Spatial justice and the land politics of renewables: dispossessing vulnerable communities through solar energy mega-projects

This paper considers aspects of spatial justice in the processes of land acquisition for large-scale solar energy projects in the developmentalist context of India. It explores the case of one of the world’s largest solar park projects in Charanka, Gujarat. While the official rhetoric suggests an inclusive project for globally benign renewable energy production, the research reveals a more controversial land and power politics of renewable energy. It is argued, in particular, that the project increases the precariousness of vulnerable communities, who are exposed to the loss of livelihoods due to the enclosure of common land and extra-legal mechanisms through which land acquisitions for the project have reportedly taken place. This case exemplifies how solar mega-projects may manifest a regime of accumulation whereby low-carbon coalitions of interests can maximize their gains by dispossessing vulnerable social groups of their life-sustaining assets

Assessing the Vulnerability of Agriculture Systems to Climate Change in Coastal Areas: A Novel Index

This study proposes a novel index to evaluate agricultural vulnerability to climate change in coastal areas, using the case of Andhra Pradesh, the state with the second longest coastline in India. Field data was collected from more than 1000 farmers (involved in over 50 varieties of crops) in 22 riverine and coastal case study areas. Data was collected through site visits, surveys and five workshops conducted between November 2018 and June 2019. Based on the collected data sets, a new Agricultural Coastal Vulnerability Index (AGCVI) was developed and applied to the 22 sites located in two districts (Krishna and Guntur) of Coastal Andhra Pradesh. The analysis revealed that the areas with three crop seasons (Kharif, Rabi and Zaid) per year are highly vulnerable to climate change. On the other hand, sites with one crop season (Kharif) per annum are the least vulnerable to climate change. Moreover, grains (particularly rice), flowers and fruit crops are more susceptible to climate change and its induced impacts. Rice is no longer a profitable crop in the case study areas partly as a result of unfavourable weather conditions, inadequate insurance provision and lack of government support for farmers. Cumulatively, all these circumstances impact farmers’ incomes and socio-cultural practices: this is leading to a marriage crisis, with a reduction in the desirability of matrimony to farmers. These findings provide valuable information that can support climate and agriculture policies, as well as sustainable cropping patterns among farmers’ communities in coastal areas of India in the future

Conflicting Parameter Pair Optimization for Linear Aperiodic Antenna Array using Chebyshev Taper based Genetic Algorithm

In this study, the peak side lobe level (PSLL) in the radiation pattern of a linear antenna array (LAA) is lowered without affecting its first null beam width (FNBW). Antenna array synthesis is commonly applied to achieve high directivity, low side lobes, high gain and desired null positions in the output radiation pattern. But output parameters like PSLL, null positions and beam width conflict with each other, i.e. as one parameter improves, the other deteriorates. To avoid this problem, a multi-objective optimization algorithm can be implemented, in which both the conflicting parameters can be simultaneously optimized. This work proposes a multi-objective algorithm, which takes advantages of the well-known Chebyshev tapering and genetic algorithm (GA), to lower the PSLL without broadening the beam further. Array elements are fed using Chebyshev tapered excitations while GA is incorporated to optimize the elemental spacing. The results of 28-element LAA are compared with those of multi-objective Cauchy mutated cat swarm optimization (MO-CMCSO) existing in literature, which has also been proven to be superior to multi-objective cat swarm optimization (MO-CSO) and multi-objective particle swarm optimization (MO-PSO). Results indicate that the proposed algorithm performs better by further reducing the PSLL from -21.57 dB (MO-CMCSO) to -28.18 dB, while maintaining the same FNBW of 7.4 degrees

Optimization Of A Main Engine Driven Roof Top Bus Air-Conditioning System

The use of a roof-top Bus air-conditioning (AC) system has been steadily growing in in the emerging markets. An AC system is the second biggest energy consumer component in a bus. The addition of an air conditioning system in a bus would cause higher fossil fuel consumption and increasing the impact on the environment due to the increasing amounts of exhaust emission from the combustion engines. For this reason, researchers and AC system manufacturers seek to improve Bus AC systems design and technology to reduce the fuel consumption rate without forfeiting passenger thermal comfort. Unlike in Developed Markets,Bus AC in Emerging Markets pose a unique challenge of undersigned Main engine , not well insulated walls and higher emphasis on first cost. Thus, there is a need to develop an affordable and efficient bus AC system featured by low first cost, economical operation in terms of energy saving and stable passenger thermal comfort at all atmospheric conditions. This paper presents a design optimization study that is used in assessing the best configuration of a bus air conditioning system, using a thermo economic approach. A cost function is introduced, defined as the sum of three contributions, the efficiency of system obtained by minimizing the rate of exergy destruction, the investment expense, and the operational expense of a roof top Bus AC system that is usually coupled with the main bus engine. The optimal trade-off between these contributions efficiency, investment and operating cost is investigated. The design optimization is conducted by investigating the effect of geometrical and operational parameters of system configuration which have a significant influence on the objective functions of the design optimization. Based on this sensitivity analysis different practical system enhancement methods like using variable displacement compressors, enhanced and demand based dehumidification, dedicated sub cooling etc are investigated to minimize the cost function. A Pareto trade-off frontier is built to give a guidance on the optimal trade-off on the multi objectives. Finally the design solutions are presented for various trade-offs. An in-house refrigeration simulation tool is used to build the base line system model, this model is calibrated using the actual test data. This calibrated model is used to run the optimization exercises. The objective function and constraints are developed for different cases and optimization solution is obtained using Matlab Optimization Toolbox

“Social justice and solar energy implementation”: a case study of Charanaka solar park, Gujarat, India

In the recent years, social issues around renewable energy implementation have been gaining prominence both in developed and developing countries. Though researchers across different disciplines in developed countries have started dealing with this issue, there is a lack of theoretical or empirical research in developing countries. This research from a pluralistic perspective and using the case study of ‘Charanaka Solar Park’ qualitatively analyses the relationship between ‘justice’ and solar energy implementation in India. The justice framework used in this thesis corresponds to the theoretical knowledge on a) procedural justice and b) distributional justice principles based in social, environmental, and energy justice literatures. The application of multiple theories of justice proved to be significant and useful instrument for analysing controversies over implementation of solar (renewable) energy policies. The results of this research have provided new insights into how social justice issues, such as recognition of marginalised communities, equal and democratic participation, and just distribution of project outcomes, are strongly interconnected to implementation of ‘environmentally good’ projects. Following the findings of this research, recommendations for policymakers and practitioners are proposed and pathways for future research are outlined

Assessing the vulnerability of agriculture systems to climate change in coastal areas: A novel index

This study proposes a novel index to evaluate agricultural vulnerability to climate change in coastal areas, using the case of Andhra Pradesh, the state with the second longest coastline in India. Field data was collected from more than 1000 farmers (involved in over 50 varieties of crops) in 22 riverine and coastal case study areas. Data was collected through site visits, surveys and five workshops conducted between November 2018 and June 2019. Based on the collected data sets, a new Agricultural Coastal Vulnerability Index (AGCVI) was developed and applied to the 22 sites located in two districts (Krishna and Guntur) of Coastal Andhra Pradesh. The analysis revealed that the areas with three crop seasons (Kharif, Rabi and Zaid) per year are highly vulnerable to climate change. On the other hand, sites with one crop season (Kharif) per annum are the least vulnerable to climate change. Moreover, grains (particularly rice), flowers and fruit crops are more susceptible to climate change and its induced impacts. Rice is no longer a profitable crop in the case study areas partly as a result of unfavourable weather conditions, inadequate insurance provision and lack of government support for farmers. Cumulatively, all these circumstances impact farmers’ incomes and socio-cultural practices: this is leading to a marriage crisis, with a reduction in the desirability of matrimony to farmers. These findings provide valuable information that can support climate and agriculture policies, as well as sustainable cropping patterns among farmers’ communities in coastal areas of India in the future

Measurement of Multi-Dimensional Poverty in India: A state level analysis

The paper measures the multidimensional poverty index (MPI) in India by considering National Sample Survey (NSS) data on ‘Consumption Expenditure’ for the period of 2004-05 and 2011-12 by using Alkire and Foster’s (2011) methodology and by considering three main indicators i.e., standard of living, education and income at the household or persons level. The results show that multidimensional poverty head count has declined from 62.2 percent in 2004-05 to 38.4 percent in 2011-12. However, rural/ urban separate analysis clearly indicates a sharp decline in rural poverty compared to urban poverty reduction. Lack of education of the household members made the highest contribution to poverty, followed by income and standard of living in India. State level analysis show that Jharkhand, Uttar Pradesh, Rajasthan, Orissa, Bihar, Chhattisgarh, and Arunachal Pradesh, have a higher poverty head count ratio while Kerala, Mizoram, Nagaland, Punjab, Himachal Pradesh, and Haryana have lower poverty rate. Promoting local resource and tourism based industries through urbanization, higher and job oriented education, and long term saving for creating funding are required to reduce poverty in India

A Generative Adversarial Network Based Approach for Synthesis of Deep Fake Electrocardiograms

Analyzing the data from an electrocardiogram (ECG) can reveal important details about a patient's heart health. A key component of modern medicine is the use of AI and ML-based computer-aided diagnosis tools to aid in making life-or-death decisions. It is common practice to use them in cardiology for the automatic early diagnosis of a variety of potentially fatal illnesses. The machine learning algorithm's need for a large amount of training data to build the learning model is an empirical challenge in the medical domain. To address this challenge, study into methods for creating synthetic patient data has blossomed. There is a higher risk of privacy invasion due to the need for massive amounts of training data for deep learning automated medical diagnostic systems that may help assess the state of the heart from this signal. To combat this issue, researchers have tried to create artificial ECG readings by analyzing only the statistical distributions of the accessible authentic training data.The primary goal of this study is to learn how generative adversarial networks can be used to create artificial ECG signals for use as training data in a classification task. In this study, we used both GAN and WGAN for generation of artificial ECG signals

Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications

People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non-uniform illumination. The four main applications of these image inpainting techniques are scratch removal, text removal, object removal and image restoration. However, all the four image inpainting applications cannot be implemented using a single technique. According to the literature, there has been relatively less work done in the field of image inpainting applications. Investigation has been carried out to find the suitability of these three techniques for the four above mentioned image inpainting applications based on two performance metrics