Visualization of Droplet Dynamics in Cloud

Data visualization uses charts, graphs and maps to illustrate some information. Visualizing data helps us understand information faster. The study of droplet dynamics is a critical part of cloud physics and includes studying droplet properties. The aim of this work is to visualize the droplet dynamics obtained from DNS (Direct Numerical Simulation) data due to evaporation and condensation of the droplets. This simulation contains coupled Eulerian and Lagrangian frames. Animation is created for both Eulerian grid data and Lagrangian droplet movement. Scientific visualization provides a way to analyze these turbulent properties in a part of a cloud and learn about the nature of droplets and mixing process in such highly turbulent areas

Visualization of Droplet Dynamics in Cloud

Data visualization uses charts, graphs and maps to illustrate some information. Visualizing data helps us understand information faster. The study of droplet dynamics is a critical part of cloud physics and includes studying droplet properties. The aim of this work is to visualize the droplet dynamics obtained from DNS (Direct Numerical Simulation) data due to evaporation and condensation of the droplets. This simulation contains coupled Eulerian and Lagrangian frames. Animation is created for both Eulerian grid data and Lagrangian droplet movement. Scientific visualization provides a way to analyze these turbulent properties in a part of a cloud and learn about the nature of droplets and mixing process in such highly turbulent areas.</jats:p

Diabetes & Heart Disease Prediction Using Machine Learning

One of the root causes of mortality in today's world is the culmination of several heart disease and diabetes illnesses. In clinical data analysis, predicting multiple diseases is a significant challenge. The machine learning approach has proved to be functional in assisting in the decision-making and governing of large amounts of data generated by the healthcare field. The various experiments scratch the surface of machine learning to predict different diseases. The papers present a novel method for identifying significant features using machine learning techniques, which improves the diagnosis of multi-purpose disease prediction. The different features and many well-known classification methods are used to implement the prediction model to predict the heart disease and diabetes. The proposed method utilizes ensemble approach for achieving a higher degree of accuracy rates for by using classification algorithms and feature selection methods. The proposed method implements voting classifier that has sigmoid SVC, AdaBoost, and Decision tree algorithms. The paper also implements the traditional classifiers and presents the comparison of different models in terms of accuracy. The web application is also developed for users to avail its services very easily and make it convenient for their use, particularly in the prediction of heart and diabetes collectively

Diabetes &amp; Heart Disease Prediction Using Machine Learning

One of the root causes of mortality in today's world is the culmination of several heart disease and diabetes illnesses. In clinical data analysis, predicting multiple diseases is a significant challenge. The machine learning approach has proved to be functional in assisting in the decision-making and governing of large amounts of data generated by the healthcare field. The various experiments scratch the surface of machine learning to predict different diseases. The papers present a novel method for identifying significant features using machine learning techniques, which improves the diagnosis of multi-purpose disease prediction. The different features and many well-known classification methods are used to implement the prediction model to predict the heart disease and diabetes. The proposed method utilizes ensemble approach for achieving a higher degree of accuracy rates for by using classification algorithms and feature selection methods. The proposed method implements voting classifier that has sigmoid SVC, AdaBoost, and Decision tree algorithms. The paper also implements the traditional classifiers and presents the comparison of different models in terms of accuracy. The web application is also developed for users to avail its services very easily and make it convenient for their use, particularly in the prediction of heart and diabetes collectively.</jats:p

Stabilization of Black Cotton Soil by Using Rice Husk and Bagasse Ash

Abstract: Black cotton soil is expansive type of soil that expands suddenly and starts swelling once it comes in contact with water. The strength of the soil is very poor due to its physical properties. Expansive soils exhibit improved response in behaviour with different types of stabilizers. Stabilization with admixtures is found to be an effective technique to improve the strength properties of the black cotton soil. During this study the potential of rice husk ash and bagasse ash are found to be useful admixtures to improve the strength properties of the expansive soil. The rice husk is an agricultural by-product from rice milling and bagasse ash is a sugarcane waste from sugar industry. In this research an approach is made to improve the properties of black cotton soil with combination of bagasse ash and rice husk ash. The results show substantial improvement in engineering properties of black cotton soil with the admixtures. Keywords: Black Cotton Soil, Rice Husk Ash, Bagasse Ash.</jats:p

Regulation of leukocyte binding to endothelial tissues by tumor-derived GM-CSF

The adherence of leukocytes to endothelial cells is the first step in the migration of these cells into tumor tissues. Specific binding to the endothelial cells by leukocytes is mediated by the development and maintenance of adhesion molecules on the endothelium; however, the mechanisms of leukocyte traffic into tumors and of their interactions with neoplastic tissue are not clearly understood. The infiltration of leukocytes occurs in most spontaneous and transplanted solid tumors and we have previously reported that not only are murine mammary tumors heavily infiltrated by leukocytes but tumor‐derived factors alter the development and function of leukocytes in tumor‐bearing mice. We now present evidence that a tumor‐derived cytokine, namely granulocyte‐macrophage‐colony‐stimulating factor, appears to be of importance in the regulation of leukocyte binding to endothelial cells. The data suggest that tumor‐derived factors may influence leukocyte trafficking within tumor tissue

Views, use and reception of visualisations of development proposals impacting cultural heritage

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