Bibliographic record
Abstract
First Virtual International Conference on Advances in Renewable and Sustainable Energy Systems (ICARSES 2020) December 3 – 5, 2020 Edited by Dr. M. Cheralathan (Chairman) Professor, Department of Mechanical Engineering, SRM Institute of Science of Technology, Kattankulathur, India Renewable energy sources are hygienic sources of energy that have a much lesser negative environmental impact than conventional fossils energy technologies. The most significant feature of renewable energy is its plentiful supply and is infinite in comparison to energy from the depleting fossil fuels. Hence an awareness about renewable energy and energy conservation has to be created globally and use of renewable, environmentally friendly and energy efficient technologies have to be promoted. Collaborative efforts for promoting research between Institutions and Industry on new energy efficient products and technologies at national and international level will help in achieving sustained energy transition. As an initiative in creating awareness on these challenges, the Department of Mechanical Engineering, SRMIST is conducting the first virtual International Conference on Advances in Renewable and Sustainable Energy Systems (ICARSES 2020) during 3 rd to 5 th December 2020. ICARSES 2020 focuses on a range of issues related to various renewable and sustainable energy like Solar, Wind, Biogas, Geothermal, Biomass etc. The purpose of the conference is to bring together the multi - disciplinary community of engineers, scientists, and academics to discuss recent trends and future developments in Renewable Energy. The conference features invited and contributed talks organized in different sessions. The invited speakers are globally recognized experts in the respective fields’ viz., Prof. Marc Rosen. University of Ontario, Canada, Prof. R.Z. Wang,, Prof. Bidyut Baran Saha, Kyshu University, Japan, Prof. S C Kaushik, IIT Delhi, Prof. Sivasankaran Harish, University of Tokyo, Japan, Prof. R Velraj, Anna University, Chennai, India, Prof. S. Murugan, NIT, Rourkela, India, Dr. G. Kumaresan, IES, Anna University, Chennai, India, Dr. Zafar Said, University of Sharjah, UAE, Prof. K.V. Sharma, JNTU, Hyderabad, India, Prof. S. K. Tyagi, IIT Delhi, New Delhi, India, Dr. B. Chitti Babu, IITDM, Kanchipuram, Chennai, India, Dr. Ravita Lamba, MNIT Jaipur, India and Dr. Alperen Günay, University of Tokyo, Japan. List of About Srmist, About The Department, Chief Patrons, Icarses-2020 Conference Committees and Review Panel Members, are available in this pdf.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".