Eimeriosis is a major problem affecting ruminants worldwide. The disease is primarily caused by Eimeria species, which are specialized for each host and grow in the small and large intestine of animals. The losses due to subclinical infections (especially weight loss) and clinical disease (diarrhea) make the species of this genus a very significant economic concern. Therefore, this study was conducted in some areas of Wasit Province. A total of 180 fecal samples from goats, of both sexes and covering different age groups and months, were collected. All fecal samples were examined microscopically, and 75 positive fecal samples were taken for molecular examination and further analyzed using conventional PCR, sequencing and phylogenetic analysis. Microscopic results showed that the overall infection rate was 41.6%. The incidence of Eimeria species ranged from 5.55% to 22.22% across three different species of the genus Emeria Schneider, 1875, namely E. arloingi (Marotel, 1905), Martin, 1909 (22.22%), E. christenseni Levine, Ivens & Fritz, 1962 (13.88%), and E. hirci Chevalier, 1966 (5.55%). Regarding the PCR reaction, results from the 18S rRNA, COI gene and genetic sequencing, Confirmed that the fecal samples were positive for Eimeria Schneider, 1875 species.
Adult of dipterous flies were collected monthly from exposed animals carcasses during the period from February 2006 to January 2007 in Baghdad city. The results obtained showed that flies could be collected all over the year with variation of their population density in different seasons. The majority of the collected species were abundant during Spring and late Autumn (at lowest numbers) . In this investigation, nineteen species confined to four families were collected; these families are: Calliphoridae, Muscidae, Sarcophagidae and Fanniidae. The species Musca domestica Linn .was the most abundant followed by Chrysomya megacephala ( Fabricius ) , while Pollenia sp . and Fannia sp . were the least abundant species.
In this paper, a subspace identification method for bilinear systems is used . Wherein a " three-block " and " four-block " subspace algorithms are used. In this algorithms the input signal to the system does not have to be white . Simulation of these algorithms shows that the " four-block " gives fast convergence and the dimensions of the matrices involved are significantly smaller so that the computational complexity is lower as a comparison with " three-block " algorithm .
New speaker identification test’s feature, extracted from the differentiated form of the wave file, is presented. Differentiation operation is performed by an operator similar to the Laplacian operator. From the differentiated record’s, two parametric measures have been extracted and used as identifiers for the speaker; i.e. mean-value and number of zero-crossing points.
The use of deep learning.