Triphasic CT Liver Characterization and Color Data Fusion

The aim of the present work is the analysis and mining of the informative content related to pathological liver tissues, when acquired by triphasic CT, with the proposal of a data-fusion approach, which is able to visualize and show up such a content in the best way as a support to the medical diagnosis. Since the huge amount of CT volumes to be analyzed in a limited time is the major cause of sensitivity loss during the diagnosis process, a better chance of detection and localization of the pathology can be derived from the method here proposed. This method can be a valid support to the current medical practice, even in the cases where pathology is at the very early stage and has a large probability to be missed by a visual inspection. As expected when analyzing the three phase volumes, one can note that the injection of a contrast agent causes significant changes in the radiological finding for both pathological and healthy parts of the liver. Thanks to a specific statistical analysis performed on a training dataset of real cases, the described study was focused on the characterization of hepatocellular carcinoma (HCC) tumor tissues and liver tissues. In order to detect and discriminate tumor from liver parenchyma, we here propose using both steady-state and dynamic features. Some common patterns have been observed suggesting rules, which have been confirmed by radiology specialists. Based on the rules and the best discriminant power of some of the characterizing features, a new color data fusion approach is then proposed and discussed which improves the mass visibility while increasing contrast with respect to surrounding parenchyma

Extracting hidden patterns in blood donor database using association rule mining

Data mining, in contrast to traditional data analysis, is discovery driven. Most of the recent data mining tools provide automatic pattern recognition and attempt to uncover patterns in data that are difficult to detect by traditional statistical methods. Most of the result patterns are not meaningful to the domain users at all. As a matter of fact, users have also to cope with the painful process to select patterns that are of actual interest from the result set. In this paper we are using the Apriori algorithm in association rules with the aim of helping experts in the blood organizations to find the best donor subjects within the whole population. We choose patterns according to rules that have some interest for the experts, or rules with values higher than a given threshold. For this purpose, we start from 7 input attributes such as: Age, sex, level of education, marital status, job, level of income and type of donor. On the basis of the results achieved, we are able to find interesting patterns from blood databases showing the healthiest donors within the whole population. Detecting the healthiest donors can guarantee a better quality of blood and blood products can prevent the environmental pollution and save the Blood Transfusion Organization the cost necessary to find the best population

Investigation of an Iron Particle Behavior in Flame Zones of Dust Combustion

Abstract: At present study, physics and dynamics of iron particles in combustion chamber crossing the flame zones of (iron-air) suspension is investigated. The effect of Gravity, drag and thermopherotic force are considered on an iron particle. Theoretical estimation for velocity profile in this study has been compared to an experimental study on velocity and concentration profile of iron particles across the flame propagating through the particles cloud

Prediction of healthy blood with data mining classification by using decision tree, na\uefve Bayesian and SVM approaches

Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Na\uefve Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood

Nanoparticles in drilling fluid: A review of the state-of-the-art

Nanoparticles (NPs) as a nanotechnologies unit have a huge potential for improving drilling fluids. However, the role of NPs in this field is still in its infancy and consequently has attracted much more attention in the last years. This review is going to investigate the drilling fluids modified by nanoparticles. Moreover, effects of various nanoparticles include polymeric, ceramic, metal and carbon-based NPs on drilling fluid and technical and economic benefits of them will be inspected. Although various reviews of nano-based drilling fluids have been reported, few papers have provided a comprehensive review and development of nanoparticles application in this issue. This review summarizes the recent research advances in the synthesis and applications of NPs in drilling fluids system. The roles of NPs in rheology and fluid loss control, mud cake thickness, filtration properties, and thermal properties are discussed. Accordingly, various literature reviews demonstrated that use of nano materials in drilling fluid has two main goals: improvement of thermal and physical-mechanical of drilling fluids. The studies in this issue will facilitate the design of advanced functional nano-composites for drilling fluids