Applications of quantitative methods, which had been explicit attention during previous period (the last two centuries) is the method of application sales man or traveling salesman method. According to this interest by the actual need for a lot of the production sectors and companies that distribute their products, whether locally made or the imported for customers or other industry sectors where most of the productive sectors and companies distributed always aspired to (increase profits, imports, the production quantity, quantity of exports. etc. ...) this is the part of the other hand, want to behave during the process of distribution routes that achieve the best or the least or most appropriate.

The Accounting Disclosure for non-current intangible assets is necessary to rely on accounting information by decision makers in the economic unity, two international accounting standards issued (IAS16,36), which aims to provide the foundations of the recognition, measurement and disclosure of appropriate assets Non-current tangible. (IAS16) allowed to use re-evaluation approach to measure assets entrance due to the inadequacy of the accounting information resulting from the application of the historical cost of the entrance under increasing technical developments and continuing that leave clear their effects on non-current intangible assets, As well as the requirements of what came (IAS36) the importance of accounting for the impairment

Simultaneous determination of Furosemide, Carbamazepine, Diazepam, and Carvedilol in bulk and pharmaceutical formulation using the partial least squares regression (PLS-1 and PLS-2) is described in this study. The two methods were successfully applied to estimate the four drugs in their quaternary mixture using UV spectral data of 84synthetic mixtures in the range of 200-350nm with the intervals Δλ=0.5nm. The linear concentration range were 1-20 μg.mL-1 for all, with correlation coefficient (R2) and root mean squares error for the calibration (RMSE) for FURO, CARB, DIAZ, and CARV were 0.9996, 0.9998, 0.9997, 0.9997, and 0.1128, 0.1292, 0.1868,0.1562 respectively for PLS-1, and for PLS-2 were 0.9995, 0.9999, 0.9997, 0.9998, and 0.1127, 0.

An optical spectroscopic study is reported in this article to study the correlation between the supermassive black hole (SMBH) and the star formation rate (SFR) for a sample of Seyfert galaxies type (I and II). The study focused on 45 galaxy of Seyfert 1, in addition to 45 galaxy of Seyfert 2, where these samples have been selected form different survey of Salon Digital Sky Survey (SDSS). The redshift (z) of these objects were between (0.02 – 0.26). The results of Seyfert 1 galaxies shows that there good correlation between the SMBH and the SFR depending on statistical analysis parameter named Spearman’s Rank Correlation in a factor of (ρ=0.609), as well as the Seyfert 2 galaxies results show a good correlation between the SMBH and

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

An optical spectroscopic study is reported in this article to study the correlation between the supermassive black hole (SMBH) and the star formation rate (SFR) for a sample of Seyfert galaxies type (I and II). The study focused on 45 galaxy of Seyfert 1, in addition to 45 galaxy of Seyfert 2, where these samples have been selected form different survey of Salon Digital Sky Survey (SDSS). The redshift (z) of these objects were between (0.02 – 0.26). The results of Seyfert 1 galaxies shows that there good correlation between the SMBH and the SFR depending on statistical analysis parameter named Spearman’s Rank Correlation in a factor of (ρ=0.609), as well as the Seyfert 2 galaxies results show a good correlation between the SMBH

Sorghum cultivation is often accompanied by low field emergence rates and weak seedlings, which may be due to genetic or environmental stress. A factorial experiment was conducted in the spring and fall seasons of 2022 using a randomized complete block design with split-plot arrangement and four replications. Planting dates (spring season: Feb. 15th, Mar. 1st, 15th, and Apr. 1st, 15th; fall season: Jun. 15th, Jul. 1st, 15th, and Aug. 1st, 15th) were allocated to the main plots. Seeds stimulation treatments (35% banana peel extract + 100 mg L-1 citric acid and distilled water soaking treatment only) were allocated to the subplots. The interaction treatment (banana peel extract + citric acid) with the planting date of April 15 showed the high

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m