Purpose: To determine the impact of service encounter in stimulating voluntary customer behaviors.

• Approach / methodology: It was selected a sample of customers Bank Iraqi Trade (TBI) was (105) individual, using a questionnaire designed in the light of previous studies, was drafted scale and tested in the light of a group of statistical methods developed (reliability coefficient, reliability coefficient composite, convergence). Then test hypotheses through structural equation modeling.
• Results: The behaviors and characteristics of the service provider in effect urged bank customers to perform voluntary extra, as the service environment service encounter

Methotrexate (MTX) is widely used chemotherapeutic agent with different side effects including germ cells toxicities. Silibinin is one of the structural isomers of silymarin, with different phytotherapeutic applications, and its possible protective effects against MTX induced germ cells damage were investigated in this work. Twenty five male mice were divided into five groups (n=5) allocated as follows: Group 1 received buffer for five days given by single intraperitoneal (IP) injection per day; Group 2 in addition to buffer for five days, animals received at day five single dose of 20mg/kg of MTX IP. Groups (3, 4, and 5) received respectively, (50, 100, or 150mg/kg body weight) of silibinin IP single daily dose for five days then at day fi

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

Particulate matter (PM) emitted from diesel engine exhaust have been measured in terms of mass, using
99.98 % pure ethanol blended directly, without additives, with conventional diesel fuel (gas – oil),to
get 10 % , 15 %, 20 % ethanol emulsions . The resulting PM collected has been compared with those
from straight diesel. The engine used is a stationary single cylinder, variable compression ratio Ricardo
E6/US. This engine is fully instrumented and could run as a compression or spark ignition.
Observations showed that particulate matter (PM) emissions decrease with increasing oxygenate
content in the fuel, with some increase of fuel consumption, which is due to the lower heating value of
ethanol. The reduction in

The coefficient of charge transfer at heterogeneous devices of Au metal with a well-known dyeis investigations using quantum model.Four different solvent are used to estimation the effective transition energy. The potential barrier at interface of Au and dye has been determined using effective transition energy and difference between the Fermi energy of Au metal and ionization energy of dye. A possible transfer mechanism cross the potential barrier dyeand coupling strength interaction between the electronic levels in systems of Au and is discussed.Differentdata of effective transition energy and potential barrier calculations suggest that solvent is more suitable to binds Au with dye.

In this paper, a Cholera epidemic model is proposed and studied analytically as well as numerically. It is assumed that the disease is transmitted by contact with Vibrio cholerae and infected person according to dose-response function. However, the saturated treatment function is used to describe the recovery process. Moreover, the vaccine against the disease is assumed to be utterly ineffective. The existence, uniqueness and boundedness of the solution of the proposed model are discussed. All possible equilibrium points and the basic reproduction number are determined. The local stability and persistence conditions are established. Lyapunov method and the second additive compound matrix are used to study the global stability of the system.

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with