ABSTRACT

This study was conducted to determine the effect of various levels of hump fat (HF) used in manufacturing of camel, beef and chicken sausage to understand the effect of (HF) on physicochemical composition sausage, Different levels of hump fat (5, 7, and 10 %) were used, physicochemical compositions like (moisture, protein, fat, Ash, water holding capacity, shrinkage, cooking loss and pH) were determined. Results of the study revealed that moisture content showed high significant differences (P≤0.01)among treatments groups, Camel sausage and beef sausage tended to have highest values while chicken sausage reported the lowest value. The study showed no significant difference (P≤0.05) among the

Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

    This paper aims to study the damage generated due to creep-fatigue interaction behaviors in solid polyamide 6,6 and its composites that include 1%wt of carbon nanotubes or 30% wt short carbon fiber prepared by an injection technique. The investigation also includes studying the influence of applied temperatures higher than the glass transition temperatures on mechanical properties. The obtained results showed that the addition of reinforcement materials increased all the mechanical properties, while the increase in test temperature reduced all mechanical properties, especially for polyamide 6,6. The creep-fatigue interaction resistance also improved due to the addition of reinforcement materials by inc

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

Background: techniques of image analysis have been used extensively to minimize interobserver variation of immunohistochemical scoring, yet; image acquisition procedures are often demanding, expensive and laborious. This study aims to assess the validity of image analysis to predict human observer’s score with a simplified image acquisition technique. Materials and methods: formalin fixed- paraffin embedded tissue sections for ameloblastomas and basal cell carcinomas were immunohistochemically stained with monoclonal antibodies to MMP-2 and MMP-9. The extent of antibody positivity was quantified using Imagej® based application on low power photomicrographs obtained with a conventional camera. Results of the software were employed