1,918 research outputs found
Video_Data – Supplemental material for In vitro comparison of conventional surgical and rotary ultrasonic bone drilling techniques
No full textSupplemental material, Video_Data for In vitro comparison of conventional surgical and rotary ultrasonic bone drilling techniques by Vishal Gupta, Ravinder Pal Singh, Pulak M Pandey and Ravi Gupta in Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine</p
sj-docx-1-pie-10.1177_09544089231211235 - Supplemental material for A data-driven ensemble machine learning approach for predicting the mechanical strength of 3D printed orthopaedic bone screws
No full textSupplemental material, sj-docx-1-pie-10.1177_09544089231211235 for A data-driven ensemble machine learning approach for predicting the mechanical strength of 3D printed orthopaedic bone screws by Raj Agarwal, Jaskaran Singh and Vishal Gupta in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
Construction of Entrepreneurial Orientation: Dispute, Demand, and Dare
No full textInvited editorial by Vishal K. Gupta, discussing the concept of entrepreneurial orientation (EO). Stated succinctly, entrepreneurial orientation (EO) refers to specific aspects of an organization-wide proclivity toward new endeavors. After about four decades of research on this topic, EO has emerged as a predominant construct of interest in strategic management. Over the years, a large—and growing—body of research has accumulated about EO, but questions do persist about the way(s) in which the various facets of EO are linked to each other. This essay distinguishes between three ways in which the various entrepreneurial elements can be combined to form the overall EO construct: additive, multiplicative, and geometric. The underlying motivation to draw this tripartite distinction is the belief that conceptual development about EO will be accelerated if researchers explore new formulations not considered before
sj-png-3-jcn-10.1177_08830738231169411 - Supplemental material for Neuroimaging Findings in Vitamin B<sub>12</sub>-Deficient Infants With Neurologic Features
No full textSupplemental material, sj-png-3-jcn-10.1177_08830738231169411 for Neuroimaging Findings in Vitamin
B12-Deficient Infants With Neurologic Features by Anjali Yadav, Chandrika Azad, Ravinder Kaur, Vishal Guglani, Seema Gupta and Sukanya Mitra in Journal of Child Neurology</p
sj-jpeg-2-jcn-10.1177_08830738231169411 - Supplemental material for Neuroimaging Findings in Vitamin B<sub>12</sub>-Deficient Infants With Neurologic Features
No full textSupplemental material, sj-jpeg-2-jcn-10.1177_08830738231169411 for Neuroimaging Findings in Vitamin
B12-Deficient Infants With Neurologic Features by Anjali Yadav, Chandrika Azad, Ravinder Kaur, Vishal Guglani, Seema Gupta and Sukanya Mitra in Journal of Child Neurology</p
sj-docx-1-jcn-10.1177_08830738231169411 - Supplemental material for Neuroimaging Findings in Vitamin B<sub>12</sub>-Deficient Infants With Neurologic Features
No full textSupplemental material, sj-docx-1-jcn-10.1177_08830738231169411 for Neuroimaging Findings in Vitamin
B12-Deficient Infants With Neurologic Features by Anjali Yadav, Chandrika Azad, Ravinder Kaur, Vishal Guglani, Seema Gupta and Sukanya Mitra in Journal of Child Neurology</p
sj-docx-1-pic-10.1177_09544062221110407 – Supplemental material for Prediction of surface roughness and cutting force induced during rotary ultrasonic bone drilling via statistical and machine learning algorithms
No full textSupplemental material, sj-docx-1-pic-10.1177_09544062221110407 for Prediction of surface roughness and cutting force induced during rotary ultrasonic bone drilling via statistical and machine learning algorithms by Raj Agarwal, Vishal Gupta, Jaskaran Singh and Vivek Jain in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science</p
Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data
No full textMishra, S., Singh, V., Gupta, A., Bhattacharya, D., & Mudgal, A. (2023). Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data. Transportation Letters, 15(4), 296-307. https://doi.org/10.1080/19427867.2022.2050493 ----The present work in the paper was not funded by any organization. One of the coauthor, Devanjan Bhattacharya has received funding from UKRI ESRC Impact Acceleration Grant (ES/T50189X/1), and European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie COFUND Grant Agreement No. 801215: TRAIN@Ed: ‘Transnational Research And Innovation Network At Edinburgh.’Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.authorsversionpublishe
Remittance income and expenditure on education in Nepal
No full textThesis(Master) --KDI School:Master of Development Policy,2014masterpublishedVishal Gupta
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