In recent years, lung tumor diagnosis and the projection of tumor segmentation in 3D has gained significant momentous in the therapeutic field. Establishing the dissimilarity exists in the three dimensional volume representation of tumor cells affords more information, which can sharpen the treatment of a multiplicity of tumors. The volume reconstruction information is indispensable in the case of surgical operations. This process introduces a contour based segmentation algorithm to acquire the appropriate differentiation of pixel boundary that scrutinizes the exact difference between tumor and non tumor cells along the tumor boundary. With the aid of aforementioned formulation, extracted tumor part pixels are reconstructed for the entire 2D slices of the patient data set. Proposal work on 3D voxel reconstruction relies on encountering the isosurfaces. Originally, volume data are subjected to the smoothening process which computes the isosurface data from the smoothened volume data. The generated outcome of this process comprises the vertices and faces of the isosurfaces and directly flows to patch the data. Exploit the 3D reconstructed model to enumerate the voxel damaged by tumor. Proposal work associated with the percentage of damaged voxel along with accurate and reliable perception, simplifies the physician task in lung tumor diagnosis and assist the surgical procedure. Experimental evaluations across the wide range of images show the superiority of the proposed work with the classification accuracy rate of 99.33%.

This paper aims to reveal a comparative analysis of classifier performance of MR brain images, particularly for the brain tumor detection and classification. The detection of brain tumor stands in need of Magnetic Resonance Imaging (MRI). The moment invariant feature extraction has been evaluated to categorize the MRI Slices as Normal, Benign and Malignant by Neural Network Classifier. In our comparative study, we examine the precision rate of aforementioned classification with extracted features and the classification of brain images with selected features by association rule based neural network classifier. The results are then analyzed with Receiver Operating Characteristics (ROC) curve and compared to illustrate the method producing higher accuracy rate in tumor recognition. Factually, our analysis proves that the classifier works below feature extraction followed by rule pruning method affords better accuracy rate.

The subject of the article is a scientist Milutin Milankovitch who proposed the theory of climate changes: slow changes and variations in the Earth\'s orbit throughout history coincide with the periods of deglaciations over the past million years. Milutin Milankovitch, a geophysicist and climatologist is best known for the most significant theory of the Ice Ages in the history of science. The American National Aeronautics and Space Administration (NASA) has ranked Milankovitch among the 15 best scientists in history who have shaped our civilization. Peter Huybers, a professor of Earth and Planetary Sciences at Harvard University, tested many hypotheses about climate changes, but he confirmed one that proposed Milankovitch. The Milankovitch Theory of climate changes states that slow changes and variations in the Earth\'s orbit throughout history coincide with the periods of deglaciations (gradual melting away of a glacier) over the past million years. While Peter Huybers was the first to test the connection between the Earth\'s orbital cycles and deglaciations, Milutin Milankovitch was the first to launch and mathematically prove this hypothesis in the first half of the Twentieth Century.

A fuel cell is a device that can directly transfer chemical energy to electric and thermal energy. Proton exchange membrane fuel cells (PEMFC) are highly efficient power generators, achieving up to 50-60% conversion efficiency, even at sizes of a few kilowatts. There are several compelling technological and commercial reasons for operating H2/air PEM fuel cells at temperatures above 100 °C; rates of electrochemical kinetics are enhanced, water management and cooling is simplified, useful waste heat can be recovered, and lower quality reformed hydrogen may be used as the fuel. All of the High Temperature PEMFC model equations are solved with finite element method using commercial software package COMSOL Multiphysics. The results from PEM fuel cell modeling were presented in terms of reactant (oxygen and hydrogen) concentrations and water concentration in the anode and cathode gases; the polarization curve of the cell was also displayed

Pollution due to increase in population, industrialization and other anthropological activities is effecting the physical, chemical and biological parameters of river water. Different water quality standards have been developed in order to keep check on the extent of water pollution, and in order to maintain these quality standards, water quality assessment and apportionment of pollution sources of river is been done using in situ laboratory analysis and multivariate statistical techniques. Traditional water quality management techniques were expensive, generally labour intensive and time consuming. Using satellite data and its potentials are new tools for managing and mapping surface water quality. Remote sensing offered a faster, repeatable, objective, cost effective and efficient way to monitor water quality. The present study demonstrates the use of spatially enhanced IRS LISS III and PAN satellite data for monitoring of water quality in part of Gomti River, Lucknow. The spatial enhancement of different spatial data sets is often used in digital image processing to improve the visual and analytical quality of the data. The spatial image enhancement technique combines the spectral and high spatial resolution information from two different sensors into one image, which has both spectral and high spatial resolution. In order to improve the spatial resolution, the efficiency of six different spatial enhancement techniques viz. Principal Component, Multiplicative, Modified IHS, HPF, Ehlers Fusion and Brovey Transform with standard deviation 5.721, 805.628, 1.567, 14.184, 10.657, 12.254 respectively were examined and evaluated. The Modified IHS spatial enhancement technique with higher standard deviation showing overall best result in compare to others in the analysis of water quality.

Organic agriculture is a holistic production management system which promotes and enhances agro-ecosystem health, including biodiversity, biological cycles, and soil biological activity. Socio-economic characteristics of the farmer was assess as well as comparison of adoption, attitude and knowledge on organic agriculture among farmers across the study areas in south west Nigeria. The data were obtained from 450 vegetable farmers adopting organic farming practices. ANOVA analysis was adopted to compare level of adoption, attitude and knowledge on organic agriculture among farmers across the states. The findings of the study reveal that majority of the respondents was male between the ages of 40-49 and married with house size of 6-10 members and farm size of 0.5-3.4 acres. It was further revealed that majority of the respondents had secondary school as educational qualification, belonged to organization and had contact with extension agents however majority of the respondents never received any subsidy from the government. The ANOVA result shows that there are differences in attitude and knowledge level across the states but there is no significant difference in adoption level across the states

This paper reviewed the potential for agricultural land management activities for increased soil carbon sequestration with particular reference to Africa. The sections of this review covered different land management activities (including agroforestry, conservation tillage, fallow management, mulching/cover crops, water management, rotation, manure, fertilizer use, grazing management) which lead to carbon sequestration in different farming systems. This review has revealed that there is high potential to sequester additional carbon through selected land management practices. The performance of these practices depends on soil properties and climatic conditions, and the degree of soil degradation at the time of time of intervention. There is need to integrate these land management practices for carbon sequestration into larger sustainable development and livelihoods strategies and practices in order to enhance an holistic approach and reduce some of the constraints that may inhibit these positive effect of land management practices for carbon sequestration.

Last years, the spread of toxic pollutants, especially heavy metals, in the environment have become very high. They represent a big danger for the environment and the human health, causing insidiously severe pathological alterations. Therefore their monitoring is important. In this paper we prove that the vegetation that grows alongside the streets with heavy traffic in an urban area and bear with the influence of industrial activities absorbs a big quantity of heavy metals, especially lead and zinc. Trace elements concentrations (lead, chromium, cadmium, cobalt, copper, zinc and nickel) were measured in the soil, leaves and bark samples of Populus nigra L., a common plant species from two areas: North Năvodari Camp and the principal traffic route (DJ 226 Năvodari city – Lumina), in Mamaia area (Romania). FAAS has been used for the elements’ analysis. Statistical tests have been performed to test the hypothesis of the dependence between the trace elements in soil, bark and leaves. The bioaccumulation factors have also been calculated. Their values prove low translocation from soil to plant leaves in the study areas.