This study is about finding the estimation of tow equations, the comparative has been done between the estimations  by using seemingly unrelated regression equations for the variable and random error has been distribution with poisson and the variable and random error has been distribution with normal and the method by using oldenary lest square.

While in the application side, we have estimated the parameter of investment specification function for the sector of agriculture with the industry sector is enabled us to obtain an estimation efficiency for the model of seemingly unrelated Poisson regression equation.

المستخلص

A two-year study (harvest years 2019 and 2020) was conducted to investigate the effect of a commercially available biofertilizer, in combination with variable nitrogen (N) rate, on bread baking quality and agronomic traits in hard winter wheat grown in conventional (CONV) and organic (ORG) farming systems in Kentucky, USA. The hard red winter wheat cultivar ‘Vision 45’ was used with three N rates (44, 89.6 and 134.5 kg/ha as Low, Med and High, respectively) and three biofertilizer spray regimes (no spray, one spray and two sprays). All traits measured were significantly affected by the agricultural production system (CONV or ORG) and N rate, although trends in their interactions were inconsistent between years. In Y2, yield was

In this study, we tackle the understudied area of Artificial Intelligence (AI) and its role in examining how modern revolutions may affect political systems across the Middle Eastern region. despite hundreds of studies documenting Middle Eastern uprisings over the past three decades, there has been little effort to harness AI to better understand or predict these multifaceted events. This study seeks to address this gap by assessing the performance of AI-intelligence in analyzing (broadly) revolutionary processes and their effects on regional political systems. The research uses a mixedmethod methodology that involves a systematic literature review of contemporary scholarly articles, and an analytics study using AI tools. Our results show t

Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model  in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling  the Symmetric gray scale texture image and estimating by using

In this work, solid random gain media were fabricated from laser dye solutions containing nanoparticles as scattering centers. Two different rhodamine dyes (123 and 6G) were used to host the highly-pure titanium dioxide nanoparticles to form the random gain media. The spectroscopic characteristics (mainly fluorescence) of these media were determined and studied. These random gain media showed laser emission in the visible region of electromagnetic spectrum. Fluorescence characteristics can be controlled to few nanometers by adjusting the characteristics of the host and nanoparticles as well as the preparation conditions of the samples. Emission of narrow linewidth (3nm) and high intensity in the visible region (533-537nm) was obtained.

In this work, solid random gain media were fabricated from laser dye solutions containing nanoparticles as scattering centers. Two different rhodamine dyes (123 and 6G) were used to host the highly-pure titanium dioxide nanoparticles to form the random gain media. The spectroscopic characteristics (mainly fluorescence) of these media were determined and studied. These random gain media showed laser emission in the visible region of electromagnetic spectrum. Fluorescence characteristics can be controlled to few nanometers by adjusting the characteristics of the host and nanoparticles as well as the preparation conditions of the samples. Emission of narrow linewidth (3nm) and high intensity in the visible region (533-537nm) was obtained.

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa