Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

We investigate the relation between the amount of entanglement localized on a chosen subsystem of a multiqubit system via local measurements on the rest of the system and the bipartite entanglement that is lost during this measurement process. We study a number of paradigmatic pure states, including the generalized Greenberger-Horne-Zeilinger (GHZ), the generalized W (gW), Dicke and the generalized Dicke states. For the generalized GHZ and W states, we analytically derive bounds on localizable entanglement in terms of the entanglement present in the system prior to the measurement. Also, for the Dicke and the generalized Dicke states, we demonstrate that with increasing system size, localizable entanglement tends to be equal to the bipartite entanglement present in the system over a specific partition before measurement. We extend the investigation numerically in the case of arbitrary multiqubit pure states. We also analytically determine the modification of these results, including the proposed bounds, in situations where these pure states are subjected to single-qubit phase-flip noise on all qubits. Additionally, we study one-dimensional paradigmatic quantum spin models, namely, the transverse-field XY model and the XXZ model in an external field and numerically demonstrate a cubic dependence of the localized entanglement on the lost entanglement. We show that this relation is robust even in the presence of disorder in the strength of the external field. © 2023 American Physical Society.

World University root node

Department of Physics

Physical Review A

Controlling gain with loss: Bounds on localizable entanglement in multiqubit systems

Chemical synthesis of glycoconjugates is essential for studying the biological functions of carbohydrates. We herein report an efficient approach for the stereoselective synthesis of challenging α-linked glycoconjugates via a RhII/chiral phosphoric acid (CPA)-cocatalyzed dynamic kinetic anomeric O-alkylation of sugar-derived lactols via carbenoid insertion to the anomeric OH bond. Notably, we observed excellent anomeric selectivity, excellent diastereoselectivity, broad substrate scope and high efficiency for this glycosylation reaction by exploring various parameters of the cocatalytic system. DFT calculations suggested that the anomeric selectivity was mainly determined by steric interactions between the C2-carbon of the carbohydrate and the phenyl group of the metal carbenoid, while π/π interactions with the C2−OBn substituent on the carbohydrate substrate play a significant role for diastereoselectivity at the newly generated stereogenic center. © 2023 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.

Department of Chemistry

Angewandte Chemie - International Edition

Dynamic Kinetic Stereoselective Glycosylation via RhII and Chiral Phosphoric Acid-Cocatalyzed Carbenoid Insertion to the Anomeric OH Bond for the Synthesis of Glycoconjugates

The present work relates to the simultaneous determination of concentration and temperature fields from a refractive index field and is motivated by applications in evaporation. Several optical measurement techniques such as schlieren and interferometry can measure the refractive index field, which can then be converted to a density and temperature field for a single component system. The refractive index, however, is dependent on both temperature and concentration for a multi-component system involving combined heat and mass transfer. Hence, either the temperature or concentration field must be known to obtain the other. To circumvent this issue, several methods are evaluated in this study to extract concentration and temperature fields from a refractive index field. The evaluation is performed based on data from a coupled numerical solution of Navier-Stokes, energy and species conservation equations. The refractive index field can be obtained from this computed temperature and concentration field. This refractive index field is then separately used to obtain the combined temperature-concentration field using the method proposed in this work. This method is based on the premise that there is a relationship between temperature and concentration fields which can help to independently calculate both when the refractive index field is known. The temperature and concentration fields obtained using this approach are then compared with the originally computed field and the errors in them are estimated for a wide range of Lewis numbers. From the study, it is concluded that the proposed methods can be used to accurately determine the temperature and concentration fields from a given refractive index field. © 2023 IOP Publishing Ltd.

Department of Mechanical Engineering

Physica Scripta

Evaluation of temperature and concentration fields from refractive index field for evaporation-induced convective flow

Early warning signals (EWSs) represent a potentially universal tool for identifying whether a system is approaching a tipping point and have been applied in fields including ecology, epidemiology, economics and physics. This potential universality has led to the development of a suite of computational approaches aimed at improving the reliability of these methods. Classic methods based on univariate data have a long history of use, but recent theoretical advances have expanded EWSs to multivariate datasets, particularly relevant given advancements in remote sensing. More recently, novel machine learning approaches have been developed but have not been made accessible in the R (www.r-project.org) environment. Here, we present EWSmethods – an R package that provides a unified syntax and interpretation of the most popular and cutting edge EWSs methods applicable to both univariate and multivariate time series. EWSmethods provides two primary functions for univariate and multivariate systems respectively, with two forms of calculation available for each: classical rolling window time series analysis and the more robust expanding window. It also provides an interface to the Python machine learning model EWSNet which predicts the probability of a sudden tipping point or a smooth transition, the first of its form available to R users. This note details the rationale for this open-source package and delivers an introduction to its functionality for assessing resilience. We have also provided vignettes and an external website to act as further tutorials and FAQs. © 2023 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos.

Department of Data Science

Ecography

EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures and machine learning models

Recent advances in deep Convolutional Neural Networks (CNNs) have established them as a premier technique for a wide range of classification tasks, including object recognition, object detection, image segmentation, face recognition and medical image analysis. However, a significant drawback of utilizing CNNs is the requirement for a large amount of annotated data, which may not be feasible in the context of historical document analysis. In light of this, we present a novel CNN-based architecture ResPho(SC)Net, to recognize handwritten word images in a zero-shot learning framework. Our method proposes a modified version of the Phosc(Net) architecture with a much lesser number of trainable parameters. Experiments were conducted on word images from two languages (Norwegian and English) and encouraging results were obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word Image Recognition

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in the field of XAI, focusing on the tradeoff between model accuracy and interpretability. Motivated by the need to address this tradeoff, we conduct an extensive review of the literature, presenting a multi-view taxonomy that offers a new perspective on XAI methodologies. We analyze various sub-categories of XAI methods, considering their strengths, weaknesses and practical challenges. Moreover, we explore causal relationships in model explanations and discuss approaches dedicated to explaining cross-domain classifiers. The latter is particularly important in scenarios where training and test data are sampled from different distributions. Drawing insights from our analysis, we propose future research directions, including exploring explainable allied learning paradigms, developing evaluation metrics for both traditionally trained and allied learning-based classifiers and applying neural architectural search techniques to minimize the accuracy–interpretability tradeoff. This survey paper provides a comprehensive overview of the state-of-the-art in XAI, serving as a valuable resource for researchers and practitioners interested in understanding and advancing the field. © 2023 by the authors.

AI (Switzerland)

Explainable Image Classification: The Journey So Far and the Road Ahead

ACS APPLIED NANO MATERIALS

Wearable Electrochemical Microneedle Sensors Based on the Graphene-Silver-Chitosan Nanocomposite for Real-Time Continuous Monitoring of the Depression Biomarker Serotonin

Numerical simulations of fluid-structure interaction problems is an arduous task, specifically for scenarios involving complex moving bodies. Immersed boundary (IB) method which has emerged in recent years as an efficient option for dealing with such hydrodynamic problems, offers a non-body conformal strategy for the smooth exchange of information across interface in moving boundary problems. Amongst several variants of IB with the lattice-Boltzmann (LB) method, the partially saturated cells (PSC) approach has proven to be a promising one, as it offers a unified evolution equation for all the media present in the computational domain. The PSC governing equation features a volume-of-body-based solid-collision operator, which accounts for the accurate implementation of the boundary conditions and dictates a smooth transition of flow variables between the solid and fluid cells. The application of PSC methodology, however, is mostly restricted to hydrodynamic problems till date. In the present study, we propose a novel solid collision operator to simulate incompressible thermal flows with moving bodies. We demonstrate that the PSC solver is as competitive as its existing counterpart and exhibits at least first-order accuracy, while also being Galilean-invariant and capable of accurately implementing the Dirichlet boundary condition. The present investigations further showcases the efficacy and robustness of the new collision operator and PSC approach using a wide spectrum of test cases, encompassing free, forced and mixed convection problems, as well as both stationary and moving boundaries. The proposed algorithm therefore offers a promising and robust computational framework for complex fluid-particle interaction problems with heat transfer. © 2023 Begell House Inc.. All rights reserved.

International Heat Transfer Conference

DEVELOPMENT AND VALIDATION OF A NOVEL SOLID COLLISION OPERATOR IN IB-LB FRAMEWORK FOR SIMULATION OF THERMAL FLOWS

Physics of Fluids

Comment on “Continuum perturbation field in quiescent ambience: Common foundation of flows and acoustics” [Phys. Fluids 35, 056111 (2023)]

Fatal diseases like cancers, neurodegenerative, cardiac, pulmonary and kidney diseases can be treated effectively if they are diagnosed at early stages. The diagnosis of these diseases can be carried out effectively using biomarkers that are released into the blood stream, sweat and saliva due to various physiological processes. If these biomarkers can be detected when their concentrations in the body start to exhibit abnormal trends, processes to mitigate these abnormalities can be adopted. Additionally, the effect of treatment methodologies and the resultant prognosis of the disease can be analyzed if the levels of biomarkers can be correlated to the treatment methodology adopted. For carrying out the early-stage diagnosis and prognosis, sensors incorporating sensitive detection elements are required. Nb MXenes are a subclass of 2D transition metal carbides that have been receiving attention in this aspect. These MXenes possess superior properties like excellent electronic conductivity, electrochemical and photothermal stability and biocompatibility that make them valuable as sensing elements in a wide variety of sensor paradigms like electrochemical, optical, electrical and electronic sensors. This review discusses the synthesis of Nb MXenes, advantages of using Nb MXenes over other MXenes and reviews the use of these materials as detection elements in sensors that were used to detect biomarkers holding diagnostic and prognostic significance. The review concludes with future perspectives in this area, which discusses avenues for development of Nb MXene based diagnostic and prognostics. © 2023