This research aims to know the role of transformational leadership in the organizational success of the College of Education at Samarra University. The researcher adopted the analytical descriptive method in analyzing the research problem. The research included two main hypotheses that resulted in four hypotheses that were subjected to statistical tests. A sample of (54) The researcher used the survey method as a main tool for collecting data and information as well as visits and structured interviews that took place during the period of application. The research reached a set of conclusions and recommendations among the conclusions that there is an art relationship There is a strong and moral impact between transformational leadership and organizational success at the macro and sub-level, as well as the managerial leadership with the qualities of transformational leadership at a good level with its commitment to increasing efficiency and organizational effectiveness. The most prominent recommendations were the need for the management of the faculty to be aware of the importance of individual consideration and intellectual To gain their employees' confidence in creativity and commitment in their work, which has an impact on increasing efficiency and organizational effectiveness, and the need to pay more attention to organizational efficiency as it is the fundamental basis for organizational success and that the focus on organizational effectiveness can not To reach the college in question to high levels of organizational success
Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.
The direct electron transfer behavior of hemoglobin that is immobilized onto screen-printed carbon electrode (SPCE) modified with silver nanoparticles (AgNPs) and chitosan (CS) was studied in this work. Cyclic voltametry and spectrophotometry were used to characterize the hemoglobin (Hb) bioconjunction with AgNPs and CS. Results of the modified electrode showed quasi-reversible redox peaks with a formal potential of (-0.245 V) versus Ag/AgCl in 0.1 M phosphate buffer solution (PBS), pH7, at a scan rate of 0.1 Vs-1. The charge transfer coefficient (α) was 0.48 and the apparent electron transfer rate constant (Ks) was 0.47 s-1. The electrode was used as a hydrogen peroxide biosensor with a linear response over 3 to 240 µM and a detection li
... Show Morehas experienced a step-change since the inception of ambient mass spectrometry removed the requirement for samples to be investigated under vacuum conditions. Approaches based on surface– plasma interactions are especially promising, including PADI. Whilst the mechanisms involved in generating PADI spectra still need to be unravelled, PADI shows significant promise to become a valuable and versatile tool in the instrumental arsenal available to the surface analyst
KE Sharquie, AA Noaimi, MS Abass, American Journal of Dermatology and Venereology, 2019 - Cited by 4
In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method
Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr
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