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

World University root node

Department of Mechanical Engineering

4th World Congress on Micro Nano Manufacturing (WCMNM)

Surface micro-layer modification of selectively laser melted TiAl alloy using wire electrical discharge polishing

Electrocatalyzed oxidative B−H nitrogenations of nido-carborane (nido-7,8-C2B9H12−) with N-heterocycles have been established, enabling the preparation of various N-substituted nido-carboranes without chemical oxidants or metal catalyst under ambient conditions. The electrolysis manifold occurred with high levels of efficiency as well as chemo- and position- selectivity, employing sustainable electricity as the sole oxidant. The strategy set the stage for a user-friendly access to novel amino acid and fluorogenic boron-dipyrrin (BODIPY)-labeled nido-carborane hybrids. © 2020 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH

Department of Chemistry

Fulltext

Angewandte Chemie - International Edition

Electrochemical B−H Nitrogenation: Access to Amino Acid and BODIPY-Labeled nido-Carboranes

Indole dearomatization is an important strategy to access indolines: a motif present in a variety of natural products and biologically active molecules. Herein, a method for transition-metal catalyzed regioselective dearomative arylboration of indoles to generate diverse indolines is presented. The method accomplishes intermolecular dearomatization of simple indoles through a migratory insertion pathway on substrates that lack activating or directing groups on the C2- or C3-positions. Synthetically useful C2- and C3-borylated indolines can be accessed through a simple change in N-protecting group in high regio- and diastereoselectivities (up to >40:1 rr and >40:1 dr) from readily available starting materials. Additionally, the origin of regioselectivity was explored experimentally and computationally to uncover the remarkable interplay between carbonyl orientation of the N-protecting group on indole, electronics of the C2-C3 π-bond, and sterics. The method enabled the first enantioselective synthesis of (-)-azamedicarpin. ©

Journal of the American Chemical Society

Nickel-Catalyzed Dearomative Arylboration of Indoles: Regioselective Synthesis of C2- And C3-Borylated Indolines

Department of Mathematics

arXiv preprint arXiv:2103.

Structure theorems for Power Series in Several Complex Variables

The development of suitable chemical sensors for the selective detection and quantification of antibiotics has aroused tremendous research interests in recent years because of their serious menace to the environment. The excessive accumulation, through high human consumption and animal activities, of antibiotics in the soil can trigger pernicious effects on the living systems and it necessitates the development of suitable sensors. Among the various detection methods that are currently available, the fluorescent technique has become an alternative and effective method for the identification of antibiotics. In recent years, a variety of fluorescent sensors have been designed concerning the discriminative sensing of antibiotics both in solution and vapor-phase. In this review article, we attempted to systematically summarize a report on the recent advancements accomplished in the use of luminescent metal–organic frameworks (LMOFs) as fluorescent sensors for the discriminative sensing of different types of antibiotics. The LMOFs based fluorescent sensors described in this article are grouped according to their sensing propensity towards the particular type of antibiotics. Finally, the article provides a prospect on the advantages and existing challenges in antibiotics detection using LMOFs based fluorescent sensors and the future direction of this field has also been stated. © 2021 Elsevier B.V.

Coordination Chemistry Reviews

Recent advances in luminescent metal–organic frameworks (LMOFs) based fluorescent sensors for antibiotics

SIRT5 is a member of the sirtuin family of NAD+-dependent protein lysine deacylases implicated in a variety of physiological processes. SIRT5 removes negatively charged malonyl, succinyl, and glutaryl groups from lysine residues and thereby regulates multiple enzymes involved in cellular metabolism and other biological processes. SIRT5 is overexpressed in human breast cancers and other malignancies, but little is known about the therapeutic potential of SIRT5 inhibition for treating cancer. Here we report that genetic SIRT5 disruption in breast cancer cell lines and mouse models caused increased succinylation of IDH2 and other metabolic enzymes, increased oxidative stress, and impaired transformation and tumorigenesis. We, therefore, developed potent, selective, and cell-permeable small-molecule SIRT5 inhibitors. SIRT5 inhibition suppressed the transformed properties of cultured breast cancer cells and significantly reduced mammary tumor growth in vivo, in both genetically engineered and xenotransplant mouse models. Considering that Sirt5 knockout mice are generally normal, with only mild phenotypes observed, these data establish SIRT5 as a promising target for treating breast cancer. The new SIRT5 inhibitors provide useful probes for future investigations of SIRT5 and an avenue for targeting SIRT5 as a therapeutic strategy. © 2021, The Author(s), under exclusive licence to Springer Nature Limited part of Springer Nature.

Oncogene

Pharmacological and genetic perturbation establish SIRT5 as a promising target in breast cancer

This work discusses the development of a sharp interface immersed boundary (IB) method for viscous compressible flows and its assessment for accurate computations of wall shear and heat fluxes in hypersonic flows. The IB method is implemented in an unstructured Cartesian finite-volume (FV) framework and resolves the geometric interface sharply on the nonconformal mesh through direct imposition of boundary conditions employing a local reconstruction approach. The efficacy of the IB-FV solver is investigated for canonical high-speed viscous flows over a range of Mach numbers. The numerical results indicate that the surface pressure and shear stress distributions are computed with reasonable accuracy, whereas surface heat fluxes for aerodynamically blunt configurations are underpredicted. Employing a set of carefully designed experiments and simple diagnostic tools, we probe the possible causes for the underprediction in heat flux. We show that there exist two sources of error - one due to grid resolution and the other due to solution reconstruction, with the latter being more prominent and responsible for the observed underprediction in heat fluxes. Studies reveal that the heat flux estimates are sensitive to the choice of temperature reconstruction and linear interpolations could lead to poor estimates of heat flux. Our investigations conclusively point out the fact that existing polynomial-based reconstruction approaches for sharp interface IB techniques are not necessarily adequate for heat transfer predictions in high Reynolds number hypersonic flows. © 2021 American Physical Society.

Physical Review E

Role of solution reconstruction in hypersonic viscous computations using a sharp interface immersed boundary method

Deep convolutional networks have been quite successful at various image classification tasks. The current methods to explain the predictions of a pre-trained model rely on gradient information, often resulting in saliency maps that focus on the foreground object as a whole. However, humans typically reason by dissecting an image and pointing out the presence of smaller concepts. The final output is often an aggregation of the presence or absence of these smaller concepts. In this work, we propose MACE: a Model Agnostic Concept Extractor, which can explain the working of a convolutional network through smaller concepts. The MACE framework dissects the feature maps generated by a convolution network for an image to extract concept based prototypical explanations. Further, it estimates the relevance of the extracted concepts to the pre-trained model's predictions, a critical aspect required for explaining the individual class predictions, missing in existing approaches. We validate our framework using VGG16 and ResNet50 CNN architectures and on datasets like Animals With Attributes 2 (AWA2) and Places365. Our experiments demonstrate that the concepts extracted by the MACE framework increase the human interpretability of the explanations and are faithful to the underlying pre-trained black-box model.

Department of Data Science

IEEE Transactions on Artificial Intelligence

MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks

A class domination coloring (also called cd-Coloring or dominated coloring) of a graph is a proper coloring in which every color class is contained in the neighborhood of some vertex. The minimum number of colors required for any cd-coloring of G, denoted by χcd(G), is called the class domination chromatic number (cd-chromatic number) of G. In this work, we consider two problems associated with the cd-coloring of a graph in the context of exact exponential-time algorithms and parameterized complexity. (1) Given a graph G on n vertices, find its cd-chromatic number. (2) Given a graph G and integers k and q, can we delete at most k vertices such that the cd-chromatic number of the resulting graph is at most q? For the first problem, we give an exact algorithm with running time O(2nn4logn). Also, we show that the problem is FPT with respect to the number q of colors as the parameter on chordal graphs. On graphs of girth at least 5, we show that the problem also admits a kernel with O(q3) vertices. For the second (deletion) problem, we show NP-hardness for each q≥2. Further, on split graphs, we show that the problem is NP-hard if q is a part of the input and FPT with respect to k and q as combined parameters. As recognizing graphs with cd-chromatic number at most q is NP-hard in general for q≥4, the deletion problem is unlikely to be FPT when parameterized by the size of the deletion set on general graphs. We show fixed parameter tractability for q∈{2,3} using the known algorithms for finding a vertex cover and an odd cycle transversal as subroutines. © 2020 Elsevier B.V.

Department of Computer Science and Engineering

Discrete Applied Mathematics

Parameterized and exact algorithms for class domination coloring

In this paper, we exhibit the equivalence between different notions of unique range sets, namely, unique range sets, weighted unique range sets and weak-weighted unique range sets under certain conditions. Also, we present some uniqueness theorems which show how two mero-morphic functions are uniquely determined by their two finite shared sets. Moreover, in the last section, we make some observations that help us to construct other new classes of unique range sets.