The work in this paper involves the planning, design and implementation of a mobile learning system called Nahrain Mobile Learning System (NMLS). This system provides complete teaching resources, which can be accessed by the students, instructors and administrators through the mobile phones. It presents a viable alternative to Electronic learning. It focuses on the mobility and flexibility of the learning practice, and emphasizes the interaction between the learner and learning content. System users are categorized into three categories: administrators, instructors and students. Different learning activities can be carried out throughout the system, offering necessary communication tools to allow the users to communicate with each other through forums, SMS and e-mails. NMLS platform is based on 3G mobile phone technology and adopted WAP as a solution for the system platform. The NMLS framework is based on three layers, which are presentation layer, business logic layer and data layer.
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Reducing global warming potential (GWP) of refrigerants is needed to the decrease of ozone-depleting of refrigeration systems leakages. Refrigerant R1234yf is now used to substitute R134a inside mobile air conditioning systems. Thermodynamic properties of R1234yf are similar to R134a. Also, it has a very low GWP of 4, compared to 1430 for R134a, making it a proper choice for future automobile refrigerants. The purpose of this research is to represent the main operating and performance differences between R1234yf and R134a. Experimental analysis was carried out on the automotive air conditioning system (AACS) with 3 kW nominal capacity, to test and compare the performance of R134a with R1234yf. Experiments were accomplish
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This paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the local minimum problem during learning the proposed controller. Simulation results show that the best weight parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state that will stabilize the Buck converter system performan
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Vapor-liquid equilibrium data are presented for the binary systems n-hexane - 1-propanol, benzene - 1-propanol and n-hexane – benzene at 760 mm of mercury pressure. In addition ternary data are presented at selected compositions with respect to the 1-propanol in the 1-propanol, benzene, n-hexane system at 760 mmHg. The results indicate the relative volatility of n-hexane relative to benzene increases appreciably with addition of 1-propanol.
Abstract A descriptive study using evaluation technique was carried at the health organizations concerning STIs/HIV/AIDS, mainly the AIDS Researches and Studies Center in Baghdad and many of the AIDS sections in the Health Directorates in the Governorates throughout the period of May 15th , 2003 through September 30th, 2003( to describe the surveillance system for the period 1993 through 2002). The study aimed to describe the STIs/HIV/AIDS surveillance system in Iraq. System evaluation questionnaire was adopted from WHO and developed for the purpose of this study. Content validity of questionnaire was establis
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Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing
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