A field experiment was conducted during winter, 2015-16 with the objective to investigate the effect of bread wheat cultivars (Abu-Ghraib3, Ibaa99, and Alfeteh) and seed priming 100, 100, 150 mg L-1 of benzyl adenine, salicylic acid, gibberellic acid (GA3), respectively, ethanolic extract of Salix Sp., water extract of Glycyrrhiza glabra and distilled water (control) on grain growth rate (GGR), effective filling period (EFP) and accelerating of physiological maturity. Randomized complete block design with three replicates was applied. GA3×Ibaa99 surpassed others in grain yield (7.432 tonne ha-1) when gave the highest grain weight (45.13 mg grain-1) and GGR (1.5 mg grain-1 day-1) with the fastest time to start and end EFP (5 and 34 days), which mean it reached to physiological maturity earlier. It can be conclude that seed priming led to accelerating the physiological maturity with increase grain yield through enhancing GGR and EFP in bread wheat.
Three cultivars of the crop Almash (Green Indian VC6089A10, Green Indian VC6173B1319, and Black Indian Gold Star) were tested in a field experiment during the 2022 growing season in Ramadi, Anbar province, to determine the impact of spraying levels of zinc (0, 25, and 50) mg Zn L-1 and manganese (0, 30, and 60) mg Mn L-1 on some growth characteristics. The experiment was conducted using a randomized complete block design (RCBD) with three replicates, with each treatment being tested in a separate split plot. The study found that there were statistically significant differences between zinc levels, with the level giving 50 mg Zn L-1
يؤدي عرض معلومات مضللة او محرفة ضمن القوائم المالية والتي تعد أهم مصادر المعلومات الموثوقة التي يُعول عليها لاتخاذ القرارات السليمة الى عدم قدرتها على عكس نتيجة النشاط والمركز المالي لها او اعمال الوحدة الاقتصادية لتلك الفترات الزمنية بصورة صادقة وعادلة نتيجة لنوعية المعلومات المفصح عنها في القوائم المالية لذلك زاد الاهتمام بتطوير الممارسات المحاسبية لتتضمن افصاحات كافية بغرض اعطائهم صورة صادقة وعادلة
... Show MoreOver the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.
The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame
In this paper, a Cholera epidemic model is proposed and studied analytically as well as numerically. It is assumed that the disease is transmitted by contact with Vibrio cholerae and infected person according to dose-response function. However, the saturated treatment function is used to describe the recovery process. Moreover, the vaccine against the disease is assumed to be utterly ineffective. The existence, uniqueness and boundedness of the solution of the proposed model are discussed. All possible equilibrium points and the basic reproduction number are determined. The local stability and persistence conditions are established. Lyapunov method and the second additive compound matrix are used to study the global stability of the system.
... Show MoreRecently, Qatar, a well-known oil production country, has been convinced as a successful case in attracting foreign direct investment (FDI) as a smaller economy. This paper aims to investigate how FDI inflows affect Qatar’s business cycles. Time series data was selected from 1990 to 2010 as available. The VAR Impulse Responses and the Granger Causality test were mainly employed by using Eviews. The derived result shows that the FDI inflows and the economic growth in Qatar interact with each other in a relatively long term.
A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
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The research dealt with a studying the impact of oil price fluctuations on one of the rules of financial discipline, which is the rule of budget deficit in the Iraqi economy for the period (2003-2020) as it is one of the quarterly economies that rely mainly on volatile oil revenues that fluctuate with oil prices in global markets, and therefore the general budget suffers. from The state of instability and then the government resorts to borrowing for a long time . this deficit in the general budget and increase the debt burden in the public debt.The research aim to measure and study the impact of oil price flu
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This paper analyses the relationship between selected macroeconomic variables and gross domestic product (GDP) in Saudi Arabia for the period 1993-2019. Specifically, it measures the effects of interest rate, oil price, inflation rate, budget deficit and money supply on the GDP of Saudi Arabia. The method employs in this paper is based on a descriptive analysis approach and ARDL model through the Bounds testing approach to cointegration. The results of the research reveal that the budget deficit, oil price and money supply have positive significant effects on GDP, while other variables have no effects on GDP and turned out to be insignificant. The findings suggest that both fiscal and monetary policies should be fo
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