Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

Moisture content prediction of Iranian wheat using dielectric technique.

The moisture measurement is the most important criteria in harvesting and post harvesting processing of wheat. This paper investigates the moisture dependent dielectric constant and calibration equation of three most cultivated wheat varieties in Iran. An instrument with a cylindrical capacitive sensor was used to measure the dielectric constant of grain at different moisture contents. Samples were prepared at five levels of moisture content. To validate the proposed equations, the dried material dielectric constant was estimated and compared with the measured ones. Homographic regression models showed an acceptable prediction of dried wheat dielectric constant. Results showed that the calibration equation obtained for Shahriar variety was able to predict the moisture content of other varieties confidently. The lowest value of R (2) = 0.985 between predicted and reference moisture content for Tajan variety is still a good result.

Evaluation of liver diseases in Iranian patients with primary antibody deficiencies.

INTRODUCTION: Patients with primary antibody deficiency (PAD) can complicate with liver disease. This study was performed in order to study the prevalence and causes of hepatobiliary diseases in Iranian patients with PAD. MATERIAL AND METHODS: Sixty-two patients with PAD were followed-up and signs and symptoms of liver disease were recorded. All patients were screened for hepatitis C virus (HCV-RNA) and those patients with any sign of liver disease or gastrointestinal complaints were tested for Cryptosporidium parvum. RESULTS: Clinical evidences of liver disease, including hepatomegaly, were documented in 22 patients (35.5%). Eight patients (13%) had clinical and/or laboratory criteria of chronic liver disease. Only one patient was HCV-RNA positive; he had stigmata of chronic liver disease and pathologic evidence of chronic active hepatitis with cirrhosis. Cryptosporidium parvum test was positive for one patient with hyper-IgM syndrome. In liver biopsy of patients with liver involvement, one had histological findings related to sclerosing cholangitis, and five had mild to moderate chronic active hepatitis with unknown reason. CONCLUSIONS: Chronic active hepatitis is the most common pathologic feature of liver injury in Iranian patients with PAD. Liver disease in PAD usually accompanies with other organ involvements and could increase the mortality of PAD. Whether this high rate of liver disease with unknown origin (75%) is the result of an unidentified hepatotropic virus or other mechanisms such as autoimmunity, is currently difficult to understand.