A quantum mechanical description of the dynamics of  non-adiabatic electron transfer in metal/semiconductor interfaces  can be achieved using simplified models  of the system. For this system we can suppose two localized quantum states donor state |D› and acceptor state |A› respectively. Expression of rate constant of electron transfer for metal/semiconductor system derived upon quantum mechanical model and perturbation theory for transition between |𝐷〉 and |𝐴〉 state when the coupling matrix element coefficient is smaller than 0.025eV. The rate of electron transfer for  Au/ ZnSe and Au/ZnS interface systems is evaluated with orientation free energy using a Matlap program. The

Objective: To measure the effect of the pharmacist-led medication reconciliation service before hospital discharge on preventing potential medication errors. Methods: This behavioral interventional study took place in a public teaching hospital in Iraq between December 2022 and January 2023. It included inpatients who were taking four or more medications upon discharge from the internal medicine ward and the cardiac care unit. The researcher provided the patients with a medication reconciliation form and reconciliation form (including medication regimen and pharmacist instructions) before discharging them home. Any discrepancies between the patients’ understanding and the actual medication recommendations prescribed by the physici

Abstract

Due to the momentum of winning in the streets of the city of Baghdad as a result of the large number of checkpoints so felt researcher to conduct a field visit to find out the main reasons that led to this congestion and to find practical solutions to mitigate wastage winning the arrival time citizen to where you want the least possible time.

This research aims to overcome the difficulties experienced by citizens to reach their places of work and reduce waste at the time of service and waiting time as well as reduce the cost of waiting.

Has emerged study a set of conclusions, including the use of model queue (G / G / C) and the mome

 In this paper we study the effect of the number of training samples for  Artificial neural networks ( ANN ) which is necessary for training process of feed forward neural network  .Also we design 5 Ann's and train 41 Ann's which illustrate how good the training samples that represent the actual function for Ann's.

               The research aimed at measuring the compatibility of Big date with the organizational Ambidexterity dimensions of the Asia cell  Mobile telecommunications company in Iraq in order to determine the possibility of adoption of Big data Triple as a approach to achieve organizational Ambidexterity.

The study adopted the descriptive analytical approach to collect and analyze the data collected by the questionnaire tool developed on the Likert scale After  a comprehensive review of the literature related to the two basic study dimensions, the data has been subjected to many statistical treatments in accordance with res

The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation. The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles. One of these methods employ neural network for obtaining the solution.

This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions. This method depends on training cascade-forward neural network by inputs which represent the mean of volterra integral equations solutions, the target of cascade-forward neural network is to get the desired output of this network. Cascade-forward neural

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co