Ranching of clams in the Ashtamudi lake

Ranching of clams in the Ashtamudi lak

Synthesis of some 6,6' -methylene- and 6,6' -sulphone-biscoumarins

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Cryptostegia grandiflora -a potential multi-use crop

Abstract Cryptostegia grandiflora, a member of Asclepiadaceae, was evaluated as a potential multi-use crop. The plant contained 14.0% protein, 6.5% oil, 6.9% polyphenol, and 2.13% hydrocarbon. The gross heat value of the species was 3878.0 cal/g, while the oil fraction was 7350.1 cal/g, and the hydrocarbon fraction was 9300.0 cal/g. The NMR spectra of the hydrocarbon fraction reveals the presence of cis-polyisoprene (natural rubber). The oil fraction contains both saturated and unsaturated fatty acids including: lauric acid (trace), myristic acid (15.24%), palmitic acid (25.90%), stearic acid (3.8%), oleic acid (8.0%), linoleic acid (24.76%), and arachidic acid (22.28%). The high proportion of saturated fatty acids and the high oil content (\5.0%) make C. grandiflora a potential source for industrial raw material and alternative for conventional oil

Exploited marine fishery resources off Tuticorin along the Gulf of Mannar coast

Tuticorin coast of Gulf of Mannar is endowed with rocky bottom, coral reefs and sea grass beds with characteristic flora and fauna. It also acts as home for several endangered marine mammals, sea cows and marine turtles. These resources were exploited by a variety of gears during 2000-2005

Point-based Gesture Recognition Techniques

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. In this paper, we describe some of Gesture recognition techniques such as Vision based gesture recognition and Graph based gesture recognition. Also, we explore these techniques with previous studies

Point-based Gesture Recognition Techniques

Gesture recognition is a computing process that attempts to recognize and interpret human gestures through the use of mathematical algorithms. In this paper, we describe Point Based Gesture Recognition and Point Clouds nearest neighbors and sampling. Also, we explore these techniques with previous studies

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance

Carangids in the Seas around India-2

Carangids in the Seas around Indi