Phase diagram and upper critical field of homogenously disordered epitaxial 3-dimensional NbN films

We report the evolution of superconducting properties with disorder, in 3-dimensional homogeneously disordered epitaxial NbN thin films. The effective disorder in NbN is controlled from moderately clean limit down to Anderson metal-insulator transition by changing the deposition conditions. We propose a phase diagram for NbN in temperature-disorder plane. With increasing disorder we observe that as kFl-->1 the superconducting transition temperature (Tc) and minimum conductivity (sigma_0) go to zero. The phase diagram shows that in homogeneously disordered 3-D NbN films, the metal-insulator transition and the superconductor-insulator transition occur at a single quantum critical point at kFl~1.Comment: To appear in Journal of Superconductivity and Novel Magnetism (ICSM2010 proceedings

Modelling & Analyzing View Growth Pattern of YouTube Videos inculcating the impact of Subscribers, Word of Mouth and Recommendation Systems

YouTube, one of the prominent online video-sharing platforms, plays a pivotal role in modern media consumption, making it crucial to understand and predict the view-count dynamics of its videos. The viewership of YouTube videos can be influenced by three distinct sources: subscribers, word-of-mouth, and recommendation systems. This paper presents a comprehensive modelling framework that takes into account the view-count obtained through these three sources, assuming that a single view-count can only be attributed to one of these sources at any given time. We investigate the interplay among these sources in shaping YouTube video view-count dynamics, proposing a novel approach to model and analyse their impact on video popularity. Additionally, the VIKOR multi-criteria decision-making method is employed to validate and rank our proposed models. This study's findings deepen our understanding of the intricate mechanisms within the YouTube ecosystem, offering insights for predicting and managing video viewership

Neonatal outcome in early term and late term pregnancy

Background: Full-term neonates born between 37- and 41-weeks’ gestational age have been considered a homogeneous, low-risk group. However, recent evidence from studies has pointed toward increased NICU admissions and morbidity associated with births (37-38 weeks) compared with term neonates (39-41 weeks). The objective of this study was to compare the short-term morbidity of early-term vs term neonates in a county-based birth cohort using the primary objective of admission to a neonatal intensive care unit (NICU) or neonatology service. Methods: Retrospective observational population-based 2 year birth cohort study at Department of Obstetrics and Gynecology GSVM Medical College, Kanpur. All full-term live births comprised the birth cohort; this information was obtained from the hospitals’ perinatal databases, and data pertaining to NICU, or neonatology service admissions were extracted from individual medical records.  Gestational age of early term (37 0/7-38 6/7 weeks) verses term (39 0/7-4 10/7 weeks). Admission to the NICU or neonatology service. Results: There were 17,132 live births during the 2 year period, of which 13679 had a gestational age between 37 and 41 weeks. Of all live births, 6204 (45.3%) were early term. Compared with term infants, early-term neonates had significantly higher risks for the following: hypoglycaemia (29.9% verses 14.7%), NICU or neonatology service admission (20.9% vs12.05 %), need for respiratory support (36.8% verses 29.9%), treatment with intravenous antibiotics [39.4% verses 25. Delivery by caesarean section was common among early-term births (45.9%)]. Conclusions: Early-term births are associated with high neonatal morbidity and with NICU or neonatology service admission. Evaluation of local prevalence data will assist in implementation of specific preventive measures and plans, as well as prioritize limited health care resources

Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach

This article presents our unimodal privacy-safe and non-individual proposal for the audio-video group emotion recognition subtask at the Emotion Recognition in the Wild (EmotiW) Challenge 2020 1. This sub challenge aims to classify in the wild videos into three categories: Positive, Neutral and Negative. Recent deep learning models have shown tremendous advances in analyzing interactions between people, predicting human behavior and affective evaluation. Nonetheless, their performance comes from individual-based analysis, which means summing up and averaging scores from individual detections, which inevitably leads to some privacy issues. In this research, we investigated a frugal approach towards a model able to capture the global moods from the whole image without using face or pose detection, or any individual-based feature as input. The proposed methodology mixes state-of-the-art and dedicated synthetic corpora as training sources. With an in-depth exploration of neural network architectures for group-level emotion recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF test set (eleventh place of the challenge). Given that the analysis is unimodal based only on global features and that the performance is evaluated on a real-world dataset, these results are promising and let us envision extending this model to multimodality for classroom ambiance evaluation, our final target application

Phase diagram of a strongly disordered s-wave superconductor, NbN, close to the metal-insulator transition

We present a phase diagram as a function of disorder in three-dimensional NbN thin films, as the system enters the critical disorder for the destruction of the superconducting state. The superconducting state is investigated using a combination of magnetotransport and tunneling spectroscopy measurements. Our studies reveal 3 different disorder regimes. At low disorder the (k_{F}l~10-4), the system follows the mean field Bardeen-Cooper-Schrieffer behavior where the superconducting energy gap vanishes at the temperature where electrical resistance appears. For stronger disorder (k_{F}l<4) a "pseudogap" state emerges where a gap in the electronic spectrum persists up to temperatures much higher than Tc, suggesting that Cooper pairs continue to exist in the system even after the zero resistance state is destroyed. Finally, very strongly disordered samples (k_{F}l<1) exhibit a pronounced magnetoresistance peak at low temperatures, suggesting that localized Cooper pairs continue to survive in the system even after the global superconducting ground state is completely destroyed.Comment: pdf file with figures (Modified Version

An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results

An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

537-542Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results

Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA)

Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs