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

In this paper, the dynamical behaviors of a delayed predator-prey model (PPM) with nonlinear harvesting effortsby using imprecise biological parameters are studied. A method is proposed to handle these imprecise parameters by using a parametric form of interval numbers. The proposed PP M is presented with Crowley-Martin type of predation and Michaelis-Menten type prey harvesting. The existence of various equilibrium points and the stability of the system at these equilibrium points are investigated. Analytical study reveals that the delaymodel exhibits a stable limit cycle oscillation. Computer simulations are carried out to illustrate the main analytical findings.

Department of Physics

ZEITSCHRIFT FUR NATURFORSCHUNG SECTION A-A JOURNAL OF PHYSICAL SCIENCES

Stability analysis of a delayed predator-prey model with nonlinear harvesting efforts using imprecise biological parameters

Reactions of two magnesium(I) compounds, [{(ArNacnac)Mg}2] (ArNacnac=[HC(MeCNAr)2]−; Ar=mesityl (Mes) or o-xylyl (Xyl)), with CO in the presence of [Mo(CO)6] lead to the reductive hexamerization of CO and formation of magnesium benzenehexolate complexes, [{(ArNacnac)Mg}6(C6O6)]. [Mo(CO)6] is not consumed in these reactions, but is apparently required to initiate (or catalyze) the CO hexamerizations. A range of studies were used to probe the mechanism of formation of the benzenehexolate complexes. The magnesium(I) reductive hexamerizations of CO are closely related to Liebig's reduction of CO with molten potassium (to give K6C6O6, amongst other products), originally reported in 1834. As the mechanism of that reaction is still unknown, it seems reasonable that magnesium(I) reductions of CO could prove useful homogeneous models for its elucidation and for the study of other C−C bond forming reactions that use CO as a C1 feedstock (e.g. the Fischer–Tropsch process). © 2020 Wiley-VCH GmbH

World University root node

Department of Chemistry

Angewandte Chemie - International Edition

Reductive Hexamerization of CO Involving Cooperativity Between Magnesium(I) Reductants and [Mo(CO)6]: Synthesis of Well-Defined Magnesium Benzenehexolate Complexes**

Oil spill is also a form of pollution that creates adverse effects on the environment. This project emphasis on design and fabrication of the Semi-Autonomous oil spills removing robot. By navigating on the surface of water, the robot proposes a new system for skimming of oil and removing oil spills from the ocean sur-face. The main aim of the project is to reduce the man power, time consumption and improve the quality of oil cleaning in the ocean. In this project, automation of the oil spill cleaning process is achieved with the help of a magnetic conveyor and magnetite powder. The flexible magnetic conveyor containing magnetite powder in the belt softly rolls over the ocean's surface, and absorbs the oil along with the magnetite powder and deflecting the ocean water with its hydrophobic properties. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Confer-ence on Advances in Materials Research - 2019.

MATERIALS TODAY-PROCEEDINGS

Fabrication and performance evaluation of semi-autonomous oil spills removing robot

The design and development of biosensors, analytical devices used to detect various analytes in different matrices, has emerged. Biosensors indicate a biorecognition element with a physicochemical analyzer or detector, i.e., a transducer. In the present scenario, various types of biosensors have been deployed in healthcare and clinical research, for instance, biosensors for blood glucose monitoring. Pathogenic microbes are contributing mediators of numerous infectious diseases that are becoming extremely serious worldwide. The recent outbreak of COVID-19 is one of the most recent examples of such communal and deadly diseases. In efforts to work towards the efficacious treatment of pathogenic viral contagions, a fast and precise detection method is of the utmost importance in biomedical and healthcare sectors for early diagnostics and timely countermeasures. Among various available sensor systems, optical biosensors offer easy-to-use, fast, portable, handy, multiplexed, direct, real-time, and inexpensive diagnosis with the added advantages of specificity and sensitivity. Many progressive concepts and extremely multidisciplinary approaches, including microelectronics, microelectromechanical systems (MEMSs), nanotechnologies, molecular biology, and biotechnology with chemistry, are used to operate optical biosensors. A portable and handheld optical biosensing device would provide fast and reliable results for the identification and quantitation of pathogenic virus particles in each sample. In the modern day, the integration of intelligent nanomaterials in the developed devices provides much more sensitive and highly advanced sensors that may produce the results in no time and eventually help clinicians and doctors enormously. This review accentuates the existing challenges engaged in converting laboratory research to real-world device applications and optical diagnostics methods for virus infections. The review’s background and progress are expected to be insightful to the researchers in the sensor field and facilitate the design and fabrication of optical sensors for life-threatening viruses with broader applicability to any desired pathogens. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Fulltext

Diagnostics

Optical biosensors for diagnostics of infectious viral disease: A recent update

Rising global concerns posed by chemical and biological threat agents highlight the critical need to develop reliable strategies for the real-time detection of such threats. While wearable sensing technology is well suited to fulfill this task, the use of on-body devices for rapid and selective field identification of chemical agents is relatively a new area. This work describes a flexible printed textile-based solid-contact potentiometric sensor for the selective detection of fluoride anions liberated by the biocatalytic hydrolysis of fluorine-containing G-type nerve agents (such as sarin or soman). The newly developed solid-contact textile fluoride sensor relies on a fluoride-selective bis(fluorodioctylstannyl)methane ionophore to provide attractive analytical performance with near-Nernstian sensitivity and effective discrimination against common anions, along with excellent reversibility and repeatability for dynamically changing fluoride concentrations. By using stress-enduring printed inks and serpentine structures along with stretchable textile substrates, the resulting textile-based fluoride sensor exhibits robust mechanical resiliency under severe mechanical strains. Such realization of an effective textile-based fluoride-selective electrode allowed biosensing of the nerve-agent simulant diisopropyl fluorophosphate (DFP), in connection to immobilized organophosphorus acid anhydrolylase (OPAA) or organophosphorus hydrolase (OPH) enzymes. A user-friendly portable electronic module transmits data from the new textile-based potentiometric biosensor wirelessly to a nearby smartphone for alerting the wearer instantaneously about potential chemical threats. While expanding the scope of wearable solid-contact anion sensors, such a textile-based potentiometric fluoride electrode transducer offers particular promise for effective discrimination of G-type neurotoxins from organophosphate (OP) pesticides, toward specific field detection of these agents in diverse defense settings. © 2021

Biosensors and Bioelectronics

Textile-based wearable solid-contact flexible fluoride sensor: Toward biodetection of G-type nerve agents

1. Sudden transitions from one stable state to a contrasting state occur in complex systems ranging from the collapse of ecological populations to climatic change, with much recent work seeking to develop methods to predict these unexpected transitions from signals in time series data. However, previously developed methods vary widely in their reliability and fail to classify whether an approaching collapse might be catastrophic (and hard to reverse) or non-catastrophic (easier to reverse) with significant implications for how such systems are managed.2. Here we develop a novel detection method, using simulated outcomes from a range of simple mathematical models with varying nonlinearity to train a deep neural network to detect critical transitions - the Early Warning Signal Network (EWSNet).3. We demonstrate that this neural network (EWSNet), trained on simulated data with minimal assumptions about the underlying structure of the system, can predict with high reliability observed real-world transitions in ecological and climatological data. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching but critically whether the collapse will be catastrophic or non-catastrophic.4. The EWSNet can flag a critical transition with unprecedented accuracy, overcoming some of the major limitations of traditional methods based on phenomena such as Critical Slowing Down. These novel properties mean EWSNet has the potential to serve as a universal indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and have much broader management implications.Competing Interest StatementThe authors have declared no competing interest.

Department of Data Science

bioRxiv

Machine learning methods trained on simple models can predict critical transitions in complex natural systems

A parametric computational study was conducted on UWS (urea-water-solution) droplets evaporating at conditions typical of an automobile SCR (selective catalytic reduction) system. The computational model used in the present work has been validated earlier using experimental data and was found capable of predicting the evaporation behaviour of UWS droplets. The ambient temperatures considered in the present study ranged from 423 K to 873 K and the free-stream gas velocities used were between 1 m/s and 100 m/s. The droplets were injected perpendicular to the free-stream and the droplet diameter, as well as its position, was tracked with time. The initial diameters of the droplets investigated ranged between 10 and 200 microns. A rapid mixing (RM) model was employed for the liquid phase in order to reduce the computational costs and the droplet evaporation model was based on the vaporization model proposed by Abramzon and Sirignano. The effect of the ambient gas, on the vaporization rate of the droplet, was also investigated in the present work. The governing equations, used in the computational model, were solved numerically using MATLAB and the results on the vaporization behaviour of the UWS droplets are presented. © 2021 ICLASS 2021 - 15th Triennial International Conference on Liquid Atomization and Spray Systems. All Rights Reserved.

Department of Mechanical Engineering

International Conference on Liquid Atomization and Spray Systems (ICLASS)

A Parametric Study of the Evaporation Behaviour of UWS Droplets

Copper oxide (CuO) is used as a reusable solid reagent for hydrogenation of nitroarenes to aminoarenes. The use of CuO resulted in 100 % conversion of 2.9 mmol of nitrobenzene to aniline in 45 s at room temperature using hydrazine hydrate as the reducing agent. During the reaction, CuO is converted to inactive metallic Cu which can be regenerated to active CuO by thermal oxidation. DFT simulations indicated facile formation of oxygen vacancies (EO,vac = −3.8 kJ/mol) on the surface of CuO (111) in reducing environment which is consistent with the XPS analysis. Oxygen vacancies facilitate stronger nitrobenzene binding (−148.5 kJ/mol) and reduced activation barrier (Ea = 36.4 kJ/mol) for N[sbnd]O dissociation. Motivated from this mechanistic insight -NO2 groups in various nitroarenes were selectively hydrogenated to [sbnd]NH2 groups using CuO. © 2021 Elsevier B.V.

Applied Catalysis B: Environmental

CuO as a reactive and reusable reagent for the hydrogenation of nitroarenes

The accurate emotional assessment of humans can prove beneficial in health care, security investigations and human interaction. In contrast to emotion recognition from facial expressions which can prove to be inaccurate, analysis of electroencephalogram (EEG) activity is a more accurate representation of one’s state of mind. With advancements in deep learning, various methods are being employed for this task. In this research, importance of attention mechanism in EEG signals is introduced through two vision transformer based methods for the classification of EEG signals on the basis of emotions. The first method utilizes 2-D images generated through continuous wavelet transform (CWT) of the raw EEG signals and the second method directly operates on the raw signal. The publicly available and widely accepted DEAP dataset has been utilized in this research for validating the proposed approaches. The proposed approaches report very high accuracies of 97% and 95.75% using CWT and 99.4% and 99.1% using raw signal for valence and arousal classifications respectively, which clearly highlights the significance of attention mechanism for EEG signals. The proposed methodology also ensures faster training and testing time which suits the clinical purposes.Clinical Relevance— This work establishes a highly accurate algorithm for emotion recognition using EEG signals which has potential applications in music-based therapy.

Department of Electrical Engineering

2021 43rd Annual International Conference of the IEEE Engineering in {\ldots}

Introducing Attention Mechanism for EEG Signals : Emotion Recognition with Vision Transformers

Ultrasound (US) imaging is becoming the routine modality for the diagnosis and prognosis of lung pathologies. Lung US imaging relies on artifacts from acoustic impedance (Z) mismatches to distinguish and interpret the normal and pathological lung conditions. The air-pleura interface of the normal lung displays specularity due to the huge Z mismatches. However, in the presence of pathologies, the interface alters exhibiting a diffuse behavior due to increased density and reduced spatial distribution of air in the sub-pleural space. The specular or the diffuse behavior influences the reflected acoustic intensity distribution. This study aims to understand the reflection pattern in a normal and pathological lung through a novel approach of determining pixel-level acoustic intensity vector field (IVF) at high frame rates. Detailed lung modeling procedures using k-Wave US toolbox under normal, edematous, and consolidated conditions are illustrated. The analysis of the IVF maps on the three lung models clearly shows the drifting of the air-pleura interface from specular to diffuse with the severity of the pathology.Clinical Relevance— The presented acoustic simulation lung models are an aid to teaching and research by providing a quick visual and intuitive understanding of lung ultrasound physics. The proposed intensity vector field maps are supplementary information to aid diagnostics and characterization of any tissue composed of specular and diffuse components.