Degradation of soil quality is an inevitable consequence of modifications to the characteristics of the soil that contribute to a decrease in ecosystem services. Numerous stressors, including chemical, biological, and physical ones, as well as those originating from both natural and artificial sources. The most prevalent kind of soil contamination that contaminates soil biota is agrochemicals. Soil is the most common place for xenobiotic dumping, which makes it the most probable source of other natural resources' pollution, such as surface and ground waters, based on the results of several studies. The danger to the environment posed by polluted soils is influenced by a variety of biological and physicochemical mechanisms that regulate the

Degradation of soil quality is an inevitable consequence of modifications to the characteristics of the soil that contribute to a decrease in ecosystem services. Numerous stressors, including chemical, biological, and physical ones, as well as those originating from both natural and artificial sources. The most prevalent kind of soil contamination that contaminates soil biota is agrochemicals. Soil is the most common place for xenobiotic dumping, which makes it the most probable source of other natural resources' pollution, such as surface and ground waters, based on the results of several studies. The danger to the environment posed by polluted soils is influenced by a variety of biological and physicochemical mechanisms that regul

Objective : Alzheimer’s disease (AD) continues to be a major challenge because handling high-dimensional data is time-consuming and expensive due to its complexity. A large feature space often increases computational costs and reduces model interpretability. This study addresses this problem by evaluating and comparing multiple feature selection techniques to identify the most informative biomarkers for AD diagnosis.

Methods : Our study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to implement and test three feature selection a

               The research aimed at measuring the compatibility of Big date with the organizational Ambidexterity dimensions of the Asia cell  Mobile telecommunications company in Iraq in order to determine the possibility of adoption of Big data Triple as a approach to achieve organizational Ambidexterity.

The study adopted the descriptive analytical approach to collect and analyze the data collected by the questionnaire tool developed on the Likert scale After  a comprehensive review of the literature related to the two basic study dimensions, the data has been subjected to many statistical treatments in accordance with res

Hsieh-Wildenthal (HW) and G-matrix PF1, (GXPF1) interactions for 40Ca and 48Ca, respectively. The 40Ca was considered to be consisted of 32S as a core and 1d3/21f7/2 subshells as a model space. The 48Ca was considered to be consisted of 40Ca as a core and 1f7/22p3/21f5/2 2p1/2 subshells as a model space. The obtained one body-density matrix elements (OBDME) for both interactions for the studied Coulomb transitions:  For 40Ca, the three non-normal parity transitions (C3): ,  and  and one C5 transitions,  .  For 48Ca, the two C2 transitions:  and  and one C4 transitions  were used in parallel with the transformed harmonic-oscillator basis to compute charge density, elastic electron scattering differential cross-section and ela

Sequencing technologies have reshaped the study of the subgingival microbiome, but selecting the appropriate method remains challenging because of differences in resolution, cost, host DNA contamination, and computational complexity. This review compares 16S rRNA sequencing, full-length 16S, shotgun metagenomics, and metatranscriptomics with respect to taxonomic resolution, functional output, sample requirements, and analytical limitations. Key practical issues, including low microbial biomass, contamination control, and the choice of appropriate bioinformatic tools, are emphasized to help researchers avoid common pitfalls. A decision-making framework is provided to link study goals to suitable sequencing methods while outlining rea

In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H