This research aims to identify the role of external environment factors on the quality of educational services, from the academic point of view, where the distribution of a questionnaire to a random sample of (100) university professors, and then analyzing a model, and test the validity of this model using structural modeling (SEM) (Structural Equation Modeling).
And then test the relationships between variables using the software of Statistical Package for Social Sciences (SPSS V.18), the research found a number of conclusions, the most important conclusion is: the external environment factors has significant impact on the quality of educational services.

The problem of the research is that some companies lack the mechanisms and policies that to enhance the loyalty of these valuable resources, which led to difficulties in generating knowledge, producing, storing, investing and investing information to achieve a sustainable competitive advantage. In addition, companies are still working with the traditional wage system (monthly salaries), which makes people not think about adding value to work. The research aims to determine the level of variables for researchers to clarify the impact of the organizational innovation climate on sustainable competitive advantage. The Qualitative approach is used based on research. The research was applied

In the last years, a new technology called Cloud computing has been developed. Empirical and previous studies, commonly examined in business field and other domains. In this study, the significant factors that affecting the adoption of cloud computing have been examined using a frequency analysis that have been explored by the previous studies. The results showed that the most effected factors were relative advantage which followed by security and privacy, complexity, innovativeness, and external support. In this study the model of technology organization-environment was used to examine the significant factors that affecting the adoption of cloud computing.

Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed

Objectives: acute kidney injury (AKI) is a serious pathophysiology side effect of rhabdomyolysis. Inflammatory mechanisms play a role in the development of rhabdomyolysis-induced AKI. Citronellol (CT) is a naturally occurring monoterpene alcohol (3,7-Dimethyl-6-often-1-ol) found in aromatic plant species' essential oils. In this study, we explored the protective effects of Citronellol on glycerol-induced AKI.

Methods: Four groups of eight mice each (n=8) were formed by randomly dividing the animals into the groups, glycerol-induced AKI model group, low-dose CT-treated group (50mg/kg), high-dose CT-treated group (100mg/kg), and control group. The renal functions of mice from all groups were evalua

Health and safety problem can be described by statistics it can only be understood by knowing and feeling the pain, suffering, and depression. Health and safety has a legal responsibility to protect it for everyone who can affect in the workplace. This includes manufacturers, suppliers, designers and controllers of work places and employees. Work injury is one of the major problems in manufacturing and production systems industries; it is reduced production efficiency and affects the cost. To gain flexibility from a traditional manufacturing system and production efficiency, this paper is about the application of estimating technology to preview and synthesis of Lost Time of Work Injuries in industry systems aims to provide a safe workin

The aim of this paper is to estimate a nonlinear regression function of the Export of the crude oil Saudi (in Million Barrels) as a function of the number of discovered fields.

 Through studying the behavior of the data we show that its behavior was not followed a linear pattern or can put it in a known form so far there was no possibility to see a general trend resulting from such exports.

We use different nonlinear estimators to estimate a regression function, Local linear estimator, Semi-parametric as well as an artificial neural network estimator (ANN).

The results proved that the (ANN) estimator is the best nonlinear estimator am

      The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin

The Dagum Regression Model, introduced to address limitations in traditional econometric models, provides enhanced flexibility for analyzing data characterized by heavy tails and asymmetry, which is common in income and wealth distributions. This paper develops and applies the Dagum model, demonstrating its advantages over other distributions such as the Log-Normal and Gamma distributions. The model's parameters are estimated using Maximum Likelihood Estimation (MLE) and the Method of Moments (MoM). A simulation study evaluates both methods' performance across various sample sizes, showing that MoM tends to offer more robust and precise estimates, particularly in small samples. These findings provide valuable insights into the ana

Surface electromyography (sEMG) and accelerometer (Acc) signals play crucial roles in controlling prosthetic and upper limb orthotic devices, as well as in assessing electrical muscle activity for various biomedical engineering and rehabilitation applications. In this study, an advanced discrimination system is proposed for the identification of seven distinct shoulder girdle motions, aimed at improving prosthesis control. Feature extraction from Time-Dependent Power Spectrum Descriptors (TDPSD) is employed to enhance motion recognition. Subsequently, the Spectral Regression (SR) method is utilized to reduce the dimensionality of the extracted features. A comparative analysis is conducted between the Linear Discriminant Analysis (LDA) class