pumping of addit

In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

In this study, a fast block matching search algorithm based on blocks' descriptors and multilevel blocks filtering is introduced. The used descriptors are the mean and a set of centralized low order moments. Hierarchal filtering and MAE similarity measure were adopted to nominate the best similar blocks lay within the pool of neighbor blocks. As next step to blocks nomination the similarity of the mean and moments is used to classify the nominated blocks and put them in one of three sub-pools, each one represents certain nomination priority level (i.e., most, less & least level). The main reason of the introducing nomination and classification steps is a significant reduction in the number of matching instances of the pixels belong to the c

The thermal method was used to produce silicoaluminophosphate (SAPO-11) with different amounts of carbon nanotubes (CNT). XRD, nitrogen adsorption-desorption, SEM, AFM, and FTIR were used to characterize the prepared catalyst. It was discovered that adding CNT increased the crystallinity of the synthesize SAPO-11 at all the temperatures which studied, wile the maximum surface area was 179.54 m2/g obtained at 190°C with 7.5 percent of CNT with a pore volume of 0.317 cm3/g ,and with nano-particles with average particle diameter of 24.8 nm, while the final molar composition of the prepared SAPO-11 was (Al2O3:0.93P2O5:0.414SiO2).

This research supports the UN’s 2030 Agenda and its goals of ending poverty and hunger. Fruit and vegetable (FandV) lose their freshness and weight when stored at unsuitable temperatures and relative humidity. This study was conducted in Baghdad governorate, located at latitude 33.3128057 and longitude 44.3614875, in the Karrada region from February 27 to April 17, 2024. It compared the effectiveness of different storage technologies, including evaporative cooling, various air velocities, and diverse packaging methods, against sustainable and nonpackaging approaches. The study employed an air cooler with a volume of 2000 ft3/s and insulated packaging. Temperature and relative humidity were recorded in the storage environment