Contract Farming, Ecological Change and the Transformations of Reciprocal Gendered Social Relations in Eastern India

Debates on gender and the commodification of land highlight the loss of land rights, intensification of demands on women’s labour, and decline in their decision-making control. Supported by ‘extra-economic forces’ of religious nationalism (Hindutva), such neoliberal interventions are producing new gender ideologies involving a subtle shift from relations of reciprocity to those of subordination. Using data from fine grained fieldwork in Koraput district, Odisha, we analyse the tensions and transformations created jointly by corporate interventions (contract farming of eucalyptus by the paper industry) and religious nationalism in the local landscape. We examine how these phenomena are reshaping relations of asymmetric mutuality between nature and society, and between men and women

Solar Energy: Incentives to Promote PV in EU27

The growth in the use of renewable energies in the EU has been remarkable. Among these energies is PV. The average annual growth rate for the EU-27 countries in installed PV capacity in the period 2005-2012 was 41.2%. While the installed capacity of PV has reached almost 82 % of National Renewable Energy Action Plan (NREAP) targets for the EU-27 countries for 2020, it is still far from being used at its full potential. Over recent years, several measures have been adopted in the EU to enhance and promote PV. This paper undertakes a complete review of the state of PV power in Europe and the measures taken to date to promote it in EU-27. 25 countries have adopted measures to promote PV. The most widespread measure to promote PV use is Feed- in Tariffs. Tariffs are normally adjusted, in a decreasing manner, annually. Nevertheless, currently, seven countries have decided to accelerate this decrease rate in view of cost reduction of the installations and of higher efficiencies. The second instrument used to promote PV in the EU-27 countries is the concession of subsidies. Nevertheless, subsidies have the disadvantage of being closely linked to budgetary resources and therefore to budgetary constraints. In most EU countries, subsidies for renewable energy for PV are being lowered. Twelve EU-27 countries adopted tax measures. Low-interest loans and green certificate systems were only sparingly used

Enhanced hyporheic exchange flow around woody debris does not increase nitrate reduction in a sandy streambed

Anthropogenic nitrogen pollution is a critical problem in freshwaters. Although riverbeds are known to attenuate nitrate, it is not known if large woody debris (LWD) can increase this ecosystem service through enhanced hyporheic exchange and streambed residence time. Over a year, we monitored the surface water and pore water chemistry at 200 points along a ~50m reach of a lowland sandy stream with three natural LWD structures. We directly injected 15N-nitrate at 108 locations within the top 1.5m of the streambed to quantify in situ denitrification, anammox and dissimilatory nitrate reduction to ammonia, which, on average, contributed 85%, 10% and 5% of total nitrate reduction, respectively. Total nitrate reducing activity ranged from 0-16µM h-1 and was highest in the top 30cm of the stream bed. Depth, ambient nitrate and water residence time explained 44% of the observed variation in nitrate reduction; fastest rates were associated with slow flow and shallow depths. In autumn, when the river was in spate, nitrate reduction (in situ and laboratory measures) was enhanced around the LWD compared with non-woody areas, but this was not seen in the spring and summer. Overall, there was no significant effect of LWD on nitrate reduction rates in surrounding streambed sediments, but higher pore water nitrate concentrations and shorter residence times, close to LWD, indicated enhanced delivery of surface water into the streambed under high flow. When hyporheic exchange is too strong, overall nitrate reduction is inhibited due to short flow-paths and associated high oxygen concentrations

Comparative efficacy of anthelmintics and their effects on hematobiochemical changes in fasciolosis of goats of South Gujarat

Aim: Fasciolosis is a parasitic disease caused by Fasciola spp. of the family Fasciolidae (trematodes) characterized by bottle jaw, anemia, progressive debility, and potbelly condition. There are many aspects of fasciolosis remaining unknown thus hemato-biochemical alterations in closantel, triclabendazole + ivermectin, and oxyclozanide + levamisole treated goats were studied. Materials and Methods: A total of 40 naturally fasciolosis infected goats having egg per gram more than 100 were randomly divided into four groups. Goats of Group I-III were treated with three different anthelmintics, whereas, goats of Group-IV were kept as control or untreated. Whole blood, serum, and fecal samples were collected on 0, 7th, and 30th day of treatment. Results: During the study, values of hemoglobin, total erythrocyte count, pack cell volume, and total protein were significantly elevated to their normal levels in anthelmintics treated groups. Whereas, values of total leukocyte count, aspartate transaminase (AST), lactate dehydrogenase (LDH), and gamma-glutamyl transferase (GGT) were significantly reduced to their normal level in anthelmintics treated groups. The efficacy of closantel (T1), triclabendazole + ivermectin (T2), and oxyclozanide + levamisole (T3) was 99.63%, 100%, and 94.74% and 100%, 100%, and 97.38% on 7th and 30th day of treatment, respectively. Conclusions: Fasciolosis in goats can be diagnosed on the basis of fecal sample examination, but alterations in important biomarkers such as AST, GGT, and LDH are also helpful for early diagnosis. The use of newer anthelmintic either alone or in combination showed a higher therapeutic response in fasciolosis of goats

Battery life time prediction of electric vehicle using artificial intelligence

The widespread use of electric vehicles (EVs) is viewed as a turning point for lower emissions of co2 and much more advanced driver assistance systems. It is time-consuming to predict the battery capacity of the electric vehicle using the endurance and dependability of battery systems. Because battery deterioration is typically non-linear, predicting the capacity of the battery of charge estimation with significantly less deterioration is incredibly time-consuming. As a result of this complexity, real-time control of battery storage has proven difficult. Nevertheless, using the latest advancements in battery deterioration comprehension, simulation techniques, and testing, there is a potential to combine this expertise with new machine learning (ML) approaches to find a potential solution for this complexity. In this study, the battery life is predicted using three different regression models Linear Regression, Ridge Regression, and Ensemble Regression. The model's error analysis is conducted to improve the battery's performance characteristics. Finally, the three ML algorithms are compared using performance parameters. Out of the three regression models, the Ensemble regression model is found to be the best model which has an accuracy of 94.8%