For several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.
This study was conducted to investigate the presence of Staphylococcus aureus in the red and white meat available in local markets. They were selected ten samples of red and white meat randomly (Iraq, Saudi Arabia, Turkey, and Brazil) from different markets in Baghdad, and the results of reading the nutrition facts of media indication card showed that all models confirm to the Iraqi standard quality in terms of scanning all data of the media indication card, except for the birds of Bayader, where the date of expire & production date of the product was not mentioned. Also, the results of the study showed that there is no Staphylococcus aureus in local red and white meat as well as imported.
The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.
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
... Show MoreFive Saccharomyces cerevisiae isolated from the ability of chitinase production from the isolates were studied. Quantitative screening appeared that Saccharomyces cerevisiae S4 was the highest chitinase producer specific activity 1.9 unit/mg protein. The yeast was culture in liquid and solid state fermentation media (SSF). Different plant obstanases were used for (SSF) with the chitine, while liquid media contained chitine with the diffrented nitrogen source. The favorable condition for chitinase producers were incubated at 30 ºC at pH 6 and 1% colloidal chitine.
In order to scrutinize the impact of the decoration of Sc upon the sensing performance of an XN nanotube (X = Al or Ga, and XNNT) in detecting sarin (SN), the density functionals M06-2X, τ-HCTHhyb, and B3LYP were utilized. The interaction of the pristine XNNT with SN was a physical adsorption with the sensing response (SR) of approximately 5.4. Decoration of the Sc metal into the surface of the AlN and GaN led to an increase in the adsorption energy of SN from −3.4 to −18.9, and −3.8 to −20.1 kcal/mol, respectively. Also, there was a significant increase in the corresponding SR to 38.0 and 100.5, the sensitivity of metal decorated XNNT (metal@XNNT) is increased. So, we found that Sc-decorating more increases the sensitivity of GaNN
... Show MoreBackground: Candida tropicalis is one of the most causes of vulvovaginal candidiasis (VVC) in women. Systemic candidiasis and candidemia may also occur in pregnancies. Objective: This study was carried out to detect and isolate of this yeast from aborted placenta, which may cause severe complications such as spontaneous abortion. Materials and methods: Fresh aborted placenta were collected and washed by normal saline to remove the blood. Then, cut it into portions and place it in test tube containing 5 ml of normal saline. Finally, shake for 10 minutes, after that, cultured for microbial isolation. Isolation and detection were done by some conventional methods with Api candida and CHROMagar. Results: The results showed that four iso
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