Phosphorus (P) is an element that is potatoes require in large amounts. Soil pH is a crucial factor impacting phosphorus availability in potato production. This study was conducted to evaluate the influence of P application rates on the P efficiency for tuber yield, specific gravity, and P uptake. Additionally, the relationship between soil pH and total potato tuber yield was determined. Six rates of P fertilization (0–280 kg P ha−1) were applied at twelve different sites across Northern Maine. Yield parameters were not responsive to P application rates. However, regression analysis showed that soil pH was significantly correlated with total potato tuber yield(R2 = 0.38). Sites with soil pH values < 6 had total tuber yields,

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

This research aims to study and evaluate the reality of the Iraqi banks in terms of how they cope with the risks of the banking business, specifically banking operational risks, and to develop a model integrated to define, identify, measure and mitigate the impact of these risks on according to the Basel Committee requirements II about the dangers of Alchgal.uchir major search to the presence of weak results in the Iraqi banks in understanding and defining and measuring operational risks and not hedged properly, which avoids those banks operating losses as well as the results show there is a shortage in the equation of capital adequacy applied by the Iraqi banks because of non-observance of the minimum capital required to counter the ris

One of the most important problems facing the world today is the energy problem. The solution was in finding renewable energy sources such as solar energy. The solar energy applications in Iraq is facing many problems . One of the most important problems is the accumulation of dust on the solar panels surface which causes decreasing its performance sharply. In the present work, a new technique was presented by using two-axis solar tracking system to reduce the accumulated dust on the solar panel surface and compared it with the fixed solar panels which installed at tilt angles 30° and 45°. The results indicated that the maximum losses of the output power due to accumulation of dust on the fixed solar panels is about 31.4% and 23.1% res

The real and imaginary part of complex dielectric constant for InAs(001) by adsorption of oxsagen atoms has been calculated, using numerical analysis method (non-linear least square fitting). As a result a mathematical model built-up and the final result show a fairly good agreement with other genuine published works.

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a