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

By- products of corn starch industry were used to prepare media for propagation the lactic acid bacteria as a natural auxotroph. The by- products used were the corn steep water (S) and gluten extract (G) after a proper treatment to get them ready for media preparation. The results showed that it was possible to replace the peptone and meat extract by gluten extract in MRS medium. The growth was approximately similar to that obtained in standard MRS media. Corn steep water (S) was used as well and the growth enhanced by including Tween – 80 at 1% level. The later media named MZ, which was superior for growing standard and local strains and starters. The MZ medium modified by adding acetate and glacial acetic acid similarly to

The study aims to select suitable ornamental plant species that can survive relatively with high concentrations of acetaminophen and methylparaben in constructed wetlands. Alternanthera spp, Asparagus aethiopicus and Chlorophytum comosum are examined to withstand three initial concentrations, 20, 100 and 200 mg/L of acetaminophen and methylparaben. A total of 21 plastic pails with each 3 L capacity consisting of nine pails are used for each pharmaceutical and personal care products (PPCPs) compounds (acetaminophen and methylparaben) for three ornamental plants (Alternanthera spp, Asparagus aethiopicus and Chlorophytum comosum), with three pails as plant controls. The results reveales

Clostridioides difficile is an emerging pathogen of One Health significance. Its highly variable genome contains mobile genetic elements (MGEs) such as transposons and prophages that influence its biology. Systematic deletion of each genetic element is required to determine their precise role in C. difficile biology and contribution to the wider mobilome. Here, Tn5397 (21 kb) and ϕ027 (56 kb) were deleted from C. difficile 630 and R20291, respectively, using allele replacement facilitated by CRISPR-Cas9. The 630 Tn5397 deletant transferred PaLoc at the same frequency (1 × 10⁻⁷

The dose rate for bremsstrahlung radiation from beta particles with energy (1.710) MeV and (2.28) MeV which comes from (32P and 90Y) beta source respectively have been calculated through six materials (polyethylene, wood, aluminum, iron, tungsten and lead) for first shielding material with thickness (x=1) mm which are putting between beta sources and second shield (polyethylene, aluminum and lead) with thickness (1, 2 &4) mm have been calculated. The distance between beta source and second shield is constant (D=1) cm. This dose rate was found by program called Rad Pro Calculator (version 3.26). The results of dose rate of beta particles were plotted as a function to the atomic number (Z) for first shield materials for each

The present study investigates the characterization of silver nanoparticles (AgNPs) synthesized using Fusarium solani and their impact on tomato seed germination, plant growth, and disease resistance. A visible color change from yellow to dark smoky indicated the formation of AgNPs, while UV-visible spectrophotometry revealed an absorbance peak at 437 nm, confirming their presence. Atomic force microscopy analysis showed that the AgNPs ranged from 0 to 39.27 nm in size, with an average height of 5.772 nm, while scanning electron microscopy highlighted their diverse surface morphology. The application of AgNPs and mycorrhizal fungi significantly improved tomato seed germination rates, plant height, and dry weight compared to untreate

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

Any design subject to a set of forces contributing to the establishment of relations working to strengthen the internal elements of the design; any imbalance in these elements can make a fragmented and weak design, thus preventing it from achieving the goal or performance. Poor performance can be attributed to various factors: the extent and function of the elements and principles in the design, realization of the idea, especially in fashion design.
Moreover, there are many aspects of a design that go into achieving the realization of the designer’s idea. The design utilizes a lot of stimulants by drawing attention to its design, which is consistent with the need for psychological and material individuals. In this research, we will