Wigner distributions for an electron

We study the Wigner distributions for a physical electron, which reveal the multidimensional images of the electron. The physical electron is considered as a composite system of a bare electron and photon. The Wigner distributions for unpolarized, longitudinally polarized and transversely polarized electron are presented in transverse momentum plane as well as in impact parameter plane. The spin-spin correlations between the bare electron and the physical electron are discussed. We also evaluate all the leading twist generalized transverse momentum distributions (GTMDs) for electron.Comment: 27 pages, 18 figures, text modified, version accepted in Nuclear Physics

Equilibration of Quantum hall edges in symmetry broken bilayer graphene

Equilibration of quantum Hall edges is studied in a high quality dual gated bilayer graphene device in both unipolar and bipolar regime when all the degeneracies of the zero energy Landau level are completely lifted. We find that in the unipolar regime when the filling factor under the top gate region is higher than the back gate filling factor, the equilibration is partial based on their spin polarization. However, the complete mixing of the edge states is observed in the bipolar regime irrespective of their spin configurations due to the Landau level collapsing at the sharp pn junction in our thin hBN (~ 15 nm) encapsulated device, in consistent with the existing theory

Gravitational form factors and angular momentum densities in light-front quark-diquark model

We investigate the gravitational form factors (GFFs) and the longitudinal momentum densities ($p^+$ densities) for proton in a light-front quark-diquark model. The light-front wave functions are constructed from the soft-wall AdS/QCD prediction. The contributions from both the scalar and the axial vector diquarks are considered here. The results are compared with the consequences of a parametrization of nucleon generalized parton distributions (GPDs) in the light of recent MRST measurements of parton distribution functions (PDFs) and a soft-wall AdS/QCD model. The spatial distribution of angular momentum for up and down quarks inside the nucleon has been presented. At the density level, we illustrate different definitions of angular momentum explicitly for an up and down quark in the light-front quark-diquark model inspired by AdS/QCD.Comment: 14 pages, 9 figures; version to appear in EPJ

Equilibration of quantum hall edge states and its conductance fluctuations in graphene p-n junctions

We report an observation of conductance fuctuations (CFs) in the bipolar regime of quantum hall (QH) plateaus in graphene (p-n-p/n-p-n) devices. The CFs in the bipolar regime are shown to decrease with increasing bias and temperature. At high temperature (above 7 K) the CFs vanishes completely and the flat quantized plateaus are recovered in the bipolar regime. The values of QH plateaus are in theoretical agreement based on full equilibration of chiral channels at the p-n junction. The amplitude of CFs for different filling factors follows a trend predicted by the random matrix theory. Although, there are mismatch in the values of CFs between the experiment and theory but at higher filling factors the experimental values become closer to the theoretical prediction. The suppression of CFs and its dependence has been understood in terms of time dependent disorders present at the p-n junctions

Multiple Determiners in Magahi: A Case Beyond Agreement

The paper proposes that Magahi, a modern Indo-Aryan language, presents the phenomenon of multiple determiners in the syntax of modification and argues that the phenomenon is not a simple case of agreement in definiteness in the noun phrase whereby the additional determiner carries a similar semantic feature. I present examples that contest the possibility of it as a case of concord or agree. For the semantic motivation of the phenomenon, following Plank (2003) & Kumar (2020), the paper claims that the definite determiner /-wa/ in Magahi is not an exclusively dedicated definiteness morpheme, and therefore, the language needs an additional linguistic element. I claim that the additional determiner weakens the definiteness of the definite determiner /-wa/, creating a projection problem in the overall referentiality of the NP. By further describing the individual semantics of the determiner on the noun and the adjective, the paper claims that the determiner on the adjective exudes the semantics of specificity that can co-occur with the numeral. However, the determiner on the noun has the semantics of familiarity or identifiability. The paper further provides an exhaustive account of semantic and structural description and motivation of the phenomenon

DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification

Driving force of water entry into hydrophobic channels of carbon nanotubes: entropy or energy?

Spontaneous entry of water molecules inside single-wall carbon nanotubes (SWCNTs) has been confirmed by both simulations and experiments. Using molecular dynamics simulations, we have studied the thermodynamics of filling of a (6,6) carbon nanotube in a temperature range from 273 to 353 K and with different strengths of the nanotube-water interaction. From explicit energy and entropy calculations using the two-phase thermodynamics method, we have presented a thermodynamic understanding of the filling behaviour of a nanotube. We show that both the energy and the entropy of transfer decrease with increasing temperature. On the other hand, scaling down the attractive part of the carbon-oxygen interaction results in increased energy of transfer while the entropy of transfer increases slowly with decreasing the interaction strength. Our results indicate that both energy and entropy favour water entry into (6,6) SWCNTs. Our results are compared with those of several recent studies of water entry into carbon nanotubes.Comment: 18 pages, 5 figures, Molecular Simulation, 201