Size-class and returns to cultivation in India: A Cold case reopened

This paper investigates the relationship between returns to cultivation per hectare and size-class of land cultivated in India, using unit level data from the 59th round National Sample Survey, 2003. The analysis is done separately for `kharif' and `rabi' - for total value of cultivation from all crops at the all India level. The empirical evidence rejects the null hypothesis of no relationship and points to the existence of an inverse association. We argue that the efficiency of the small-holders has to be taken with a pinch of salt because their low absolute returns brings into focus the question of their livelihood sustainability which is further aggravated on account of higher unit costs. Being the first exercise in a series of proposed explorations into disaggregated analyses across states, and for specific crops, it opens up the classic debate on farm size and productivity in the 21st century.agrarian crisis, agriculture, efficiency, livelihood sustainability, NSS, productivity, size-class

To Bt or not to Bt? Risk and uncertainty considerations in technology assessment

The acreage under the transgenic Bt cotton seeds in India has risen significantly since its legalization in the year 2002. Discussions on the advantages from the technology have focused on increments in productivity and income, without much analysis on risk. We point out that claims on productivity gains seem to be misplaced, as appropriate counterfactuals do not exist for the same hybrids. In this article we analyse production costs and crop incomes in drought years to test a simplistic theory of risk based on first principles. We employ a mixed-methods framework to draw inferences by combining data from two cross-sectional surveys in Gujarat (Saurashtra and Southern-Plains) and Maharashtra (Western Vidarbha) for the period 2009-10 and compare it with unit-level data for the corresponding regions from a nationally representative sample for the period 2002-03. Empirical evidence, though limited, brings out the problem of how a high cost technology could be associated with higher risks and may be dominated by traditional alternatives under certain conditions. Ethnographic accounts from the field provide qualitative support to our understanding of potential risks and uncertainties associated with the new technology.Bt cotton, agricultural risk, technology evaluation, mixed-methods, India

Effect of methane supplementation on the performance, vibration and emissions characteristics of methane-diesel dual fuel engine

The increasing energy demands, especially in transportation sector, and the challenges of excess pollution and environmental degradation caused due to the conventional fuels, as well as their limited availability has highlighted the need to look for alternative fuels to sustain future needs. Methane is capable of catering to these demands due to its wide availability, both in renewable and non-renewable energy sources. The present work explores the effect of methane supplementation on the performance and emission characteristics as well as the vibrations in internal combustion engines. A four-stroke compression ignition engine is modified to run as a methane-diesel dual fuel engine where methane is inducted through intake manifold and diesel is directly injected into cylinder. Tests are performed by varying engine load and methane energy levels up to 75%. Our study shows that the participation of methane at lower load conditions is weak due to its higher auto ignition temperature and higher calorific value. The emissions, particularly CO and NO, are observably higher at 75% load conditions due to the efficient combustion and higher temperature at higher load conditions. The vibration studies on the dual fuel combustion indicates that the introduction of methane also suppresses the frequency spectrum of combustion noise and reduces the ringing intensity level of vibration for complete spectrum of engine loads, with the effect being prominent at higher loads. Overall, our results suggests that combustion of methane in dual fuel diesel engine shows distinct characteristics at contrasting load conditions

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Are rainfed agricultural households insured? Evidence from five villages in Vidarbha, India

There are several studies on risk and insurance in village economies in the developing world. However, there is a lack of evidence for recent decades in spite of profound changes including structural transformation and widespread market liberalization in most regions. In this paper, I use primary panel data from five rainfed villages in a high-risk region of India and find evidence of considerable exposure as well as vulnerability to idiosyncratic and covariate risks. Further, there is significant wealth differentiated heterogeneity in vulnerability. The findings help identify the scope of public policy in improving risk management in rainfed agriculture