This research aimed to examine the correlation and influence of leader-member exchange in the organizational citizenship behavior in the existence of organizational trust as mediator variable among workers in ministry of migration and displaced, has been collecting data and information relating to research by designing questionnaire was distributed to the stratified random sample included (56) member of the workers in the ministry mentioned, and through the use of several statistical method were the main findings of research was the existence of a correlation relationship and influence between leader- member exchange and organizational trust, while the results confirmed no correlation and influence exist between leader- member exchange and organizational citizenship behavior. The research ended with a group of Conclusions The most prominent of the organizational trust important role in increasing the impact of leader- member exchange of the Organizational Citizenship Behavior, And it recommended that research on the need for the Planning Directorate in the Ministry of Displacement and Migration work poll periodically to the views of staff for the running of the ministry and the relationship between supervisor and there Subordinates, it would provide for the heads of sources to develop their performance as well as enhance their openness and transparency, which requires that takes presidents including posed by subordinates of Notesand ideas.
There is an evidence that channel estimation in communication systems plays a crucial issue in recovering the transmitted data. In recent years, there has been an increasing interest to solve problems due to channel estimation and equalization especially when the channel impulse response is fast time varying Rician fading distribution that means channel impulse response change rapidly. Therefore, there must be an optimal channel estimation and equalization to recover transmitted data. However. this paper attempt to compare epsilon normalized least mean square (ε-NLMS) and recursive least squares (RLS) algorithms by computing their performance ability to track multiple fast time varying Rician fading channel with different values of Doppler
... Show MoreImmune-mediated hepatitis is a severe impendence to human health, and no effective treatment is currently available. Therefore, new, safe, low-cost therapies are desperately required. Berbamine (BE), a natural substance obtained primarily from
This c
Tanuma and Zubair formations are known as the most problematic intervals in Zubair Oilfield, and they cause wellbore instability due to possible shale-fluid interaction. It causes a vast loss of time dealing with various downhole problems (e.g., stuck pipe) which leads to an increase in overall well cost for the consequences (e.g., fishing and sidetrack). This paper aims to test shale samples with various laboratory tests for shale evaluation and drilling muds development. Shale's physical properties are described by using a stereomicroscope and the structures are observed with Scanning Electron Microscope. The shale reactivity and behavior are analyzed by using the cation exchange capacity testing and the capillary suction test is
... Show MoreHTH Ahmed Dheyaa Al-Obaidi,", Ali Tarik Abdulwahid', Mustafa Najah Al-Obaidi", Abeer Mundher Ali', eNeurologicalSci, 2023
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
... Show MoreWater quality sensors have recently received a lot of attention due to their impact on human health. Due to their distinct features, environmental sensors are based on carbon quantum dots (CQDs). In this study, CQDs were prepared using the electro-chemical method, where the structural and optical properties were studied. These quantum dots were used in the environmental sensor application after mixing them with three different materials: CQDs, Alq3 polymer and CQDs and Alq3 solutions using two different methods: drop casting and spin coating, and depositing them on silicon. The sensitivity of the water pollutants was studied for each case of the prepared samples after measuring the change in resistance of the samples at a temperature of
... Show More'Steganography is the science of hiding information in the cover media', a force in the context of information sec, IJSR, Call for Papers, Online Journal