Showing papers by "Indian Institute of Technology Indore published in 2020"

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches.

Abstract: This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen's Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (-8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901-1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions' also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.

Removal Attacks on Logic Locking and Camouflaging Techniques

Abstract: With the adoption of a globalized and distributed IC design flow, IP piracy, reverse engineering, and counterfeiting threats are becoming more prevalent. Logic obfuscation techniques including logic locking and IC camouflaging have been developed to address these emergent challenges. A major challenge for logic locking and camouflaging techniques is to resist Boolean satisfiability (SAT) based attacks that can circumvent state-of-the-art solutions within minutes. Over the past year, multiple SAT attack resilient solutions such as Anti-SAT and AND-tree insertion (ATI) have been presented. In this paper, we perform a security analysis of these countermeasures and show that they leave structural traces behind in their attempts to thwart the SAT attack. We present three attacks, namely “signal probability skew” (SPS) attack, “AppSAT guided removal (AGR) attack, and “sensitization guided SAT” (SGS) attack”, that can break Anti-SAT and ATI, within minutes.

Tumor-derived exosomes in the regulation of macrophage polarization

Abstract: This review focuses on exosomes derived from various cancer cells. The review discusses the possibility of differentiating macrophages in alternatively activated anti-inflammatory pro-tumorigenic M2 macrophage phenotypes and classically activated pro-inflammatory, anti-tumorigenic M1 macrophage phenotypes in the tumor microenvironment (TME). The review is divided into two main parts, as follows: (1) role of exosomes in alternatively activating M2-like macrophages-breast cancer-derived exosomes, hepatocellular carcinoma (HCC) cell-derived exosomes, lung cancer-derived exosomes, prostate cancer-derived exosomes, Oral squamous cell carcinoma (OSCC)—derived exosomes, epithelial ovarian cancer (EOC)—derived exosomes, Glioblastoma (GBM) cell-derived exosomes, and colorectal cancer-derived exosomes, (2) role of exosomes in classically activating M1-like macrophages, oral squamous cell carcinoma-derived exosomes, breast cancer-derived exosomes, Pancreatic-cancer derived modified exosomes, and colorectal cancer-derived exosomes, and (3) exosomes and antibody-dependent cellular cytotoxicity (ADCC). This review addresses the following subjects: (1) crosstalk between cancer-derived exosomes and recipient macrophages, (2) the role of cancer-derived exosome payload(s) in modulating macrophage fate of differentiation, and (3) intracellular signaling mechanisms in macrophages regarding the exosome’s payload(s) upon its uptake and regulation of the TME. Under the electron microscope, nanoscale exosomes appear as specialized membranous vesicles that emerge from the endocytic cellular compartments. Exosomes harbor proteins, growth factors, cytokines, lipids, miRNA, mRNA, and DNAs. Exosomes are released by many cell types, including reticulocytes, dendritic cells, B-lymphocytes, platelets, mast cells, and tumor cells. It is becoming clear that exosomes can impinge upon signal transduction pathways, serve as a mediator of signaling crosstalk, thereby regulating cell-to-cell wireless communications. Based on the vesicular cargo, the molecular constituents, the exosomes have the potential to change the fate of macrophage phenotypes, either M1, classically activated macrophages, or M2, alternatively activated macrophages. In this review, we discuss and describe the ability of tumor-derived exosomes in the mechanism of macrophage activation and polarization.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review

Abstract: Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.

6G White Paper on Machine Learning in Wireless Communication Networks

Abstract: The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented.

Quantum-enhanced multiobjective large-scale optimization via parallelism

Abstract: Traditional quantum-based evolutionary algorithms are intended to solve single-objective optimization problems or multiobjective small-scale optimization problems. However, multiobjective large-scale optimization problems are continuously emerging in the big-data era. Therefore, the research in this paper, which focuses on combining quantum mechanics with multiobjective large-scale optimization algorithms, will be beneficial to the study of quantum-based evolutionary algorithms. In traditional quantum-behaved particle swarm optimization (QPSO), particle position uncertainty prevents the algorithm from easily falling into a local optimum. Inspired by the uncertainty principle of position, the authors propose quantum-enhanced multiobjective large-scale algorithms, which are parallel multiobjective large-scale evolutionary algorithms (PMLEAs). Specifically, PMLEA-QDE, PMLEA-QjDE and PMLEA-QJADE are proposed by introducing the search mechanism of the individual particle from QPSO into differential evolution (DE), differential evolution with self-adapting control parameters (jDE) and adaptive differential evolution with optional external archive (JADE). Moreover, the proposed algorithms are implemented with parallelism to improve the optimization efficiency. Verifications performed on several test suites indicate that the proposed quantum-enhanced algorithms are superior to the state-of-the-art algorithms in terms of both effectiveness and efficiency.

Selective and Recyclable Congo Red Dye Adsorption by Spherical Fe 3 O 4 Nanoparticles Functionalized with 1,2,4,5-Benzenetetracarboxylic Acid

Abstract: In this study, the new material Fe3O4@BTCA has been synthesized by immobilization of 1,2,4,5-Benzenetetracarboxylic acid (BTCA) on the surface of Fe3O4 NPs, obtained by co-precipitation of FeCl3.6H2O and FeCl2.4H2O in the basic conditions. Characterization by P-XRD, FE-SEM, and TEM confirm Fe3O4 has a spherical crystalline structure with an average diameter of 15 nm, which after functionalization with BTCA, increases to 20 nm. Functionalization also enhances the surface area and surface charge of the material, confirmed by BET and zeta potential analyses, respectively. The dye adsorption capacity of Fe3O4@BTCA has been investigated for three common dyes; Congo red (C.R), Methylene blue (M.B), and Crystal violet (C.V). The adsorption studies show that the material rapidly and selectively adsorbs C.R dye with very high adsorption capacity (630 mg/g), which is attributed to strong H-bonding ability of BTCA with C.R dye as indicated by adsorption mechanism study. The material also shows excellent recyclability without any considerable loss of adsorption capacity. Adsorption isotherm and kinetic studies suggest that the adsorption occurs by the Langmuir adsorption model following pseudo-second-order adsorption kinetics.

Cost-Effective Video Summarization Using Deep CNN With Hierarchical Weighted Fusion for IoT Surveillance Networks

Abstract: Video summarization (VS) has attracted intense attention recently due to its enormous applications in various computer vision domains, such as video retrieval, indexing, and browsing. Traditional VS researches mostly target at the effectiveness of the VS algorithms by introducing the high quality of features and clusters for selecting representative visual elements. Due to the increased density of vision sensors network, there is a tradeoff between the processing time of the VS methods with reasonable and representative quality of the generated summaries. It is a challenging task to generate a video summary of significant importance while fulfilling the needs of Internet of Things (IoT) surveillance networks with constrained resources. This article addresses this problem by proposing a new computationally effective solution through designing a deep CNN framework with hierarchical weighted fusion for the summarization of surveillance videos captured in IoT settings. The first stage of our framework designs discriminative rich features extracted from deep CNNs for shot segmentation. Then, we employ image memorability predicted from a fine-tuned CNN model in the framework, along with aesthetic and entropy features to maintain the interestingness and diversity of the summary. Third, a hierarchical weighted fusion mechanism is proposed to produce an aggregated score for the effective computation of the extracted features. Finally, an attention curve is constituted using the aggregated score for deciding outstanding keyframes for the final video summary. Experiments are conducted using benchmark data sets for validating the importance and effectiveness of our framework, which outperforms the other state-of-the-art schemes.

Discovery of New Hydroxyethylamine Analogs Against 3CLpro Protein Target of SARS-CoV-2: Molecular Docking, Molecular Dynamics Simulation and Structure-Activity Relationship Studies

Abstract: The novel coronavirus, SARS-CoV-2, has caused a recent pandemic called COVID-19 and a severe health threat around the world. In the current situation, the virus is rapidly spreading worldwide, and the discovery of a vaccine and potential therapeutics are critically essential. The crystal structure for the main protease (Mpro) of SARS-CoV-2, 3-chymotrypsin-like cysteine protease (3CLpro), was recently made available and is considerably similar to the previously reported SARS-CoV. Due to its essentiality in viral replication, it represents a potential drug target. Herein, a computer-aided drug design (CADD) approach was implemented for the initial screening of 13 approved antiviral drugs. Molecular docking of 13 antivirals against the 3-chymotrypsin-like cysteine protease (3CLpro) enzyme was accomplished, and indinavir was described as a lead drug with a docking score of -8.824 and a XP Gscore of -9.466 kcal/mol. Indinavir possesses an important pharmacophore, hydroxyethylamine (HEA), and thus, a new library of HEA compounds (>2500) was subjected to virtual screening that led to 25 hits with a docking score more than indinavir. Exclusively, compound 16 with a docking score of -8.955 adhered to drug-like parameters, and the structure-activity relationship (SAR) analysis was demonstrated to highlight the importance of chemical scaffolds therein. Molecular dynamics (MD) simulation analysis performed at 100 ns supported the stability of 16 within the binding pocket. Largely, our results supported that this novel compound 16 binds with domains I and II, and the domain II-III linker of the 3CLpro protein, suggesting its suitability as a strong candidate for therapeutic discovery against COVID-19.

T cell engagement of cross-presenting microglia protects the brain from a nasal virus infection.

Abstract: The neuroepithelium is a nasal barrier surface populated by olfactory sensory neurons that detect odorants in the airway and convey this information directly to the brain via axon fibers. This barrier surface is especially vulnerable to infection, yet respiratory infections rarely cause fatal encephalitis, suggesting a highly evolved immunological defense. Here, using a mouse model, we sought to understand the mechanism by which innate and adaptive immune cells thwart neuroinvasion by vesicular stomatitis virus (VSV), a potentially lethal virus that uses olfactory sensory neurons to enter the brain after nasal infection. Fate-mapping studies demonstrated that infected central nervous system (CNS) neurons were cleared noncytolytically, yet specific deletion of major histocompatibility complex class I (MHC I) from these neurons unexpectedly had no effect on viral control. Intravital imaging studies of calcium signaling in virus-specific CD8+ T cells revealed instead that brain-resident microglia were the relevant source of viral peptide-MHC I complexes. Microglia were not infected by the virus but were found to cross-present antigen after acquisition from adjacent neurons. Microglia depletion interfered with T cell calcium signaling and antiviral control in the brain after nasal infection. Collectively, these data demonstrate that microglia provide a front-line defense against a neuroinvasive nasal infection by cross-presenting antigen to antiviral T cells that noncytolytically cleanse neurons. Disruptions in this innate defense likely render the brain susceptible to neurotropic viruses like VSV that attempt to enter the CNS via the nose.

Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals

Abstract: The neurological disease such as the epilepsy is diagnosed using the analysis of electroencephalogram (EEG) recordings. The areas of the brain associated with the consequence of epilepsy are termed as epileptogenic regions. The focal EEG signals are generated from epileptogenic areas, and the nonfocal signals are obtained from other regions of the brain. Thus, the classification of the focal and non-focal EEG signals are necessary for locating the epileptogenic areas during surgery for epilepsy. In this paper, we propose a novel method for the automated classification of focal and non-focal EEG signals. The method is based on the use of the synchrosqueezing transform (SST) and deep convolutional neural network (CNN) for the classification. The time-frequency matrices of EEG signal are evaluated using both Fourier SST (FSST) and wavelet SST (WSST). The two-dimensional (2D) deep CNN is used for the classification using the time-frequency matrix of EEG signals. The experimental results reveal that the proposed method attains the accuracy, sensitivity, and specificity values of more than 99% for the classification of focal and non-focal EEG signals. The method is compared with existing approaches for the discrimination of focal and non-focal categories of EEG signals.

Evidence of Spin-Orbital Angular Momentum Interactions in Relativistic Heavy-Ion Collisions.

Abstract: The first evidence of spin alignment of vector mesons (K0 and ϕ) in heavy-ion collisions at the Large Hadron Collider (LHC) is reported. The spin density matrix element ρ00 is measured at midrapidity (|y| < 0.5) in Pb-Pb collisions at a center-of-mass energy (√sNN) of 2.76 TeV with the ALICE detector. ρ00 values are found to be less than 1=3 (1=3 implies no spin alignment) at low transverse momentum (pT < 2 GeV/c) for K0 and ϕ at a level of 3σ and 2σ, respectively. No significant spin alignment is observed for the K^0_S meson (spin = 0) in Pb-Pb collisions and for the vector mesons in pp collisions. The measured spin alignment is unexpectedly large but qualitatively consistent with the expectation from models which attribute it to a polarization of quarks in the presence of angular momentum in heavy-ion collisions and a subsequent hadronization by the process of recombination.

Identification of a Potential Peptide Inhibitor of SARS-CoV-2 Targeting its Entry into the Host Cells.

Abstract: Coronavirus disease (COVID-19) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Due to the incessant spread of the disease with substantial morbidity and mortality rates, there is an urgent demand for effective therapeutics and vaccines to control and diminish this pandemic A critical step in the crosstalk between the virus and the host cell is the binding of SARS-CoV-2 spike protein to the angiotensin-converting enzyme 2 (ACE2) receptor present on the surface of the host cells Hence, inhibition of this interaction could be a promising strategy to combat the SARS-CoV-2 infection Docking and Molecular Dynamics (MD) simulation studies revealed that designed peptide maintains their secondary structure and provide a highly specific and stable binding (blocking) to SARS-CoV-2 We have designed a novel peptide that could inhibit SARS-CoV-2 spike protein interaction with ACE2, thereby blocking the cellular entry of the virus Our findings suggest that computationally developed inhibitory peptide may be developed as an anti-SARS-CoV-2 agent for the treatment of SARS-CoV-2 infection We further plan to pursue the peptide in cell-based assays and eventually for clinical trials

Polythiophene-nanoWO3 bilayer as an electrochromic infrared filter: a transparent heat shield

Abstract: A power-efficient, ultrafast and stable organic–inorganic hybrid electrochromic device fabricated using WO3 and P3HT as active materials has been demonstrated herein for dual application in an electrochromic window as well as an IR filter. The device when turned ON using a bias as low as 1 V shows heat shielding effect as well as increased optical transparency to keep the area cooler by ∼8 °C (22%) to maintain the room temperature at 302 K, as imaged using an IR camera. The solid state electrochromic IR filter device was fabricated in simple cross bar geometry with the WO3/P3HT bilayer sandwiched between two electrodes. The device works on the principle of a bias-induced redox change process, as established using electrochemical and spectroscopic investigations. The device exhibits superior electrochromic behavior with dual function, with a maximum color contrast of ∼60% in the IR region with excellent coloration efficiency of ∼380 cm2 C−1 at 520 nm and stability of more than 1600 ON/OFF cycles without compromising its performance. Possibility of the device to be used as an electrochromic window and heat shield has been demonstrated using real life experiments.

On the Performance of NOMA-Assisted Overlay Multiuser Cognitive Satellite-Terrestrial Networks

Abstract: In this letter, we investigate the performance of an overlay cognitive satellite-terrestrial network comprising a primary satellite source with its multiple terrestrial receivers and a secondary transmitter-receiver pair on the ground. Herein, the primary satellite source employs a non-orthogonal multiple access (NOMA) scheme to simultaneously serve its all users, while the secondary transmitter assists the primary communication through cooperative relaying technique in exchange for spectrum access. For this overall set-up, we obtain the closed-form expressions for the outage probability of primary satellite network and secondary terrestrial network by adopting pertinent heterogeneous fading models. Further, we derive the asymptotic outage behaviour at high signal-to-noise-ratio (SNR) for the primary and secondary networks, and thereby calculate the achievable diversity orders. Our analytical findings are corroborated through various numerical and simulation results.

Probing the Effects of Strong Electromagnetic Fields with Charge-Dependent Directed Flow in Pb-Pb Collisions at the LHC

Abstract: The first measurement at the LHC of charge-dependent directed flow (v1) relative to the spectator plane is presented for Pb-Pb collisions at √sNN p = 5.02 TeV. Results are reported for charged hadrons and D0 mesons for the transverse momentum intervals pT > 0.2 GeV=c and 3 < pT < 6 GeV=c in the 5%–40% and 10%–40% centrality classes, respectively. The difference between the positively and negatively charged hadron v1 has a positive slope as a function of pseudorapidity η, dΔv1=dη = [1.68 ± 0.49(stat) x 0.41(syst) × 10^−4. The same measurement for D^0 and D¯ ^0 mesons yields a positive value dΔv1/dη = [4.9 ± 1.7(stat) ± 0.6(syst) × 10^−1, which is about 3 orders of magnitude larger than the one of the charged hadrons. These measurements can provide new insights into the effects of the strong electromagnetic field and the initial tilt of matter created in noncentral heavy ion collisions on th e dynamics of light (u, d, and s) and heavy (c) quarks. The large difference between the observed Δv1 of charged hadrons and D0 mesons may reflect different sensitivity of the charm and light quarks to the early time dynamics of a heavy ion collision. These observations challenge some recent theoretical calculations, which predicted a negative and an order of magnitude smaller value of dΔv1=dη for both light flavor and charmed hadrons.