Study of edge states and conductivity in spin-orbit coupled bilayer graphene

We present an elaborate and systematic study of the conductance properties of a zigzag bilayer graphene nanoribbon modeled by a Kane-Mele (KM) Hamiltonian. The interplay of the Rashba and the intrinsic spin-orbit couplings with the edge states, electronic band structures, charge and spin transport are explored in details. We have analytically derived the conditions for the edge states for a bilayer KM nanoribbon and show how these modes decay for lattice sites inside the bulk. It is particularly interesting to note that for a finite-size ribbon an even number of zigzag ribbon hosts a finite energy gap at the Dirac points, while the odd ones do not. This asymmetry is present both in presence and absence of a bias voltage that may exist between the layers. The interlayer Rashba spin-orbit coupling, along with the intralayer intrinsic spin-orbit and intralayer Rashba spin-orbit couplings seem to destroy the quantum spin Hall (QSH) phase where the QSH phase is identified by the presence of a conductance plateau (of magnitude 4e/h) in the vicinity of zero Fermi energy. The plateau is sensitive to the values of the spin-orbit coupling parameters. Further, the spin polarized conductance data reveal that a bilayer KM ribbon is found to be more efficient for spintronic applications compared to a monolayer graphene. Finally, a quick check with experiments is done via computing the effective mass of electrons.Comment: 12 page

BIODEGRADABLE POLYMER: A NOVEL PHARMACEUTICAL CARRIER FOR SUSTAINED RELEASE OF METRONIDAZOLE

Objective: To optimize and evaluate the formulation of metronidazole (MT)-loaded chitosan microspheres and to investigate the efficiency of biodegradable polymer in developing sustained release formulation of MT to prolong the action of drug.Methods: MT microspheres were prepared using emulsion cross-linking method. Polymer-drug compatibility study was done using Fourier transform infrared. Physical characteristics were evaluated by particle size,SEM, flow properties etc. In vitro studies for evaluating drug release for MT-loaded chitosan microspheres were done by dissolution study.Results: Particle size of the formulated microspheres was found to be within the range of 110-130 μm. Flow properties of F1-F7 such as angle of repose, bulk density, and tapped density were found to be within limits. Drug entrapment efficiency was found to be better for all the formulations within the range of 74.82-84.32% w/w. Drug loading capacity was found to be in the range of 56-83.2% w/v. In vitro drug release was found to be in the range of 81.32-96.23% w/v.Conclusion: In spite of all the above results, we conclude that F5 formulation was optimized depending on the data obtained from the drug loading capacity and percentage drug release studies. F5 formulation is formulated with drug-polymer ratio 1:2 with 1% of di octyl sodium sulfo succinate and 8 ml of glutaraldehyde as a cross-linking agent

Library and Information Science Scholarly Journals Publishing Simulation: A Study

The author\u27s productivity is assessed based on publications, which requires a lot of motivation and time. Manuscripts get through several steps before being accepted and published. The purpose of this paper is to understand the time gap between acceptance to the publication of manuscripts in reputed journals of Library and Information Science. This paper is useful to contemporary researchers for knowing the journal publication duration. In this paper, we discussed the refereed and index journals in the field of library and information science. For this study, we collected the data from six LIS journals which were published from the 2020 January to December Asian region. The study focuses on detailed analyses of journal processing and publishing duration. The major contribution of this study gives the six LIS journal processing time they are: author manuscript submitted to accepted, accepted to published, and submitted to published period

Ensemble Based Feature Extraction and Deep Learning Classification Model with Depth Vision

It remains a challenging task to identify human activities from a video sequence or still image due to factors such as backdrop clutter, fractional occlusion, and changes in scale, point of view, appearance, and lighting. Different appliances, as well as video surveillance systems, human-computer interfaces, and robots used to study human behavior, require different activity classification systems. A four-stage framework for recognizing human activities is proposed in the paper. As part of the initial stages of pre-processing, video-to-frame conversion and adaptive histogram equalization (AHE) are performed. Additionally, watershed segmentation is performed and, from the segmented images, local texton XOR patterns (LTXOR), motion boundary scale-invariant feature transforms (MoBSIFT) and bag of visual words (BoW) based features are extracted. The Bidirectional gated recurrent unit (Bi-GRU) and the Bidirectional long short-term memory (Bi-LSTM) classifiers are used to detect human activity. In addition, the combined decisions of the Bi-GRU and Bi-LSTM classifiers are further fused, and their accuracy levels are determined. With this Dempster-Shafer theory (DST) technique, it is more likely that the results obtained from the analysis are accurate. Various metrics are used to assess the effectiveness of the deployed approach

Biofeedback: Can it be used as an Assessment Tool?

Biofeedback forms an integral part of Complementary and Alternate Medicine (CAM). It acts as a self-regulation technique through which individuals voluntarily learn to control what they believe are involuntary body processes. It records physiological signals using sensors and converts them into meaningful visual and auditory cues that provide feedback about physiological responses through a computer screen. It has been widely used as an intervention tool since the time of its development. The utility and effectiveness of biofeedback are not only restricted to illness but also to enhancing health and well-being. The biofeedback mechanism relies on two primary principles: Psychophysiological Mechanism and Operant Conditioning Mechanism. Applying the same mechanisms, biofeedback can also be used as an assessment tool. It may be used in research studies to assess the efficacy of a particular intervention at various data points and also be used in clinical practice to assess the improvement in the patient, which in turn will be a self-reinforcement for the patient. Thus, research in biofeedback as an assessment tool besides an effective intervention measure is warranted in both clinical studies and pure theoretical research

Handover-Count based Velocity Estimation of Cellular-Connected UAVs

Cellular-connected unmanned aerial vehicles (UAVs) are expected to play a major role in various civilian and commercial applications in the future. While existing cellular networks can provide wireless coverage to UAV user equipment (UE), such legacy networks are optimized for ground users which makes it challenging to provide reliable connectivity to aerial UEs. To ensure reliable and effective mobility management for aerial UEs, estimating the velocity of cellular-connected UAVs carries critical importance. In this paper, we introduce an approximate probability mass function (PMF) of handover count (HOC) for different UAV velocities and different ground base station (GBS) densities. Afterward, we derive the Cramer-Rao lower bound (CRLB) for the velocity estimate of a UAV, and also provide a simple unbiased estimator for the UAV's velocity which depends on the GBS density and HOC measurement time. Our simulation results show that the accuracy of velocity estimation increases with the GBS density and HOC measurement window. Moreover, the velocity of commercially available UAVs can be estimated efficiently with reasonable accuracy.Comment: Submitted to IEEE SPAWC 2020, Atlanta, G