Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed to evaluate effect of each of these variables on estimation process. Two error statistics namely root mean squared error and coefficient of determination were used to measure the performance of the developed models. The results indicated that the model, whose input variables are T, W, and RH, perform the best for estimating evaporation values. In addition, the model which is dominated by (T) is significantly and distinctly helps to prove the predictive ability of fuzzy inference system. Furthermore, agreements of the results with the observed measurements indicate that fuzzy logic is adequate intelligent approach for modeling the dynamic of evaporation process.
The family Chalcididae (Order: Hymenoptera) is known as one of the large chalcidoid wasps with some distinct morphological characters. The first occurrence of two parasitoid species belonging to this family was reported in the Al-Husayniya district Karbala Province, Iraq; which are: Brachymeria podagrica (Fabricius, 1787) and Chalcis myrifex (Sulzer, 1776). Both species were collected by using the sweeping net from orchards during July 2020.
The aim of the research to highlight the calendar of the most important tools used by the Central Bank of Iraq, in the implementation of the function of supervisory oversight, to verify the stability of the banking system, and protect the funds of shareholders, and depositors in general and the absence of any raises the risks of default and financial failure in particular, for commercial banks. The most important flaws and weaknesses in these tools, in the early detection of the risks of continuity in a timely manner, The study concluded a set of conclusions, including the weakness of the tools used in the performance of the function of supervisory oversight in detecting cases of default and financial failure in the early time as well as
... Show MoreThis research aims to focus on the reality of the imbalance of the balance of trade structure in order to improve it and determine the size of the imbalance as a result of dependence on one commodity, namely crude oil in the structure of exports versus the diversity of the structure of imports of various goods and goods.
In order to achieve that goal, a deductive approach was adopted, which included a shift from general theory data to special applications.
We have reached through the research to a number of conclusions, most notably the effectiveness of public spending in correcting the imbalance of the balance of trade structure during the study pe
... Show MoreThis study aimed to statement jet stream and its impact in the anti-cyclone affecting the climate of Iraq. Through the use of simple correlation coefficient ( Pearson ) that there is a very strong relationship between high- Siberian and both of the jet stream especially during the winter or over the stations of North . Therefore we, observe the relationship be significant in most of the winter months , spring and autumn . Statistically significant , but are different between station and another station , while the study come to another Anti-ciyclon have a real ,significant and statistically relationship corrclation . But this relationship is less than which found in are much less it with the Siberian high , it depends on the type of stat
... Show MoreA total of 335 suspected fecal sample were collected from calf of cattle and buffalo with age in between (3 days to 4 months) from middle area of Iraq between November 2016 to May 2017.
Cryptosporidium is a protozoan parasite of medical and veterinary significance that causes gastroenteritis in a number of vertebrate hosts. Several studies have recorded different degrees of pathogenicity and virulence among Cryptosporidium species and isolates of the same species as well as evidence of variation in host susceptibility to infection. Nevertheless, important progress has been made in determining Cryptosporidium's putative virulence factors. Since the publication of C parvum and C. Hominis this development has been accelerated genomes, identified by a range of immunological and molecular techniques with the characterization of over 25 putative virulence factors, which are proposed to be involved in aspects of host-pat
... Show MoreThe main objectives of present study are to evaluate the trace elements pollution in the sediment of the Tigris River and drainage canals in Wasit Governorate, Iraq. Assessment of trace elements pollutants were conducted for 18 sediment samples collected in March 2017. Trace elements were analyzed in sediment Tigris River samples in Wasit Governorate. This metal pollution was evaluated using geo-accumulation (I-geo) index, Contamination Factor (CF) and Pollution Load Index (PLI). According to these statistical indices, the sediments collected from Tigris River in the study area are highly polluted with Titanium (71.9 ppm), Nickel (226.6 ppm) Chromium (425.2 ppm), Cadmium (2ppm) and Molybdenum (15.8 ppm) while the sediments&nb
... Show MoreThe mechanism for selecting the President of the Republic and his constitutional powers under the Constitution of the Republic of Iraq
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