Results showed that the optimum conditions for production of inulunase from isolate Kluyveromyces marxianus AY2 by submerged culture could be achieved by using inulin as carbon source at a concentration of 2% with mixture of yeast extract and ammonium sulphate in a ratio of 1:1 in a concentration of 1% at initial pH 5.5 after incubation for 42 hours at 30ºC.

This paper studies the behavior of axially loaded RC columns which are confined with carbon fiber reinforced polymers’ sheet (CFRP) and steel jackets (SJ). The study is based on twelve axially loaded RC columns tested up to failure. It is divided into three schemes based on its strengthening type; each scheme has four columns. The main parameters in this study were the compressive strength of the concrete and steel reinforcement ratio. Furthermore, the results of the experimental test showed a substantial enhancement in the column's load-carrying capacity. When compared to the original columns, the CFRP sheet had a significant effect on improving the ductility of the column by increasing the axial deformation by about 59.2 to 95.7

This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to

أن التطور العلمي الحاصل فيما يخص المجال الرياضي أرسى آفاق جديدة لمواكبة التطور الكبير في مجا ل الألعاب والفعاليات الرياضية المختلفة ,و أن تحقيق النتائج الجيدة في فعاليات العاب القوى بشكل عام والثلاثية بشكل خاص في التدريب الرياضي يتطلب إتباع الأساليب العلمية الدقيقة والموضوعية بشكل سليم ومخطط له،فضلا عنة تطبيق نظريات ومنحى جديد لمواكبة الاتجاهات الحديثة في تحقيق النتائج الجيدة للوصول إلى المستويات العالية

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

Four different spectrophotometric methods are used in this study for the determination of Sulfamethoxazole and sulfanilamide drugs in pharmaceutical compounds, synthetic samples, and in their pure forms. The work comprises four chapters which are shown in the following: Chapter One: Includes a brief for Ultraviolet-Visible (UV-VIS) Absorption spectroscopy, antibacterial drugs and sulfonamides with some methods for their determination. The chapter lists two methods for optimization; univariate method and multivariate method. The later includes different types, two of these were mentioned; simplex method and design of experiment method. Chapter Two: Includes reaction of the two studied drugs with sodium nitrite and hydrochloric acid for diazo

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

      Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify and estimate the long memory parameter in partially integrated time series. One of the most common models used to represent time series that have a long memory is the ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are a fractional number called the fractional parameter. To analyze and determine the ARFIMA model, the fractal parameter must be estimated. There are many methods for fractional parameter estimation. In this research, the estimation methods were divided into indirect methods, where the Hurst parameter is estimated fir