1,722,096 research outputs found
LMYE Studio: Maya Krishna Rao - The Solo Devising Performer; Pathways into Imagination and Process
No full textMaya Krishna Rao combines elements of performance lecture, workshop and improvisation to demonstrate the workings of her creative methodology and its gradual development. Spanning continents, genres and decades of Rao’s career this is a tightly picked 90 minutes with much to relish and enjoy. <br
LMYE Gallery #1: The Chord that Opens Up the Subconscious - Interview with Maya Krishna Rao
No full textInterview with Maya Krishna Rao about her works Khol Do (1993), A Deep Fried Jam (2002), Walk (2012), Loose Woman (2018) and Lockdown Stories (2020). This interview was recorded on 02.07.2020
Pseudanthias vizagensis Krishna, Rao, & Venu 2017
No full textPseudanthias vizagensis Krishna, Rao, & Venu, 2017 A junior synonym of Pseudanthias pillai Heemstra & Akhilesh, 2012. See account for that species from Anderson, 2018 (above). Syntypes: 44, 93–97 mm SL. Type locality: off Visakhapatnam, Andhra Pradesh, India, depth 180 meters. Illustrations: Krishna, Rao, & Venu, 2017:215, figs. 1 & 2. Counts: D: X, 16 or 17. A: III, 7. P: 17. C: 15. V: 26 (10 + 16). GR: 39 or 40 (11 + 28 or 29). LL: 44 or 45. Distribution: Indian Ocean: Bay of Bengal: east coast of India.Published as part of Anderson, William D., 2022, Additions and emendations to the annotated checklist of anthiadine fishes (Percoidei: Serranidae), pp. 567-578 in Zootaxa 5195 (6) on page 573, DOI: 10.11646/zootaxa.5195.6.5, http://zenodo.org/record/722395
Pseudanthias vizagensis Krishna, Rao 2017
No full textPseudanthias vizagensis Krishna, Rao, & Venu, 2017 Syntypes: 44, 93– 97 mm SL. Type locality: off Visakhapatnam, Andhra Pradesh, India, depth 180 meters. Illustrations: Krishna et al., 2017:215, figs. 1 & 2. D: X, 16 or 17. A: III, 7. P: 17. C: 15. V: 26 (10 + 16). GR: 39 or 40 (11 + 28 or 29). LL: 44 or 45. Distribution: Indian Ocean: Bay of Bengal: east coast of India.Published as part of William D. Anderson, Jr., 2018, Annotated checklist of anthiadine fishes (Percoidei: Serranidae), pp. 1-62 in Zootaxa 4475 (1) on page 49, DOI: 10.11646/zootaxa.4475.1.1, http://zenodo.org/record/145328
Polynomial-time learnability of logic programs with local variables from entailment
No full textAbstractIn this paper, we study exact learning of logic programs from entailment and present a polynomial time algorithm to learn a rich class of logic programs that allow local variables and include many standard programs like append, merge, split, delete, member, prefix, suffix, length, reverse, append/4 on lists, tree traversal programs on binary trees and addition, multiplication and exponentiation on natural numbers. Grafting a few aspects of incremental learning (Krishna Rao, Proc. Algorithmic Learning Theory, ALT’95, Lecture Notes in Artificial Intelligence, vol, 997, pp. 95–109. Revised version in Theoret. Comput. Sci. special issue on ALT’95 185 (1995) 193–213) onto the framework of learning from entailment (Arimura, Proc. Algorithmic Learning Theory, ALT’97, Lecture Notes in Artificial Intelligence, vol. 1316, 1997, pp. 432–445), we generalize the existing results to allow local variables, which play an important role of sideways information passing in the paradigm of logic programming
Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction
Get PDFAs non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned
Trap state spectroscopy in CMR manganites and spinel manganates using opto-impedance
No full textThe photo-induced AC-impedance method is used as a versatile instrumentation to study the trap state densities and the carrier
relaxation times in magneto-resistive manganites and magneto-conductive manganates and thus as a probe to check the crystal quality,
which is important for the performance of any device material. A comparative study using compounds of different defect densities is
presented. High defect concentrations in the compounds are identified through the photoresistivity and/or the photo-induced capacitive
build up
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