Optical conductivity study of screening of many-body effects in graphene interfaces

Theoretical studies have shown that electron-electron (e-e) and electron-hole (e-h) interactions play important roles in many observed quantum properties of graphene making this an ideal system to study many body effects. In this report we show that spectroscopic ellipsometry can enable us to measure this interactions quantitatively. We present spectroscopic data in two extreme systems of graphene on quartz (GOQ), an insulator, and graphene on copper (GOC), a metal which show that for GOQ, both e-e and e-h interactions dominate while for GOC e-h interactions are screened. The data further enables the estimation of the strength of the many body interaction through the effective fine structure constant, $\alpha_{g}^{*}$. The $\alpha_{g}^{*}$ for GOQ indicates a strong correlation with an almost energy independent value of about 1.37. In contrast, $\alpha_{g}^{*}$ value of GOC is photon energy dependent, is almost two orders of magnitude lower at low energies indicating very weak correlation.Comment: Main Article (4 pages, 4 figures); Supporting Online Material (12 pages, 9 figures

Estimation of the effect of long-range transport on seasonal variation of aerosols over northeastern India

Spectral aerosol optical depth (AOD) at ten discrete channels in the visible and near IR regions were estimated over Dibrugarh, located in the northeastern part of India, using a ground-based multi-wavelength solar radiometer (MWR) from October 2001 to February 2006. The observations reveal seasonal variations with low values of AODs in retreating monsoon and high values in the pre-monsoon season. Generally the AODs are high at shorter wavelengths and low at longer wavelengths. AOD spectra are relatively steep in winter compared to that in the monsoon period. The average value of AOD lies between 0.44±0.07 and 0.56±0.07 at 500 nm during the pre-monsoon season and between 0.19±0.02 and 0.22±0.02 during re-treating monsoon at the same wavelength. Comparison of MWR observation on Dibrugarh with satellite (MODIS) observation indicates a good correspondence between ground-based and satellite derived AODs. The synoptic wind pattern obtained from National Centre for Medium Range Weather Forecasting (NCMRWF), India and back trajectory analysis using the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) Model indicates that maximum contribution to aerosol extinction could be due to transport of pollutants from the industrialized and urban regions of India and large amounts of desert and mineral aerosols from the west Asian and Indian desert. Equal contributions from Bay-of- Bengal (BoB), in addition to that from the Indian landmass and west Asian desert leads to a further increase of AOD over the region of interest in the pre-monsoon seasons

MAC: A Meta-Learning Approach for Feature Learning and Recombination

Optimization-based meta-learning aims to learn an initialization so that a new unseen task can be learned within a few gradient updates. Model Agnostic Meta-Learning (MAML) is a benchmark algorithm comprising two optimization loops. The inner loop is dedicated to learning a new task and the outer loop leads to meta-initialization. However, ANIL (almost no inner loop) algorithm shows that feature reuse is an alternative to rapid learning in MAML. Thus, the meta-initialization phase makes MAML primed for feature reuse and obviates the need for rapid learning. Contrary to ANIL, we hypothesize that there may be a need to learn new features during meta-testing. A new unseen task from non-similar distribution would necessitate rapid learning in addition reuse and recombination of existing features. In this paper, we invoke the width-depth duality of neural networks, wherein, we increase the width of the network by adding extra computational units (ACU). The ACUs enable the learning of new atomic features in the meta-testing task, and the associated increased width facilitates information propagation in the forwarding pass. The newly learnt features combine with existing features in the last layer for meta-learning. Experimental results show that our proposed MAC method outperformed existing ANIL algorithm for non-similar task distribution by approximately 13% (5-shot task setting)Comment: 20 pages, 3 figures, 2 graph

Possible impact of a major oil-well fire on aerosol optical depth at Dibrugarh

This article does not have an abstract

Shear Strength of Continuous Lightly Reinforced T-Beams

The shear strength of continuous lightly reinforced concrete T -beams is studied. Six twospan T -beams with and without web reinforcement are tested. The primary variables are longitudinal reinforcement ratio (0.75% and 1.0%) and nominal stirrup strength (0 to 82 psi). The test results are analyzed and compared with the shear design provisions of "Building Code Requirements for Reinforced Concrete (ACI 318-89)" and predictions of other investigators, including predictions obtained using the modified compression field theory. The tests indicate that ACI 318-89 overpredicts the concrete shear capacity of lightly reinforced beams without shear reinforcement. Little difference exists between shear cracking stresses in the negative and positive moment regions for beams in the current study. For both the negative and positive moment regions, the stirrup contribution to shear strength exceeds the value predicted by ACI 318-89. Stirrup contribution to shear strength increases with increasing flexural reinforcement ratio. Overall, the ACI 318-89 shear provisions are conservative for the beams tested in the current study. Two procedures based on the modified compression field theory are also conservative. ACI 318-89 better predicts the nominal shear strength of the beams in the current study than either of the modified compression field theory procedures

Liver injury in COVID-19: The hepatic aspect of the respiratory syndrome — what we know so far

© 2020. All Rights Reserved. The 2019 novel coronavirus disease (COVID-19) pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a serious threat to global public health. Although primarily, the infection causes lung injury, liver enzyme abnormalities have also been reported to occur during the course of the disease. We conducted an extensive literature review using the PubMed database on articles covering a broad range of issues related to COVID-19 and hepatic injury. The present review summarizes available information on the spectrum of liver involvement, the possible mechanisms and risk factors of liver injury due to SARS-CoV-2 infection, and the prognostic significance of the presence of liver injury. Hopefully, this review will enable clinicians, especially the hepatologists, to understand and manage the liver derangements they may encounter in these atients better and provide guidance for further studies on the liver injury of COVID-19