Background: Waterpipe and cigarette are two products of tobacco consumption, tobacco use has detrimental effects on the oral cavity, numerous studies around the world have reported a significant relationship between smoking and increase dental caries and viable count of cariogenic bacteria, Materials and Methods: unstimulated saliva was collected from 84 subjects and divided equally into three groups waterpipe smokers, cigarette smokers, and non-smokers all of the participants are adult male aged between 25-60 years, dental caries was measured by use DMFT index, while S.mutans and S.sobrinus were isolated by using a selective medium SB 20M (Sugar bacitracin-20 modified) agar Results: this present study showed a significant (p≤0.01)

The present work aimed to make a comparative investigation between three different ionospheric models: IRI-2020, ASAPS and VOACAP. The purpose of the comparative study is to investigate the compatibility of predicting the Maximum Usable Frequency parameter (MUF) over mid-latitude region during the severe geomagnetic storm on 17 March 2015. Three stations distributed in the mid-latitudes were selected for study; these are (Athens (23.50o E, 38.00o N), Jeju (124.53o E, 33.6o N) and Pt. Arguello (239.50o W, 34.80o N). The daily MUF outcomes were calculated using the tested models for the three adopted sites, for a span of five-day (the day of the event and two days preceding and following the event day). The calculated datasets were co

Abstract

 The problem of missing data represents a major obstacle before researchers in the process of data analysis in different fields since , this problem is a recurrent one in all fields of study including social , medical , astronomical and clinical experiments .

The presence of such a problem within the data to be studied may influence negatively on the analysis and it may lead to misleading conclusions , together with the fact that these conclusions that result from a great bias caused by that problem in spite of the efficiency of wavelet methods but they are also affected by the missing of data , in addition to the impact of the problem of miss of accuracy estimation

Pareto distribution is used in many economic, financial and social applications. This distribution is used for the study of income and wealth and the study of settlement in cities and villages and the study of the sizes of oil wells as well as in the field of communication through the speed of downloading files from the Internet according to their sizes. This distribution is used in mechanical engineering as one of the distributions of models of failure, stress and durability. Given the practical importance of this distribution on the one hand, and the scarcity of sources and statistical research that deal with it, this research touched on some statistical characteristics such as derivation of its mathematical function , probability density

A true random TTL pulse generator was implemented and investigated for quantum key distribution systems. The random TTL signals are generated by low cost components available in the local markets. The TTL signals are obtained by using true random binary sequences based on registering photon arrival time difference registered in coincidence windows between two single – photon detectors. The true random TTL pulse generator performance was tested by using time to digital converters which gives accurate readings for photon arrival time. The proposed true random pulse TTL generator can be used in any quantum -key distribution system for random operation of the transmitters for these systems

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the