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Rajesh R. Nair

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

Papers
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Journal ArticleDOI
27 May 2019-BMJ Open
TL;DR: In this article, the authors 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.
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.

65 citations

Proceedings ArticleDOI
01 Jun 2017
TL;DR: An unprecedented demonstration of a robust STT-MRAM technology designed in a 2x nm CMOS-embedded 40 Mb array with full array functionality, process uniformity and reliability, and 10 years data retention at 125C with extended endurance to ∼ 107 cycles is presented.
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.

43 citations

Journal ArticleDOI
TL;DR: In this paper, the elastic thickness (Te) and Moho depth data were used to estimate the configuration of the Moho/Crust-Mantle Interface that reveals regional correlations with the thickness variations.

35 citations

Journal ArticleDOI
TL;DR: In this paper, 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.

30 citations

Journal ArticleDOI
TL;DR: In this article, a NNW trending linear seamount chain along the axial part of the Laxmi Basin in the eastern Arabian Sea, between 15°N, 70°15'E and 17°20'N, 69°E, was identified.
Abstract: Multibeam (Hydrosweep) swath bathymetric investigations revealed the presence of a NNW trending linear seamount chain along the axial part of the Laxmi Basin in the eastern Arabian Sea, between 15°N, 70°15'E and 17°20'N, 69°E. This chain consists of three major edifices: RAMAN1 and PANIKKAR2 seamounts and WADIA2 guyot. These seamounts are elongated in plan and have heights and basal areas varying between 1068–2240 m and 300–1210 sq km, respectively. Steep lower flanks, flat plateaus, terraces, secondary peaks, and an extensive dendritic gullie pattern are the identified characteristic morphological features of these seamounts. The origin of these seamounts is attributed to anomalous volcanism resulting from the intersection of the Reunion hotspot with an extinct spreading center.

24 citations


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Journal Article
TL;DR: In this article, 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 was created.
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.

752 citations

01 Jan 2010
TL;DR: In this article, the International Seminar on Information and Communication Technology Statistics, 19-21 July 2010, Seoul, Republic of Korea, 19 and 21 July 2010 was held. [
Abstract: Meeting: International Seminar on Information and Communication Technology Statistics, Seoul, Republic of Korea, 19-21 July 2010

619 citations

Journal ArticleDOI
22 Mar 2019-Science
TL;DR: Solid Earth geoscience is a field that has very large set of observations, which are ideal for analysis with machine-learning methods, and how these methods can be applied to solid Earth datasets is reviewed.
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.

547 citations

Journal ArticleDOI
TL;DR: In this paper, the authors 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.
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.

208 citations