Fish are regarded as a crucial indicator of alterations in the aquatic environment due to their position at the apex of the food chain. Monitoring these alterations is crucial for identifying modifications in the aquatic ecosystem. The principal elements influencing fish health are temperature, pH, dissolved oxygen, salinity, pesticide contamination, microplastics, and algal presence. These elements substantially influence fish health regarding development, reproduction, respiration, oxygen stress, and the internal enzymes associated with digesting and other metabolic functions. Alterations in global environmental conditions and anthropogenic pollutants result in modifications to fish populations, their lives, and their behavior and

Information pollution is regarded as a big problem facing journalists working in the editing section, whereby journalistic materials face such pollution through their way across the editing pyramid. This research is an attempt to define the concept of journalistic information pollution, and what are the causes and sources of this pollution. The research applied the descriptive research method to achieve its objectives. A questionnaire was used to collect data. The findings indicate that journalists are aware of the existence of information pollution in journalism, and this pollution has its causes and resources.

The current study included, studying the ability of eight genera of plants belong to Brassicaceae family, Brassica tournifortii, Cakile Arabica, Capsella bursa – pastoris,Carrichtera annua, Diplotaxis acris, Diplotaxis haru , Eruca sativa and Erucaria hispanica to accumulate ten heavy metals Cadmium, Chromium , Copper, Mercury, Manganese ,Nickel ,Lead ,and Zinc . Plant leaves samples were collected from Al-Tib area during spring of 2021.The data demonstrated that, the highest conc. of Cd was 2.7 mg/kg in Diplotaxis acris leaves and lower value was 0.3 mg/kg in Cakile Arabica leaves. For Co, the highest conc.was 1.3 mg/kg in Capsella bursa – pastoris leaves, whereas the lower value was 0.5 mg/kg in Cakile arabica leaves. As for Cr ele

The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.    

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator

Transforming the common normal distribution through the generated Kummer Beta model to the Kummer Beta Generalized Normal Distribution (KBGND) had been achieved. Then, estimating the distribution parameters and hazard function using the MLE method, and improving these estimations by employing the genetic algorithm. Simulation is used by assuming a number of models and different sample sizes. The main finding was that the common maximum likelihood (MLE) method is the best in estimating the parameters of the Kummer Beta Generalized Normal Distribution (KBGND) compared to the common maximum likelihood according to Mean Squares Error (MSE) and Mean squares Error Integral (IMSE) criteria in estimating the hazard function. While the pr

  Weibull distribution is considered as one of the most widely  distribution applied in real life, Its similar to normal distribution in the way of applications, it's also considered as one of the distributions that can applied in many fields such as industrial engineering to represent replaced and manufacturing time ,weather forecasting, and other scientific uses in reliability studies and survival function in medical and communication engineering fields.

   In this paper, The scale parameter has been estimated for weibull distribution using Bayesian method based on Jeffery prior information as a first method , then enhanced by improving Jeffery prior information and then used as a se

Phenytoin selective electrodes were constructed based on penytoin-phosphotungstate (Ph-PT) complex with different plasticizers; di-butyl phosphate (DBP), tri-butyl phosphate (TBP), di-butyl phthalate (DBPH),and o-nitro phenyl octyl ether (NPOE) phthalate. The electrodes based on DBPH, ONPOE plasticizers gave Narnistain slope which are, 56.4 and 55.3mV/decade with detection limit of 1.9x10-5 M , 1.8x10-5 and concentration range 10-1 to 10-4 M and pH range 3.0 – 8.0. The electrodes based on TBP and DBP showed non-Nernistain slopes, 40.2,40.5 mV/decade for both plasticizers. Interfering of some cations was investigated and shows no interfering with electrodes response. Potentiometric methods were used for measuring phenytion in

Interval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an ef