Manipal University

About: Manipal University is a education organization based out in Manipal, Karnataka, India. It is known for research contribution in the topics: Population & Health care. The organization has 9525 authors who have published 11207 publications receiving 110687 citations.

Wavelet singular entropy-based symmetrical fault-detection and out-of-step protection during power swing

Abstract: Fault-detection and out-of-step protection functions are two important requirements in distance relays while dealing with power-swing conditions. In the proposed research, the first part focuses on developing wavelet entropy-based out-of-step blocking function during stable power swing and tripping function during unstable power swing. The process starts at retrieving the current signal samples during power swing and process it through wavelet transform to derive singular values, used to find out Shannon entropy, called wavelet singular entropy (WSE). Further, the power swing indicators are computed to distinguish stable power swing from unstable ones. The second part computes WSE-based indicator to distinguish faults from power swing. The WSE blocks the relay during power swing and issue the tripping signal during fault conditions based on a set threshold. The proposed technique is extensively tested for different stable and unstable power swing conditions providing improved response time for fault detection during power swing and distinguishing stable power swings from unstable ones.

Multiscale factors affecting human attitudes toward snow leopards and wolves.

Abstract: The threat posed by large carnivores to livestock and humans makes peaceful coexistence between them difficult. Effective implementation of conservation laws and policies depends on the attitudes of local residents toward the target species. There are many known correlates of human attitudes toward carnivores, but they have only been assessed at the scale of the individual. Because human societies are organized hierar- chically, attitudes are presumably influenced by different factors at different scales of social organization, but this scale dependence has not been examined. We used structured interview surveys to quantitatively assess the attitudes of a Buddhist pastoral community toward snow leopards (Panthera uncia) and wolves (Canis lupus). We interviewed 381 individuals from 24 villages within 6 study sites across the high-elevation Spiti Valley in the Indian Trans-Himalaya. We gathered information on key explanatory variables that together captured variation in individual and village-level socioeconomic factors. We used hierarchical linear models to examine how the effect of these factors on human attitudes changed with the scale of analysis from the individual to the community. Factors significant at the individual level were gender, education, and age of the respondent (for wolves and snow leopards), number of income sources in the family (wolves), agricultural production, and large-bodied livestock holdings (snow leopards). At the community level, the significant factors included the number of smaller-bodied herded livestock killed by wolves and mean agricultural production (wolves) and village size and large livestock holdings (snow leopards). Our results show that scaling up from the individual to higher levels of social organization can highlight important factors that influence attitudes of people toward wildlife and toward formal conservation efforts in general. Such scale-specific information can help managers apply conservation measures at appropriate scales. Our results reiterate the need for conflict management programs to be multipronged.

Injectable small molecule hydrogel as a potential nanocarrier for localized and sustained in vivo delivery of doxorubicin

Abstract: The majority of the localized drug delivery systems are based on polymeric or polypeptide scaffolds, as weak intermolecular interactions of low molecular weight hydrogelators (LMHGs, Mw <500 Da) are significantly perturbed in the presence of anticancer drugs. Here, we present l-alanine derived low molecular weight hydrogelators (LMHGs) that remain injectable even after entrapping the anticancer drug doxorubicin (DOX). These DOX containing nanoassemblies (DOX-Gel) showed promising anticancer activity in mice models. Subcutaneous injection of DOX-Gel near the tumor achieved a greater decrease in tumour load than by intravenous injection of DOX (DOX-IV), and local injection of DOX alone (DOX-Local) at the tumor site. We noticed that DOX-Gel nanocarriers are especially effective when injected during the early stage of tumor progression, and achieve a substantial decrease in tumor load in the long term.

Efficient prediction of drug–drug interaction using deep learning models

Abstract: A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.