An optical spectroscopic study is reported in this article to study the correlation between the supermassive black hole (SMBH) and the star formation rate (SFR) for a sample of Seyfert galaxies type (I and II). The study focused on 45 galaxy of Seyfert 1, in addition to 45 galaxy of Seyfert 2, where these samples have been selected form different survey of Salon Digital Sky Survey (SDSS). The redshift (z) of these objects were between (0.02 – 0.26). The results of Seyfert 1 galaxies shows that there good correlation between the SMBH and the SFR depending on statistical analysis parameter named Spearman’s Rank Correlation in a factor of (ρ=0.609), as well as the Seyfert 2 galaxies results show a good correlation between the SMBH and

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Newly 4-amino-1,2,4-triazole-3-thione ring 2 was formed at position six of 2-methylphenol from the reaction of 6-(5-thio1,3,4-oxadiazol-2-yl)-2-methylphenol 1 with hydrazine hydrochloride in the presence of anhydrase sodium acetate. Seven newly fused heterocyclic compounds were synthesized from compound 2. First fused heterocyclic was 6-(6-(3,5-di-tertbutyl-4-hydroxyphenyl)-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl)-2-methylphenol 3 synthesized from reaction compound 2 with 3,5-di-tert-butyl-4-hydroxybenzoic acid in POCl3. Reaction compound 2 with bromophencylbromide afford 6-(6-(4-bromophenyl)-5H-[1,2,4]triazolo[3,4-b][1,3,4]-thiadiazin-3-yl)-2-methylphenol 4. 6-(6-thio-1,7a-dihydro-[1,2,4] triazolo[3,4-b][1,3,4]-thiadiazol-3-yl)-2

The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

Abstract:

     This study aims to identify the level of patients’ satisfaction among a sample of hospitalized patients in the targeted hospital (Al-Kindy Teaching Hospital, and Al-Yarmook Teaching Hospital). Moreover, this study highlights the reality of services for patients, especially in the targeted governmental teaching hospitals. The Patient Satisfaction with Nursing Care (PSNCS) has been measured in these hospitals through the revised scale by Tang et al, (2013).This scale includes four major domains; Health Information (5 items), Influencing Support (4 items), Decision Control (4 items), Specialized Technical Competence (7 items). The method of surveying patients’ opinions about the degree

To perform a secure evaluation of Indoor Design data, the research introduces a Cyber-Neutrosophic Model, which utilizes AES-256 encryption, Role-Based Access Control, and real-time anomaly detection. It measures the percentage of unpredictability, insecurity, and variance present within model features. Also, it provides reliable data security. Similar features have been identified between the final results of the study, corresponding to the Cyber-Neutrosophic Model analysis, and the cybersecurity layer helped mitigate attacks. It is worth noting that Anomaly Detection successfully achieved response times of less than 2.5 seconds, demonstrating that the model can maintain its integrity while providing privacy. Using neutrosophic sim

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Derivatives of Schiff-bases possess a great importance in pharmaceutical chemistry. They can be used for synthesizing different types of bioactive compounds. In this paper, derivatives of new Schiff bases have been synthesized from several serial steps. The acid (I) was synthesized from the reaction of dichloroethanoic acid with 2 moles of p-aminoacetanilide. New acid (I) converted to its ester (II) via the reaction of (I) with dimethyl sulphate in the present of anhydrous of sodium carbonate and dry acetone. Acid hydrazide (III) has been synthesized by adding 80% of hydrazine hydrate  to the new ester using ethanol as a solvent. The last step included the preparation of new Schiff-bases (IV-VIII) by the reaction of acid hydrazide with