أن الامم والشعوب لاتستطيع المحافظة على استمرار وجودها وتقدمها ورقيها، إلابفضل إعداد اجيالها المتعاقبة الاعداد السليم المتكامل ، وبقدر ما تحافظ الامم والشعوب على تربية هذه الاجيال للتمسك بدينها ، ومعتقداتها ، واخلاقها ، بقدر ما تحافظ على بقائها وعلو شأنها ، وكما فطنت الامم والشعوب إلى اهمية تربية اجيالها ، فطن اعداؤها الى ضرورة التخريب في جذور البناء الصحيح لتربية الاجيال ( فاطمة ، 1998، ص2).

Phenol condensed with β-keto esters via Pechmann condensation to form derivatives of Coumarin in various reaction conditions by two ways. Present paper is comparative study of synthesis Coumarin with the yield of product , reaction time and reaction conditions.

At thermal energies near stellar conditions, nuclear reactions are sensitive to resonance strengths of the nuclear reaction cross-section. In this paper, the resonance strengths of  nuclear reaction were evaluated numerically by means of nuclear reaction rate calculations using a written Matlab code, at the energies of interest in stellar nuclear reactions. The results were compared with standard reaction before and after application of a statistical analyses, to select the best parameters that made theoretical results as close as possible to the standard values. Fitting was made for different temperature ranges up to 10 GK, 0.6 GK and 0.25 GK. The evaluated results showed that as the temperature range becomes narrower, more error is ad

This study offers a new Mixed Meta Heuristics algorithm (HGSABAT) for estimating the parameter values of each of the six categories of Non-Linear regression models examined (Misrald, Meyer4, Meyer7, Militky4, Militky2, and MGH09) by combining the Gravitational Search Algorithm and Bat Algorithm. Some models have different numbers of parameters. For example, the Misrald and Militky2 models of the Non-Linear Regression model have two parameters (Bl, B2). In contrast, the MGH09 and Militky4 models have four parameters (MGHl, MGH2, MGH3, and MGH4), in which location as the Meyer4 and Meyer7 models have three attributes (Meyerl, MGH2, and MGH3). To examine the effectiveness of the suggested Hybrid Meta Heuristics algorithm (HGSABAT), a simulatio

The present investigation is concerned for the purification of impure zinc oxide (80-85 wt %) by using petroleum coke
(carbon content is 76 wt %) as reducing agent for the impure zinc oxide to provide pure zinc vapor, which will be
oxidized later by air to the pure zinc oxide.
The operating conditions of the reaction were studied in detail which are, reaction time within the range (10 to 30 min),
reaction temperature (900 to 1100 oC), air flow rate (0.2 to 1 l/min) and weight percentage of the reducing agent
(petroleum coke) in the feed (14 to 30 wt %).
The best operating conditions were (30 min) for the reaction time, (1100 oC) for the reaction temperature, (1 l/min) for
the air flow rate, and (30 wt %) of reducing

Nurse scheduling problem is one of combinatorial optimization problems and it is one of NP-Hard problems which is difficult to be solved as optimal solution. In this paper, we had created an proposed algorithm which it is hybrid simulated annealing algorithm to solve nurse scheduling problem, developed the simulated annealing algorithm and Genetic algorithm. We can note that the proposed algorithm (Hybrid simulated Annealing Algorithm(GS-h)) is the best method among other methods which it is used in this paper because it satisfied minimum average of the total cost and maximum number of Solved , Best and Optimal problems. So we can note that the ratios of the optimal solution are 77% for the proposed algorithm(GS-h), 28.75% for Si

The evaluation process of transportation problem required finding basic feasible solution (bfs). Represent the base of decision making process. To purpose start anther batter solution to able from decisions taking process, consider the decision making process, find (bfs) represent the standard form in linear programming (LP). Most important stage of update (bsf) stages that achieved minimum cost (the optimal solution). the goal of paper to improving & evaluation (bfs) by new procedure (proposed method) comparison with Vogel method.

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system