Recasting Culture to Undo Gender: A Sociological Analysis of Jeevika in Rural Bihar, India

This paper brings together sociological theories of culture and gender to answer the question – how do large-scale development interventions induce cultural change? Through three years of ethnographic work in rural Bihar, the authors examine this question in the context of Jeevika, a World Bank-assisted poverty alleviation project targeted at women, and find support for an integrative view of culture. The paper argues that Jeevika created new “cultural configurations” by giving economically and socially disadvantaged women access to a well-defined network of people and new systems of knowledge, which changed women’s habitus and broke down normative restrictions constitutive of the symbolic boundary of gender

Deep learning and benchmark machine learning based landslide susceptibility investigation, Garhwal Himalaya (India)

Garhwal Himalaya is the worst affected landslide prone region in Indian subcontinent mainly due to its complex geological settings and active tectonic activities. The data showed that every year, around 400 fatalities occur in Himalayan terrain due to landslide. In the current study, we have mapped the landslide susceptibility zones in the segment of Garhwal Himalaya using robust machine and deep learning algorithms. Individual machine and deep learning models have its own limitations like low generation capacity with nonlinear functions to describe the intricate relationship among predictors. In this study total five models i.e., SVM (Support Vector Machine), RF (Random Forest), bagging, ANN (Artificial Neural Network), DLNN (Deep Learning Neural Network) have been used along with twenty landslide controlling factors. Here, the principal objective of the study is to precisely delineate landslide susceptibility zones of the Garhwal Himalaya. The selecting factors have been considered through multi-collinearity test and information gain ratio statistics and the previous landslide points have been taken as training (70%) and testing (30%) dataset. According to area under curve value (AUC), the DLNN technique has high capability (AUC = 0.925) and accuracy for landslide area demarcation. The approach of integrated physical and social factors creates more precise prediction aptitude that can support large scale landslide management. These high precision models identified most of the parts of Rudraprayag and Tehri Garhwal as a very high landslide susceptibility zone. The generated maps can assist to policy makers for micro scale landslide management and sustainable land use planning particularly in Himalayan terrain

Cone-beam computed tomography assessment of root canal transportation and evaluation of canal centering using Protaper Gold, XP Endoshaper, and Edgefile X7

Aim: This study aims to access and evaluate canal transportation and canal centering ability of Protaper Gold (PTG), XP EndoShaper (XPS) and EdgeFile X7 using cone-beam computed tomography (CBCT). Materials and Methods: Sixty freshly extracted single-rooted premolars with mature apex and a canal curvature of 10°–20° were chosen and arbitrarily divided into three experimental groups (n = 20). After decoronation, the teeth measuring 16 mm were included in the study for standardization. According to the manufacturer's instructions, canals were shaped with PTG in Group 1, XPS in Group 2 and EdgeFile X7 in Group 3. For the evaluation of the root canal transportation at 2 mm, 4 mm, and 6 mm from the apex, canals were scanned before and after instrumentation using CBCT scanner. Independent t-tests and one-way ANOVA were used to analyze data and significance level was set at P < 0.05. Results: XPS showed significantly lower canal transportation than PTG system. Moreover, the centering ability of the XPS significantly higher than EdgeFile X7 and PTG at all root levels (P < 0.05). Conclusion: The XPS and EdgeFile X7 rotary file system showed the lowest transportation in both mesiodistal and buccolingual directions and also the highest centering ability. The PTG file showed the highest transportation and lowest centering ability