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Anubhav Jain

Researcher at Lawrence Berkeley National Laboratory

Publications -  294
Citations -  29383

Anubhav Jain is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Computer science & Ion exchange. The author has an hindex of 58, co-authored 266 publications receiving 21124 citations. Previous affiliations of Anubhav Jain include University of California, Berkeley & Management Development Institute.

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Exploring the Relative Relevance of Organizational Citizenship Behavior and Emotional Intelligence

Anubhav Jain
TL;DR: In this article, the authors examined the predictive ability of Organizational Citizenship Behavior (OCB) and Emotional Intelligence (EI) with regard to organizationally relevant criterion variables including job satisfaction, personal effectiveness, Reputational Effectiveness, General Health, Career Orientation, Perceived Job Mobility, Turnover Intention, Organizational Commitment, Vertical Trust, Work Recognition, Organizing Productivity, and Organizational Effectiveness.
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A solid membrane sensor for chromate ions

TL;DR: In this article, a solid membrane sensor has been fabricated using basic lead sulphate as an elecroactive phase, which can be used for the estimation of CrO 4 2− by potentiometric titrations with Pb 2+ ions.
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Mapping and management of aquifers suffering from over-exploitation of groundwater resources in Baswa-Bandikui watershed, Rajasthan, India

TL;DR: In this article, a micro-level aquifer mapping was carried out in the Baswa-Bandikui watershed in the state of Rajasthan in northwest India to find a solution to this serious problem.
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The mediating role of job satisfaction in the relationship of vertical trust and distributed leadership in health care context

TL;DR: In this article, the authors investigated the effect of vertical trust on distributed leadership and performance as mediated by job satisfaction, and further observed the role of DL in carrying out the impact of satisfaction on employees' performance.
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A critical examination of compound stability predictions from machine-learned formation energies

TL;DR: It is demonstrated that accurate predictions of formation energy do not imply accurate predictors of stability, emphasizing the importance of assessing model performance on stability predictions, for which this work provides a set of publicly available tests.