The study of economic growth indicators is of fundamental importance in estimating the effectiveness of economic development plans, as well as the great role it plays in determining appropriate economic policies in order to optimally use the factors that lead to the dynamics of growth in Iraq, especially during a certain period of time. The gross domestic product (GDP) at current prices), which is considered a part of the national accounts, which is considered as an integrated dynamic of statistics that produces in front of policy makers the possibility of determining whether the economy is witnessing a state of expansion or evaluating economic activity and its efficiency in order to reach the size of the overall economy. The research aims to determine the best and most efficient statistical model to be used in forecasting the GDP in Iraq based on time series data for the period from (1970-2020) years. Where the general trend models (Linear trend, Quadratic trend and Exponential Trend) were applied, and the three models were compared to choose the best model using some statistical criteria, including the Akiaki Information Standard (AIC) and Schwartz Standard (SBS). The results showed that the appropriate model is the Quadratic trend model, were predicting and forecasting values are close to the real values of the GDP series.
In this study, an analytical model depending on experimental results for InPInGaAs
avalanche photodiode at low bias was presented and the characteristics of
gain for this photodiode were determined directly by the impulse response. The
model have considered the most important mechanisms contributing the
photocurrent, they are trapping, photogeneration in the undepleted region and
charge-carriers velocity due to the built-in electrical field. Also, the bandwidth
was determined as a function to the total gain of photodiode and it was mainly
determined by diffusion and trapping processes at low gain regarding to the multilayer
structure considered in this study
This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback
... Show MoreToday with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned
The Iraqi sports journalism has paid great attention to sports events through press coverage of all its forms and arts, especially the coverage of the World Cup of football which is one of the most watched events in the world. Thousands of journals are preparing for the immediate coverage of such event which is a daunting task in itself. Newspapers have devoted a wider space to this great event in its pages as well as the weakly sports newspapers work on issuing a daily special issue. The importance of this research sheds light on the coverage of major events such as World Cup in Iraqi newspapers. The topic is new
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