The current research aimed to investigate the psychometric characteristics of the Arabic version of the Nomophobia scale for the Omani youth. The scale was administered to a random sample of students from public and private universities and colleges in Oman. The research sample consisted of 2507 students, of whom 868 males and 1639 females. The validity of the measure was first checked by presenting the scale to a group of experts in this field. Then the exploratory and confirmatory factor analysis was carried out. The exploratory factor analysis revealed the existence of three main factors: the fear of connectivity loss, the fear of communication loss with others, and the fear of network outages. These factors accounted for 65.6% of the

ABSTRACT Planetary Nebulae (PN) distances represent the fundamental parameter for the determination the physical properties of the central star of PN. In this paper the distances scale to Planetary Nebulae in the Galactic bulge were calculated re- lated to previous distances scales. The proposed distance scale was done by recalibrated the previous distance scale technique CKS/D82. This scale limited for nearby PN (D ≤ 3.5 kpc), so the surface fluxes less than other distance scales. With these criteria the results showed that the proposed distance scale is more accurate than other scales related to the observations for adopted sample of PN distances, also the limit of ionized radius (Rio) for all both optically thick and optically thin in

Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing artificial TABU algorithm to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as sport, chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement.

Improving" Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi