Rajesh R. Nair

Bio: Rajesh R. Nair is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topic(s): Continental margin & Heavy mineral. The author has an hindex of 9, co-authored 47 publication(s) receiving 328 citation(s). Previous affiliations of Rajesh R. Nair include GlobalFoundries & Indian Institute of Technology Kharagpur.

Size, composition and distribution of human resource for health in India: new estimates using National Sample Survey and Registry data.

Abstract: Objectives We provide new estimates on size, composition and distribution of human resource for health in India and compare with the health workers population ratio as recommended by the WHO. We also estimate size of non-health workers engaged in health sector and the size of technically qualified health professionals who are not a part of the health workforce. Design Nationally representative cross-section household survey and review of published documents by the Central Bureau of Health Intelligence. Setting National. Participants Head of household/key informant in a sample of 101 724 households. Interventions Not applicable. Primary and secondary outcome measures The primary outcome was the number and density of health workers,and the secondary outcome was the percentage of health workers who are technically qualified and the percentage of individuals technically qualified and not in workforce. Results The total size of health workforce estimated from the National Sample Survey (NSS) data is 3.8 million as of January 2016, which is about 1.2 million less than the total number of health professionals registered with different councils and associations. The density of doctors and nurses and midwives per 10 000 population is 20.6 according to the NSS and 26.7 based on the registry data. Health workforce density in rural India and states in eastern India is lower than the WHO minimum threshold of 22.8 per 10 000 population. More than 80% of doctors and 70% of nurses and midwives are employed in the private sector. Approximately 25% of the currently working health professionals do not have the required qualifications as laid down by professional councils, while 20% of adequately qualified doctors are not in the current workforce. Conclusions Distribution and qualification of health professionals are serious problems in India when compared with the overall size of the health workers. Policy should focus on enhancing the quality of health workers and mainstreaming professionally qualified persons into the health workforce.

CMOS-embedded STT-MRAM arrays in 2x nm nodes for GP-MCU applications

Abstract: Perpendicular Spin-Transfer Torque (STT) MRAM is a promising technology in terms of read/write speed, low power consumption and non-volatility, but there has not been a demonstration of high density manufacturability at small geometries. In this paper we present an unprecedented demonstration of a robust STT-MRAM technology designed in a 2x nm CMOS-embedded 40 Mb array. Key features are full array functionality with low BER (bit error rate), process uniformity and reliability, 10 years data retention at 125C with extended endurance to ∼ 107 cycles. All achieved with standard BEOL process temperatures. Data retention post 260°C solder reflow temperature cycle is demonstrated.

India–Madagascar paleo-fit based on flexural isostasy of their rifted margins

Abstract: The present study contributes new constraints on, and definitions of, the reconstructed plate margins of India and Madagascar based on flexural isostasy along the Western Continental Margin of India (WCMI) and the Eastern Continental Margin of Madagascar (ECMM). We have estimated the nature of isostasy and crustal geometry along the two margins, and have examined their possible conjugate structure. Here we utilize elastic thickness (Te) and Moho depth data as the primary basis for the correlation of these passive margins. We employ the flexure inversion technique that operates in spatial domain in order to estimate the spatial variation of effective elastic thickness. Gravity inversion and flexure inversion techniques are used to estimate the configuration of the Moho/Crust-Mantle Interface that reveals regional correlations with the elastic thickness variations. These results correlate well with the continental and oceanic segments of the Indian and African plates. The present study has found a linear zone of anomalously low-Te (1-5 km) along the WCMI (similar to 1680 km), which correlates well with the low-Te patterns obtained all along the ECMM. We suggest that the low-Te zones along the WCMI and ECMM represent paleo-rift inception points of lithosphere thermally and mechanically weakened by the combined effects of the Marion hotspot and lithospheric extension due to rifting. We have produced an India-Madagascar paleo-fit representing the initial phase of separation based on the Te estimates of the rifted conjugate margins, which is confirmed by a close-fit correlation of Moho geometry and bathymetry of the shelf margins. The matching of tectonic lineaments, lithologies and geochronological belts between India and Madagascar provide an additional support for the present plate reconstruction. (C) 2014 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

Implications of the 3D micro scale coal characteristics along with Raman stress mapping of the scratch tracks

Abstract: The microscale interfacial characteristics of coal are studied based on the analysis of mechanical behavior of individual elements contributing to coal heterogeneity with respect to high volatile bituminous A of coal, from Singrauli coal field, Madhya Pradesh, India. Micro-scale delineation of elastic plastic properties of coal by grid micro-indentation test and fracture toughness was accomplished by micro-scratch test on collotenite maceral. Micro-scratch test was performed in different directions on and critical points of failure were diagnosed by measuring acoustic emissions and tangential force. The 3D heterogeneity of coal is studied based on the damages at certain point of loads known as critical points with respect to distribution of macerals derived from coal petrography. Damages were observed at higher critical loads on samples collected from 332.9–335.6 m depth compared to 329.7–330.9 m depth samples owing to less heterogeneity and increased compaction. Raman stress mapping at critical points of scratch track revealed a spectral shift due to stress inversion. Shift from 1576–1593 cm− 1 for graphite band (G), and from 1344–1357 cm− 1 for disordered Carbon (D) band indicate the nature of stress and the deformation occurred for coal bulk sample. Raman spectra variation for maceral collotelinite is studied and compared with coal bulk samples behaviour a shift from 1574–1597 cm− 1 for G, and from 1346–1356 cm− 1 for D.

Granularity and textural analysis as a proxy for extreme wave events in southeast coast of India

Abstract: Extreme wave events of 1000 and 1500 years (radiocarbon ages) have been recently reported in Mahabalipuram region, southeast coast of India. Subsequently, we carried out extensive sedimentological analysis in regions covering a total lateral coverage of 12 km with a new archeological site as the central portion of the study area. Twelve trenches in shore normal profiles exhibit landward thinning sequences as well as upward fining sequences confirming with the global signatures of extreme wave events. The sediment size ranges from fine-to-medium and moderately well sorted-to-well sorted, and exhibit positive skewness with platykurtic-to-leptokurtic nature. We now propose the abrupt winnowing or back and forth motion including unidirectional transport of these deposited sediments, which results in positive skewness. Textural analyses derived from scanning electron microscope studies (SEM) demonstrate the alteration produced, in the ilmenite mineral with vivid presence of pits and crescents with deformation observed on the surface due to extreme wave activities. This is further confirmed with the predominance of high-density mineral such as magnetite (5.2) and other heavy minerals in these deposits inferred the high-intensity of the reworking process of the beach shelf sediments.

Digital isochrons of the world's ocean floor

Abstract: We have created a digital age grid of the ocean floor with a grid node interval of 6 arc min using a self-consistent set of global isochrons and associated plate reconstruction poles. The age at each grid node was determined by linear interpolation between adjacent isochrons in the direction of spreading. Ages for ocean floor between the oldest identified magnetic anomalies and continental crust were interpolated by estimating the ages of passive continental margin segments from geological data and published plate models. We have constructed an age grid with error estimates for each grid cell as a function of (1) the error of ocean floor ages identified from magnetic anomalies along ship tracks and the age of the corresponding grid cells in our age grid, (2) the distance of a given grid cell to the nearest magnetic anomaly identification, and (3) the gradient of the age grid: i.e., larger errors are associated with high age gradients at fracture zones or other age discontinuities. Future applications of this digital grid include studies of the thermal and elastic structure of the lithosphere, the heat loss of the Earth, ridge-push forces through time, asymmetry of spreading, and providing constraints for seismic tomography and mantle convection models.

Ministry of Statistics and Programme Implementation

Abstract: Meeting: International Seminar on Information and Communication Technology Statistics, Seoul, Republic of Korea, 19-21 July 2010

Machine learning for data-driven discovery in solid Earth geoscience

Abstract: BACKGROUND The solid Earth, oceans, and atmosphere together form a complex interacting geosystem. Processes relevant to understanding Earth’s geosystem behavior range in spatial scale from the atomic to the planetary, and in temporal scale from milliseconds to billions of years. Physical, chemical, and biological processes interact and have substantial influence on this complex geosystem, and humans interact with it in ways that are increasingly consequential to the future of both the natural world and civilization as the finiteness of Earth becomes increasingly apparent and limits on available energy, mineral resources, and fresh water increasingly affect the human condition. Earth is subject to a variety of geohazards that are poorly understood, yet increasingly impactful as our exposure grows through increasing urbanization, particularly in hazard-prone areas. We have a fundamental need to develop the best possible predictive understanding of how the geosystem works, and that understanding must be informed by both the present and the deep past. This understanding will come through the analysis of increasingly large geo-datasets and from computationally intensive simulations, often connected through inverse problems. Geoscientists are faced with the challenge of extracting as much useful information as possible and gaining new insights from these data, simulations, and the interplay between the two. Techniques from the rapidly evolving field of machine learning (ML) will play a key role in this effort. ADVANCES The confluence of ultrafast computers with large memory, rapid progress in ML algorithms, and the ready availability of large datasets place geoscience at the threshold of dramatic progress. We anticipate that this progress will come from the application of ML across three categories of research effort: (i) automation to perform a complex prediction task that cannot easily be described by a set of explicit commands; (ii) modeling and inverse problems to create a representation that approximates numerical simulations or captures relationships; and (iii) discovery to reveal new and often unanticipated patterns, structures, or relationships. Examples of automation include geologic mapping using remote-sensing data, characterizing the topology of fracture systems to model subsurface transport, and classifying volcanic ash particles to infer eruptive mechanism. Examples of modeling include approximating the viscoelastic response for complex rheology, determining wave speed models directly from tomographic data, and classifying diverse seismic events. Examples of discovery include predicting laboratory slip events using observations of acoustic emissions, detecting weak earthquake signals using similarity search, and determining the connectivity of subsurface reservoirs using groundwater tracer observations. OUTLOOK The use of ML in solid Earth geosciences is growing rapidly, but is still in its early stages and making uneven progress. Much remains to be done with existing datasets from long-standing data sources, which in many cases are largely unexplored. Newer, unconventional data sources such as light detection and ranging (LiDAR), fiber-optic sensing, and crowd-sourced measurements may demand new approaches through both the volume and the character of information that they present. Practical steps could accelerate and broaden the use of ML in the geosciences. Wider adoption of open-science principles such as open source code, open data, and open access will better position the solid Earth community to take advantage of rapid developments in ML and artificial intelligence. Benchmark datasets and challenge problems have played an important role in driving progress in artificial intelligence research by enabling rigorous performance comparison and could play a similar role in the geosciences. Testing on high-quality datasets produces better models, and benchmark datasets make these data widely available to the research community. They also help recruit expertise from allied disciplines. Close collaboration between geoscientists and ML researchers will aid in making quick progress in ML geoscience applications. Extracting maximum value from geoscientific data will require new approaches for combining data-driven methods, physical modeling, and algorithms capable of learning with limited, weak, or biased labels. Funding opportunities that target the intersection of these disciplines, as well as a greater component of data science and ML education in the geosciences, could help bring this effort to fruition. The list of author affiliations is available in the full article online.

The breakup of East Gondwana: Assimilating constraints from Cretaceous ocean basins around India into a best‐fit tectonic model

Abstract: Published models for the Cretaceous sea!oor-spreading history of East Gondwana result in unlikely tectonic scenarios for at least one of the plate boundaries involved and/or violate particular constraints from at least one of the associated ocean basins. We link East Gondwana spreading corridors by integrating magnetic and gravity anomaly data from the Enderby Basin off East Antarctica within a regional plate kinematic framework to identify a conjugate series of east-west-trending magnetic anomalies, M4 to M0 (~126.7–120.4 Ma). The mid-ocean ridge that separated Greater India from Australia-Antarctica propagated from north to south, starting at ~136Ma northwest of Australia, and reached the southern tip of India at ~126 Ma. Sea!oor spreading in the Enderby Basin was abandoned at ~115 Ma, when a ridge jump transferred the Elan Bank and South Kerguelen Plateau to the Antarctic plate. Our revised plate kinematic model helps resolve the problem of successive two-way strike-slip motion between Madagascar and India seen in many previously published reconstructions and also suggests that sea!oor spreading between them progressed from south to north from 94 to 84 Ma. This timing is essential for tectonic !ow lines to match the curved fracture zones of the Wharton and Enderby basins, as Greater India gradually began to unzip from Madagascar from ~100 Ma. In our model, the 85-East Ridge and Kerguelen Fracture Zone formed as conjugate !anks of a "leaky" transform fault following the ~100Ma spreading reorganization. Our model also identi"es the Afanasy Nikitin Seamounts as products of the Conrad Rise hotspot.