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Real-time bus travel time prediction has been an interesting problem since past decade, especially in India. Popular methods for travel time prediction include time series analysis, regression methods, Kalman filter method and Artificial Neural Network (ANN) method. Reported studies using these methods did not consider the high variance situations arising from the varying traffic and weather conditions, which is very common under heterogeneous and lane-less traffic conditions such as the one in India. The aim of the present study is to analyse the variance in bus travel time and predict the travel time accurately under such conditions. Literature shows that Support Vector Machines (SVM) technique is capable of performing well under such conditions and hence is used in this study. In the present study, nu-Support Vector Regression (SVR) using linear kernel function was selected. Two models were developed, namely spatial SVM and temporal SVM, to predict bus travel time. It was observed that in high mean and variance sections, temporal models are performing better than spatial. An algorithm to dynamically choose between the spatial and temporal SVM models, based on the current travel time, was also developed. The unique features of the present study are the traffic system under consideration having high variability and the variables used as input for prediction being obtained from Global Positioning System (GPS) units alone. The adopted scheme was implemented using data collected from GPS fitted public transport buses in Chennai (India). The performance of the proposed method was compared with available methods that were reported under similar traffic conditions and the results showed a clear improvement. [ABSTRACT FROM AUTHOR]

• The accuracy of methods to calculate the global distribution of income has not yet been tested. • Among four methods, the Lorenz-curve regression method is the most accurate. The two-parameter distribution method is also very precise. • On the basis of our new dataset, we find that income inequality among the citizens of 145 countries declined significantly between 1988 and 2015. • Our new method to deconstruct the change in the global Gini coefficient of income inequality reveals that income convergence was the main driver. • Without China and India, global interpersonal income inequality in 143 countries was higher in 2015 than in 1988. While various methodologies have been used in the literature to estimate global interpersonal income inequality, the accuracy of these methods has not so far been tested. We compare the accuracy of four methods and find that the Lorenz curve regression method is the most accurate and robust, while the accuracy of the identical quantile income and the Kernel density methods depends on the level of detail about income shares. The simple two-parameter distribution method is also very accurate when either the Log-normal or the Weibull distribution is used. Using the two-parameter distribution method, we show that global income inequality among the citizens of 145 countries declined significantly between 1988 and 2015, largely because of the convergence of income per capita, which was offset to a small degree by the increase in within-country inequalities and the increased population share of poorer and more unequal countries. Regional income inequality declined in most parts of the world, with the notable exception of developing Asia where it has increased. Despite the large increases in within-China and within-India inequality, income convergence of the two countries drove down global income inequality. Without China and India, global interpersonal income inequality in 143 countries was higher in 2015 than in 1988, indicating that more than half of the world has not really become more equal. [ABSTRACT FROM AUTHOR]

• Regular breast health examination is the only way to reduce the breast cancer mortality rate. • Infrared Breast Thermography can pinpoint the minute temperature changes related to breast health. • The non-radiating Infrared breast thermography can be used as a routine breast health checkup tool. • By identifying the breast abnormalities in early stages, infrared breast thermography provides more treatment options. The high incidence and mortality rate of breast cancer in India and the limitations of gold standard method X-ray mammography to be used as a screening and diagnostic modality in young women tempted us to evaluate the efficiency of highly sensitive and non-radiating Infrared Breast Thermography (IBT) in early breast abnormality detection. This study investigates the efficiency of IBT by doing Temperature based analysis (TBA), Intensity based analysis (IBA), and Tumor Location Matching (TLM). In TBA and IBA, several temperature and intensity features were extracted from each thermogram to characterize healthy, benign and malignant breast thermograms. In TLM, the locations of suspicious regions in thermograms were matched with the tumor locations in mammograms/Fine Needle Aspiration Cytology images to prove the efficiency of IBT. Thirteen different sets of features have been created from the extracted temperature and intensity features and their classification performances have been evaluated by using Support Vector Machine with Radial basis function kernel. Among all feature sets, the feature set comprising the statistically significant (p < 0.05) features provides the highest classification accuracy of 83.22% with sensitivity 85.56% and specificity 73.23%. Based on the results of this study, IBT is found to be potential enough to be used as a proactive technique for early breast abnormality detection in asymptomatic population and hence, capable of identifying the subjects that need urgent medical attention. [ABSTRACT FROM AUTHOR]

The objective of the present study is to estimate total organic carbon (TOC) content over the entire thickness of Cambay Shale, in the boreholes of Jambusar–Broach block of Cambay Basin, India. To achieve this objective, support vector regression (SVR), a supervised data mining technique, has been utilized using five basic wireline logs as input variables. Suitable SVR model has been developed by selecting epsilon-SVR algorithm and varying three different kernel functions and parameters like gamma and cost on a sample dataset. The best result is obtained when the radial-basis kernel function with gamma = 1 and cost = 1, are used. Finally, the performance of developed SVR model is compared with the ΔlogR method. The TOC computed by SVR method is found to be more precise than the ΔlogR method, as it has better agreement with the core-TOC. Thus, in the present study area, the SVR method is found to be a powerful tool for estimating TOC of Cambay Shale in a continuous and rapid manner. [ABSTRACT FROM AUTHOR]

One issue that has attracted considerable attention recently among scholars interested in inequality and conflict is polarization. We analyze polarization in India over roughly the past three decades using consumption expenditure data. We show that both bipolarization and multidimensional polarization (on several dimensions: rural–urban, state, region) have increased since the 1990s. In the case of bipolarization, this is a reversal from an earlier trend (in the 1980s). Overall, our results suggest that the high growth that India has witnessed since the 1990s has been associated with widening disparities. Comparing polarization and inequality trends, we find similarities, but also some differences; we also show how the study of polarization can provide different insights. Our results therefore underscore the importance of studying polarization as distinct from traditional inequality. [ABSTRACT FROM PUBLISHER]

The experiment was conducted to know the physical and cooking properties of ten aromatic rice varieties. Among the varieties milling and head rice out-turn ranged from 72.48 to 80.94 and 65.11 to 81.85 per cent respectively. The grain length varied from 4.30 to 7.80 mm, breadth 1.84 to 2.27 mm, L / B ratio 2.02 to 4.22 and grain width 1.54 to 1.88 mm, 1000 grain weight 11.36 to 20.18 g , and bulk density 0.76 to 0.89 (g / ml). Cooking time ranged from 15 to 20 minutes, percent increase in weight 127 to 210, per cent increase in volume 128 to 257, water uptake ratio 15.85 to 39.93, kernel elongation ratio 1.25 to 1.53, per cent curled grain 17 to 61 and dispersed solids 2.63 to 8.46 per cent respectively. Per cent increase in volume and weight, water uptake ratio, and kernel elongation ratio were positively correlated with sensory mean score whereas percent curled grains, and cooking time were negatively correlated with sensory mean score. [ABSTRACT FROM AUTHOR]

The efficacy of common insecticides viz., neem seed kernel extract (NSKE), Helicoverpa armigera Hubner Nuclear poly hedrosis virus (HaNPV), Endosulfan were tested alone and in combination against gram pod borer on chickpea. It was observed that larval reduction was highest with NSKE 1.66% + HaNPV 250LE/ha + Endosulfan 0.023% followed by NSKE 1.66% + Endosulfan 0.023%, NSKE 2.5% + HaNPV 250LE sprayed twice at 15 days interval. [ABSTRACT FROM AUTHOR]