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.

&nbsp;

IIT Palakkad

In the present scenario, metal cutting industries are looking for alternative cooling techniques to conventional cooling to satisfy the stringent environment regulations as well as lower productivity problems while machining of difficult to cut materials. Cryogenic machining is a novel eco-friendly as well as efficient cooling techniques. In present work, an attempt has been made to study the effect of process parameters on turning performance characteristics and the development of correlation models between the input process parameters and output responses while machining of difficult to cut materials 17-4 precipitated hardened stainless steel (PH SS) using response surface methodology (RSM) under the cryogenic cooling environment. The turning process parameters considered in the present study are cutting velocity (v), feed rate (f) and depth of cut (d) whereas responses are tool flank wear (Vb), surface roughness (Ra) and material removal rate (MRR) respectively. RSM based face centered central composite design (CCD) experimental design has been used to perform the experiments. From the conformation test results, it was observed that very good agreement was found between the actual and predicted values, which represent that the developed predictive models are well effective with a maximum of ± 5% error. © 2018, Springer Science+Business Media B.V., part of Springer Nature.

World University root node

Department of Mechanical Engineering

Silicon

Analysis and Modeling of Cryogenic Turning Operation Using Response Surface Methodology

Treatment of a dirhodium analogue of pentaborane(9), [(Cp*Rh)2B3H7] (nido-1; Cp* = η5-C5Me5), with [Fe2(CO)9] at room temperature led to the formation of [(Cp*Rh)2Fe(CO)3(µ-CO)B3H2Cl] (2) and the metal-rich metallaborane [(Cp*Rh)2Fe(CO)3Fe(CO)2(µ-CO)2B2H2] (3). When the same reaction was carried out at moderate temperature, two metal-rich metallaboranes, [(Cp*Rh)3Fe(CO)2(µ3-CO)2B2HX] 4 (X = H) and 5 (X = Cl) and a heterometallic µ9-boride cluster, [(Cp*Rh)3(RhCO)3Fe(CO)3(µ-CO)3B3H2] (6), were obtained. Compounds 4 and 5 can be viewed as cubane clusters with 62 cluster valence electrons (cves) and five metal–metal bonds. In another reaction, the treatment of nido-1 with [Mn2(CO)10] yielded the heterometallic µ9-boride cluster [(Cp*Rh)3Rh(CO)2{Mn(CO)3}2B4H3] (7). The cluster cores of both 6 and 7 are comprised of tricapped trigonal prisms containing a µ9-B atom bonded to seven/six metals and two/three boron atoms, respectively. All the new compounds have been characterized by mass spectrometry, IR, 1H, 11B{1H} and 13C{1H} NMR spectroscopy and X-ray crystallographic analysis. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Department of Chemistry

European Journal of Inorganic Chemistry

Metal‐Rich Metallaboranes: Structures and Geometries of Heterometallic µ9‐Boride Clusters

Learning from different modalities is a challenging task. In this paper, we look at the challenging problem of cross modal face verification and recognition between caricature and visual image modalities. Caricature have exaggerations of facial features of a person. Due to the significant variations in the caricatures, building vision models for recognizing and verifying data from this modality is an extremely challenging task. Visual images with significantly lesser amount of distortions can act as a bridge for the analysis of caricature modality. We introduce a publicly available large Caricature-VIsual dataset [CaVI] with images from both the modalities that captures the rich variations in the caricature of an identity. This paper presents the first cross modal architecture that handles extreme distortions of caricatures using a deep learning network that learns similar representations across the modalities. We use two convolutional networks along with transformations that are subjected to orthogonality constraints to capture the shared and modality specific representations. In contrast to prior research, our approach neither depends on manually extracted facial landmarks for learning the representations, nor on the identities of the person for performing verification. The learned shared representation achieves 91% accuracy for verifying unseen images and 75% accuracy on unseen identities. Further, recognizing the identity in the image by knowledge transfer using a combination of shared and modality specific representations, resulted in an unprecedented performance of 85% rank-1 accuracy for caricatures and 95% rank-1 accuracy for visual images.

Department of Data Science

MM 2018 - Proceedings of the 2018 ACM Multimedia Conference

Deep cross modal learning for caricature verification and identification(CaViNet)

A versatile and ultra-sensitive sensor platform based on lamellar Ti3C2Tx (MXene)-modified glassy carbon electrode has been developed for the detection of bromate (BrO3−) in water. MXene showed unique electrocatalytic properties towards effective BrO3− reduction and the developed sensor displayed a linear response for the BrO3− concentration from 50 nM to 5 $\mu$M with a detection limit of 41 nM. The catalytic reduction of BrO3− by MXene was assumed by an increase of the peak current and a shift to lower potential. Surface changes to MXene after electrocatalytic reduction of BrO3− was investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The formation of trace TiO2 crystals at the surface of Ti3C2Tx during the electrochemical reduction of BrO3− have confirmed the redox reaction between BrO3− and MXene. Also the developed sensor showed excellent selectivity for BrO3− among other interfering ions. This work provides an evidence for the application of MXene as an effective platform for detection of water contaminants especially when its redox functionality can be utilized.

Biological Sciences and Engineering

Sensors and Actuators, B: Chemical

Ultra-sensitive electrocatalytic detection of bromate in drinking water based on Nafion/Ti3C2Tx (MXene) modified glassy carbon electrode

We developed a highly sensitive and selective sensor based on the nanoprobe conjugates of catalytic nanoparticles and double-stranded DNA (dsDNA) for the colorimetric detection of NF-$\kappa$B protein. The sensing mechanism takes advantage of the catalytic activity of nanoparticle surfaces and the specific binding of NF-$\kappa$B to a dsDNA sequence. In the presence of NF-$\kappa$B, the highly selective interactions between dsDNA and NF-$\kappa$B lead to the passivation of the catalytic nanoparticle surfaces, impeding the sodium borohydride-mediated reduction rate of 4-nitrophenol. The correlation between the NF-$\kappa$B concentration and the visualized reduction rate of 4-nitrophenol from yellow to colorless clearly demonstrates the highly quantitative nature of the sensor. Importantly, this sensor can conclusively detect concentrations as low as 6.39 pM of NF-$\kappa$B, which to best of our knowledge is the lowest limit of detection for a colorimetric NF-$\kappa$B detection system. The excellent sensitivity of this sensor relies on the high binding constant of NF-$\kappa$B to dsDNA and the catalytic activity of nanoparticle surfaces for the signal amplification. This sensor allows visual detection without the need for any spectrometric instrumentation. We also determined the various parameters such as the pH, temperature, incubation time and salt concentration for optimal NF-$\kappa$B-dsDNA interactions. Finally, we demonstrated the performance of the sensor with simulated sample analysis. [Figure not available: see fulltext.].

Analytical and Bioanalytical Chemistry

Ultrasensitive colorimetric detection of NF-$\kappa$B protein at picomolar levels using target-induced passivation of nanoparticles

Protein S-palmitoylation refers to a post-translational modification (PTM) wherein palmitic acid, a 16-carbon long saturated fatty acid gets covalently attached to Cys sidechain of a protein. It has been known to the literature for almost 50 years and in general, this PTM is believed to facilitate membrane attachments of proteins for the obvious hydrophobicity of the palmitoyl group. But after the discovery of the protein palmitoyl acyltransferases (PATs, also known as DHHC-PATs), a major paradigm shift has been observed in the field of protein S-palmitoylation. A family of 23 mammalian DHHC-PATs has been identified and the majority of them are associated with many human diseases spanning from neuropsychiatric diseases to cancers. Novel unique and essential role of DHHC-mediated protein S-palmitoylation has been revealed apart from its membrane trafficking role. Biomedical importance of DHHCs has also been reiterated with small molecule inhibitors for DHHCs as well as in DHHC-knockout mice or mouse Xenograft models. In this review, we present recent advances in the field of protein S-palmitoylation and the involvement of individual DHHC isoforms in human diseases. In addition, the recent development of the analytical tools to study S-palmitoylation and their inhibitors are discussed in detail. We also highlight the issues that need to be addressed in detail to further develop our understanding on protein S-palmitoylation and strongly believe that pharmacological modulation of DHHC-mediated protein S-palmitoylation has a massive potential to emerge as a novel therapeutic strategy for human diseases. It will not be surprising if reversible protein S-palmitoylation prove to be an indispensable PTM that regulates a host of cellular processes, just like protein phosphorylation or ubiquitination. © 2018 Elsevier GmbH

Fulltext

European Journal of Cell Biology

Emerging Roles of DHHC-mediated Protein S-palmitoylation in Physiological and Pathophysiological Context

Certain power system situations force protection schemes to mal-operate. Such mal-operations associated with backup protection (BP) schemes might worsen the situation by initiating the cascading effect, damaging critical elements such as generation units, transformers etc. of the power system, or leading to complete collapse of the power system. To adapt to such a network structure, the relays which are going to participate in BP of transmission line have to evolve. Integration of protection systems, communication network, and computer technology has to be the approach for faster information sharing and decision making. Analysis of recent blackouts clearly shows the importance of advancement of wide-area measurement systems and adaptive nature of relaying. Synchro-phasors assisted BP scheme is proposed in this work, which helps the relays adapt according to the prevailing system situations and decisive action is taken considering security and dependability aspects of the relays. © The Institution of Engineering and Technology 2018.

Department of Electrical Engineering

IET Generation, Transmission and Distribution

Synchro-phasors assisted back-up protection of transmission line

The effect of inverter dead time on the inverter output voltage is well studied in the literature. This paper studies the effect of the dead time on the inverter input current. An expression for the switching-cycle-averaged inverter input current including the dead time effect is derived, which shows the dead time effect to primarily result in a reduction in the average value of the inverter dc input current. The effect of dead time on the input current is incorporated into the dc-link dynamic model of a diode-bridge-rectifier (DBR) fed voltage source inverter (VSI). A switching-cycle-average model of a DBR-VSI-fed induction motor is proposed, which considers the effect of dead time on both the ac output voltage and dc input current of the VSI. The proposed average model is used to study the light-load instability phenomenon in a 100-kW open-loop motor drive. The proposed model is shown to predict the region of oscillatory behavior (i.e., region exhibiting light-load instability) with better accuracy than existing average models. The proposed model also accurately predicts the dc-link voltage swing at such oscillatory operating points. While being comparable in accuracy in predicting the system dynamics, the proposed model is much more computationally efficient than the state-of-the-art switching model. The validity of the proposed average model is also confirmed by experimental measurements on a 100-kW induction motor drive. © 2017 IEEE.

IEEE Transactions on Industry Applications

Impact of Dead Time on Inverter Input Current, DC-Link Dynamics, and Light-Load Instability in Rectifier-Inverter-Fed Induction Motor Drives

An event triggered fault detection problem for linear systems is addressed using generalized likelihood ratio test. Using measurements obtained from any general event sampling mechanism and assuming known fault magnitude, fault occurrence time is estimated. While estimating the fault occurrence time, state estimates are required in computing likelihood functions. For measurements satisfying the event triggering mechanism, states are estimated with the obtained measurements, while for the other measurements, event triggering information is used to obtain the state estimates. The effectiveness of the proposed approach is demonstrated using an illustrative example. © 2018

IFAC-PapersOnLine

Event triggered fault detection in linear systems using Generalized likelihood ratio test

This paper introduces a novel family of constrained adaptive filtering algorithms for sensor array beamforming. These algorithms, namely, constrained least mean logarithmic square (CLMLS), ℓ1-norm CLMLS (ℓ1-CLMLS) and its weighted version ℓ1-WCLMLS, are developed based on a relative logarithmic cost function. The proposed algorithms gracefully adjust cost function depending on the amount of the error thereby achieving better performance compared to constrained least mean square (CLMS) family of algorithms. The transient and steady-state performance analysis of the proposed CLMLS algorithm is presented and these analytical results are validated through extensive simulations. Proposed CLMLS algorithm is then extended to sparse system identification problem by incorporating the ℓ1-norm penalty into CLMLS cost function. We show that the resultant ℓ1-CLMLS and ℓ1-WCLMLS algorithms outperform their CLMS counterparts in sparse system identification. When applied to sparse sensor array synthesis, these algorithms achieve desired beampattern with lesser number of sensor elements compared to state-of-the-art algorithms. © 2001-2012 IEEE.