Indian Institute of Technology Bhubaneswar

In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in the current online and printed version. doi:10.1093/ptep/ptz106.

Abstract Cyclonic storms associated with the midlatitude Subtropical Westerly Jet (SWJ), referred to as Western Disturbances (WDs), play a critical role in the meteorology of the Indian subcontinent. WDs embedded in the southward propagating SWJ produce extreme precipitation over northern India and are further enhanced over the Himalayas due to orographic land‐atmosphere interactions. During December, January, and February, WD snowfall is the dominant precipitation input to establish and sustain regional snowpack, replenishing regional water resources. Spring melt is the major source of runoff to northern Indian rivers and can be linked to important hydrologic processes from aquifer recharge to flashfloods. Understanding the dynamical structure, evolution‐decay, and interaction of WDs with the Himalayas is therefore necessary to improve knowledge which has wide ranging socioeconomic implications beyond short‐term disaster response including cold season agricultural activities, management of water resources, and development of vulnerability‐adaptive measures. In addition, WD wintertime precipitation provides critical mass input to existing glaciers and modulates the albedo characteristics of the Himalayas and Tibetan Plateau, affecting large‐scale circulation and the onset of the succeeding Indian Summer Monsoon. Assessing the impacts of climate variability and change on the Indian subcontinent requires fundamental understanding of the dynamics of WDs. In particular, projected changes in the structure of the SWJ will influence evolution‐decay processes of the WDs and impact Himalayan regional water availability. This review synthesizes past research on WDs with a perspective to provide a comprehensive assessment of the state of knowledge to assist both researchers and policymakers, and context for future research.

We report the first measurement of the $\ensuremath{\tau}$ lepton polarization ${P}_{\ensuremath{\tau}}({D}^{*})$ in the decay $\overline{B}\ensuremath{\rightarrow}{D}^{*}{\ensuremath{\tau}}^{\ensuremath{-}}{\overline{\ensuremath{\nu}}}_{\ensuremath{\tau}}$ as well as a new measurement of the ratio of the branching fractions $R({D}^{*})=\mathcal{B}(\overline{B}\ensuremath{\rightarrow}{D}^{*}{\ensuremath{\tau}}^{\ensuremath{-}}{\overline{\ensuremath{\nu}}}_{\ensuremath{\tau}})/\mathcal{B}(\overline{B}\ensuremath{\rightarrow}{D}^{*}{\ensuremath{\ell}}^{\ensuremath{-}}{\overline{\ensuremath{\nu}}}_{\ensuremath{\ell}})$, where ${\ensuremath{\ell}}^{\ensuremath{-}}$ denotes an electron or a muon, and the $\ensuremath{\tau}$ is reconstructed in the modes ${\ensuremath{\tau}}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{\ensuremath{-}}{\ensuremath{\nu}}_{\ensuremath{\tau}}$ and ${\ensuremath{\tau}}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\rho}}^{\ensuremath{-}}{\ensuremath{\nu}}_{\ensuremath{\tau}}$. We use the full data sample of $772\ifmmode\times\else\texttimes\fi{}1{0}^{6}\text{ }\text{ }B\overline{B}$ pairs recorded with the Belle detector at the KEKB electron-positron collider. Our results, ${P}_{\ensuremath{\tau}}({D}^{*})=\ensuremath{-}0.38\ifmmode\pm\else\textpm\fi{}0.51{(\text{stat})}_{\ensuremath{-}0.16}^{+0.21}(\text{syst})$ and $R({D}^{*})=0.270\ifmmode\pm\else\textpm\fi{}0.035{(\text{stat})}_{\ensuremath{-}0.025}^{+0.028}(\text{syst})$, are consistent with the theoretical predictions of the standard model.

Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from Twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.

Two dimensional layered inorganic nanomaterials (2D-LINs) have recently attracted huge interest because of their unique thickness dependent physical and chemical properties and potential technological applications. The properties of these layered materials can be tuned via both physical and chemical processes. Some 2D layered inorganic nanomaterials like MoS2, WS2 and SnS2 have been recently developed and employed in various applications, including new sensors because of their layer-dependent electrical properties. This article presents a comprehensive overview of recent developments in the application of 2D layered inorganic nanomaterials as sensors. Some of the salient features of 2D materials for different sensing applications are discussed, including gas sensing, electrochemical sensing, SERS and biosensing, SERS sensing and photodetection. The working principles of the sensors are also discussed together with examples.

We report here the synthesis of layer structured WS2/reduced graphene oxide (RGO) hybrids by a facile hydrothermal method for its possible application as supercapacitor materials in energy storage devices. The prepared two-dimensional materials are characterized thoroughly by various analytical techniques to ascertain their structure and to confirm the absence of any impurities. Two-electrode capacitance measurements have been carried out in aqueous 1 M Na2SO4. The WS2/RGO hybrids exhibited enhanced supercapacitor performance with specific capacitance of 350 F/g at a scan rate of 2 mV/s. The obtained capacitance values of WS2/RGO hybrids are about 5 and 2.5 times higher than bare WS2 and RGO sheets. Because of the unique microstructure with combination of two layered materials, WS2/RGO hybrids emerge as a promising supercapacitor electrode material with high specific capacitance, energy density, and excellent cycling stability.

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In July 2012, the ATLAS and CMS collaborations at the CERN Large Hadron Collider announced the observation of a Higgs boson at a mass of around 125 gigaelectronvolts. Ten years later, and with the data corresponding to the production of a 30-times larger number of Higgs bosons, we have learnt much more about the properties of the Higgs boson. The CMS experiment has observed the Higgs boson in numerous fermionic and bosonic decay channels, established its spin-parity quantum numbers, determined its mass and measured its production cross-sections in various modes. Here the CMS Collaboration reports the most up-to-date combination of results on the properties of the Higgs boson, including the most stringent limit on the cross-section for the production of a pair of Higgs bosons, on the basis of data from proton-proton collisions at a centre-of-mass energy of 13 teraelectronvolts. Within the uncertainties, all these observations are compatible with the predictions of the standard model of elementary particle physics. Much evidence points to the fact that the standard model is a low-energy approximation of a more comprehensive theory. Several of the standard model issues originate in the sector of Higgs boson physics. An order of magnitude larger number of Higgs bosons, expected to be examined over the next 15 years, will help deepen our understanding of this crucial sector.

This paper presents an intelligent protection scheme for microgrid using combined wavelet transform and decision tree. The process starts at retrieving current signals at the relaying point and preprocessing through wavelet transform to derive effective features such as change in energy, entropy, and standard deviation using wavelet coefficients. Once the features are extracted against faulted and unfaulted situations for each-phase, the data set is built to train the decision tree (DT), which is validated on the unseen data set for fault detection in the microgrid. Further, the fault classification task is carried out by including the wavelet based features derived from sequence components along with the features derived from the current signals. The new data set is used to build the DT for fault detection and classification. Both the DTs are extensively tested on a large data set of 3860 samples and the test results indicate that the proposed relaying scheme can effectively protect the microgrid against faulty situations, including wide variations in operating conditions.

In this paper, a novel algorithm, based on dc link voltage, is proposed for effective energy management of a standalone permanent magnet synchronous generator (PMSG)-based variable speed wind energy conversion system consisting of battery, fuel cell, and dump load (i.e., electrolyzer). Moreover, by maintaining the dc link voltage at its reference value, the output ac voltage of the inverter can be kept constant irrespective of variations in the wind speed and load. An effective control technique for the inverter, based on the pulsewidth modulation (PWM) scheme, has been developed to make the line voltages at the point of common coupling (PCC) balanced when the load is unbalanced. Similarly, a proper control of battery current through dc-dc converter has been carried out to reduce the electrical torque pulsation of the PMSG under an unbalanced load scenario. Based on extensive simulation results using MATLAB/SIMULINK, it has been established that the performance of the controllers both in transient as well as in steady state is quite satisfactory and it can also maintain maximum power point tracking.

In this paper, we propose a novel signal quality-aware Internet of Things (IoT)-enabled electrocardiogram (ECG) telemetry system for continuous cardiac health monitoring applications. The proposed quality-aware ECG monitoring system consists of three modules: 1) ECG signal sensing module; 2) automated signal quality assessment (SQA) module; and 3) signal-quality aware (SQAw) ECG analysis and transmission module. The main objectives of this paper are: design and development of a light-weight ECG SQA method for automatically classifying the acquired ECG signal into acceptable or unacceptable class and real-time implementation of proposed IoT-enabled ECG monitoring framework using ECG sensors, Arduino, Android phone, Bluetooth, and cloud server. The proposed framework is tested and validated using the ECG signals taken from the MIT-BIH arrhythmia and Physionet challenge databases and the real-time recorded ECG signals under different physical activities. Experimental results show that the proposed SQA method achieves promising results in identifying the unacceptable quality of ECG signals and outperforms existing methods based on the morphological and RR interval features and machine learning approaches. This paper further shows that the transmission of acceptable quality of ECG signals can significantly improve the battery lifetime of IoT-enabled devices. The proposed quality-aware IoT paradigm has great potential for assessing clinical acceptability of ECG signals in improvement of accuracy and reliability of unsupervised diagnosis system.

We report a measurement of the ratio $\mathcal{R}({D}^{*})=\mathcal{B}({\overline{B}}^{0}\ensuremath{\rightarrow}{D}^{*+}{\ensuremath{\tau}}^{\ensuremath{-}}{\overline{\ensuremath{\nu}}}_{\ensuremath{\tau}})/\mathcal{B}({\overline{B}}^{0}\ensuremath{\rightarrow}{D}^{*+}{\ensuremath{\ell}}^{\ensuremath{-}}{\overline{\ensuremath{\nu}}}_{\ensuremath{\ell}})$, where $\ensuremath{\ell}$ denotes an electron or a muon. The results are based on a data sample containing $772\ifmmode\times\else\texttimes\fi{}1{0}^{6}\text{ }\text{ }B\overline{B}$ pairs recorded at the $\mathrm{\ensuremath{\Upsilon}}(4S)$ resonance with the Belle detector at the KEKB ${e}^{+}{e}^{\ensuremath{-}}$ collider. We select a sample of ${B}^{0}{\overline{B}}^{0}$ pairs by reconstructing both $B$ mesons in semileptonic decays to ${D}^{*\ensuremath{\mp}}{\ensuremath{\ell}}^{\ifmmode\pm\else\textpm\fi{}}$. We measure $\mathcal{R}({D}^{*})=0.302\ifmmode\pm\else\textpm\fi{}0.030(\text{stat})\ifmmode\pm\else\textpm\fi{}0.011(\text{syst})$, which is within $1.6\ensuremath{\sigma}$ of the Standard Model theoretical expectation, where the standard deviation $\ensuremath{\sigma}$ includes systematic uncertainties. We use this measurement to constrain several scenarios of new physics in a model-independent approach.

Abstract Speleothem proxy records from northeastern (NE) India reflect seasonal changes in Indian summer monsoon strength as well as moisture source and transport paths. We have analyzed a new speleothem record from Mawmluh Cave, Meghalaya, India, in order to better understand these processes. The data show a strong wet phase 33,500–32,500 years B.P. followed by a weak/dry phase from 26,000 to 23,500 years B.P. and a very weak phase from 17,000 to 15,000 years B.P. The record suggests abrupt increase in strength during the Bølling‐Allerød and early Holocene periods and pronounced weakening during the Heinrich and Younger Dryas cold events. We infer that these changes in monsoon strength are driven by changes in temperature gradients which drive changes in winds and moisture transport into northeast India.

We present a measurement of angular observables and a test of lepton flavor universality in the $B\ensuremath{\rightarrow}{K}^{*}{\ensuremath{\ell}}^{+}{\ensuremath{\ell}}^{\ensuremath{-}}$ decay, where $\ensuremath{\ell}$ is either $e$ or $\ensuremath{\mu}$. The analysis is performed on a data sample corresponding to an integrated luminosity of $711\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ containing $772\ifmmode\times\else\texttimes\fi{}1{0}^{6}$ $B\overline{B}$ pairs, collected at the $\mathrm{\ensuremath{\Upsilon}}(4S)$ resonance with the Belle detector at the asymmetric-energy ${e}^{+}{e}^{\ensuremath{-}}$ collider KEKB. The result is consistent with standard model (SM) expectations, where the largest discrepancy from a SM prediction is observed in the muon modes with a local significance of $2.6\ensuremath{\sigma}$.

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease which caused global health emergency and announced as pandemic disease by World Health Organization. Lack of specific drug molecules or treatment strategy against this disease makes it more devastating. Thus, there is an urgent need of effective drug molecules to fight against COVID-19. The main protease (Mpro) of SARS CoV-2, a key component of this viral replication, is considered as a prime target for anti-COVID-19 drug development. In order to find potent Mpro inhibitors, we have selected eight polyphenols from green tea, as these are already known to exert antiviral activity against many RNA viruses. We have elucidated the binding affinities and binding modes between these polyphenols including a well-known Mpro inhibitor N3 (having binding affinity -7.0 kcal/mol) and Mpro using molecular docking studies. All eight polyphenols exhibit good binding affinity toward Mpro (-7.1 to -9.0 kcal/mol). However, only three polyphenols (epigallocatechin gallate, epicatechingallate and gallocatechin-3-gallate) interact strongly with one or both catalytic residues (His41 and Cys145) of Mpro. Molecular dynamics simulations (100 ns) on these three Mpro-polyphenol systems further reveal that these complexes are highly stable, experience less conformational fluctuations and share similar degree of compactness. Estimation of total number of intermolecular H-bond and MM-GBSA analysis affirm the stability of these three Mpro-polyphenol complexes. Pharmacokinetic analysis additionally suggested that these polyphenols possess favorable drug-likeness characteristics. Altogether, our study shows that these three polyphenols can be used as potential inhibitors against SARS CoV-2 Mpro and are promising drug candidates for COVID-19 treatment.

This study presents the effect of incorporating metakaolin (MK) on the mechanical and durability properties of high strength concrete for a constant water/binder ratio of 0.3. MK mixtures with cement replacement of 5, 10 and 15 % were designed for target strength and slump of 90 MPa and 100 25 mm. From the results, it was observed that 10 % replacement level was the optimum level in terms of compressive strength. Beyond 10 % replacement levels, the strength was decreased but remained higher than the control mixture. Compressive strength of 106 MPa was achieved at 10 % replacement. Splitting tensile strength and elastic modulus values have also followed the same trend. In durability tests MK concretes have exhibited high resistance compared to control and the resistance increases as the MK percentage increases. This investigation has shown that the local MK has the potential to produce high strength and high performance concretes.

We propose a graphene-based photonic crystal fiber (PCF) sensor based on surface plasmon resonance. Graphene helps in prevention of oxidation of the silver layer used as a plasmonic active metal. The birefringent nature of the structure allows one component of the core guided mode to be more sensitive. Further, this structure does not need filling of the voids. The structural parameter of PCF and metal thickness has been optimized. The proposed sensor shows high amplitude sensitivity of 860 RIU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and has a resolution as high as 4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> RIU. This reported performance is higher than bimetallic (gold on silver) configuration.

Abstract The measurement of the luminosity recorded by the CMS detector installed at LHC interaction point 5, using proton–proton collisions at $$\sqrt{s}=13\,{\text {TeV}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mspace/> <mml:mtext>TeV</mml:mtext> </mml:mrow> </mml:math> in 2015 and 2016, is reported. The absolute luminosity scale is measured for individual bunch crossings using beam-separation scans (the van der Meer method), with a relative precision of 1.3 and 1.0% in 2015 and 2016, respectively. The dominant sources of uncertainty are related to residual differences between the measured beam positions and the ones provided by the operational settings of the LHC magnets, the factorizability of the proton bunch spatial density functions in the coordinates transverse to the beam direction, and the modeling of the effect of electromagnetic interactions among protons in the colliding bunches. When applying the van der Meer calibration to the entire run periods, the integrated luminosities when CMS was fully operational are 2.27 and 36.3 $$\,\text {fb}^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mspace/> <mml:msup> <mml:mtext>fb</mml:mtext> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> in 2015 and 2016, with a relative precision of 1.6 and 1.2%, respectively. These are among the most precise luminosity measurements at bunched-beam hadron colliders.

This paper presents a data-mining-based intelligent differential protection scheme for the microgrid. The proposed scheme preprocesses the faulted current and voltage signals using discrete Fourier transform and estimates the most affected sensitive features at both ends of the respective feeder. Furthermore, differential features are computed from the corresponding features at both ends of the feeder and are used to build the decision tree-based data-mining model for registering the final relaying decision. The proposed scheme is extensively validated for fault situations in the standard IEC microgrid model with wide variations in operating parameters for radial and mesh topology in grid-connected and islanded modes of operation. The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.

Electrocardiogram (ECG) signal quality assessment (SQA) plays a vital role in significantly improving the diagnostic accuracy and reliability of unsupervised ECG analysis systems. In practice, the ECG signal is often corrupted with different kinds of noises and artifacts. Therefore, numerous SQA methods were presented based on the ECG signal and/or noise features and the machine learning classifiers and/or heuristic decision rules. This paper presents an overview of current state-of-the-art SQA methods and highlights the practical limitations of the existing SQA methods. Based upon past and our studies, it is noticed that a lightweight ECG noise analysis framework is highly demanded for real-time detection, localization, and classification of single and combined ECG noises within the context of wearable ECG monitoring devices which are often resource constrained.