Most studies indicated that the values of atmospheric variables have changed from their general rates due to pollution or global warming etc. Hence, the research indicates the changes of direct solar radiation values over a whole century i.e. from 1900 to 2000 depending on registered data for four cities, namely (Mosul - Baghdad - Rutba - Basra. Moreover, attemptsto correlate the direct solar radiation with the temperature values have been recorded over that period. The results showed that there is a decreasing pattern of radiation quantities over time throughout the study period, where the value of direct radiation over the city of Baghdad 5550 w/m2 was recorded in the year 1900, but this ratio decreased clearly to approximately 5400 w/m2 in the year 2000, which is perhaps due to the increase of general pollution rates in the atmosphere. The results also showed that the city of Rutba recorded the highest annual rate of radiation quantities, and Baghdad with Mosul recorded the lowest radiation ratios compared to the rest of the cities, in addition to that there was a large convergence of radiation ratios between them to some extent. The results showed that there is a sharp drop in radiation ratios, specifically in the years1980and 1990. As for the extent of the relationship for solar radiation with temperature levels throughout the study period, it was found that there is an inverse relationship between them, and this confirms that the cause of high temperatures is not because of increased solar radiation, but it may rather due to other reasons such as increased greenhouse gases.
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
For any group G, we define G/H (read” G mod H”) to be the set of left cosets of H in G and this set forms a group under the operation (a)(bH) = abH. The character table of rational representations study to gain the K( SL(2,81)) and K( SL(2, 729)) in this work.
A factor group is a mathematical group obtained by aggregating similar elements of a larger group using an equivalence relation that preserves some of the group structure. In this paper, the factor groups K(SL(2,121)) and K(SL(2,169)) computed for each group from the character table of rational representations.
The group for the multiplication of closets is the set G|N of all closets of N in G, if G is a group and N is a normal subgroup of G. The term “G by N factor group” describes this set. In the quotient group G|N, N is the identity element. In this paper, we procure K(SL(2,125)) and K(SL(2,3125)) from the character table of rational representations for each group.
The current research aims to show the impact of the international auditing standard IAS 540 in reducing income smoothing practices in Iraq. To achieve the objectives of the research, the researcher adopted a questionnaire for a sample of auditors in Iraq. Where 60 forms were distributed and after the questionnaire was retrieved and statistical analysis was done using the SPSS program, The research reached a number of results, the most important of which are: the existence of a statistically significant effect of the application of the international auditing standard IAS 540 in reducing income smoothing practices, The research recommended the necess
... Show MoreIn this paper a dynamic behavior and control of a jacketed continuous stirred tank reactor (CSTR) is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time.
The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller.
The results s
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