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

This paper studies the dynamic analysis and characterization of two-stage planetary gear transmission system for wind turbine drive train under random loads. Rigid multi body dynamics with discrete flexibility is used for modeling the wind turbine drive train. Variable gear mesh stiffness, bearings and shaft stiffness are accounted in this model. The derivation of the system governing equation by Lagrange approach requires the formulation of kinetic and potential energies. The coupled torsional and bending model of 22 degrees of freedom is solved for vibration responses in time domain using Newmark time algorithm. Applying the Fast Fourier Transformation (FFT) on a time signal, vibration responses in frequency domain are obtained and further studied for wind turbine drive train characteristics. To identify the wind characteristics at low frequencies, wavelet decomposition is used. © 2018 Author(s).

World University root node

Department of Mechanical Engineering

AIP Conference Proceedings

Dynamic analysis and characterization of two stage epicyclic gear box in wind turbine drive train

Polymerising filaments generate force against an obstacle, as in, e.g., microtubule-kinetochore interactions in the eukaryotic cell. Earlier studies of this problem have not included explicit three-dimensional monomer diffusion, and consequently, missed out on two important aspects: (i) the barrier, even when it is far from the polymers, affects free diffusion of monomers and reduces their adsorption at the tips, while (ii) parallel filaments could interact through the monomer density field ("diffusive coupling"), leading to negative interference between them. In our study, both these effects are included and their consequences investigated in detail. A mathematical treatment based on a set of continuum Fokker-Planck equations for combined filament-wall dynamics suggests that the barrier-induced monomer depletion reduces the growth velocity and also the stall force, while the total force produced by many filaments remains additive. However, Brownian dynamics simulations show that the linear force-number scaling holds only when the filaments are far apart; when they are arranged close together, forming a bundle, sublinear scaling of force with number appears, which could be attributed to diffusive interaction between the growing polymer tips. © 2018 The Author(s).

Department of Physics

Fulltext

Scientific Reports

Polymerisation force of a rigid filament bundle: Diffusive interaction leads to sublinear force-number scaling

Department of Civil Engineering

Proceedings of GeoShanghai 2018 International Conference: Geoenvironment and Geohazard

Evaluation of Hydro-Mechanical Behaviour of Hydraulic Barriers of Landfill Covers

Nitridoferrates containing monovalent iron ions are a class of materials of recent interest as potentially novel magnetic materials. Aiming at the exploration of nitridoferrates of calcium, we report the single crystal growth from Ca ﬂux and crystal structure of the first member (n = 2) of a series of inverse Ruddlesden-Popper nitrides with a general formula of An −1A'2BnX3n+1, where A = Li/Fe, A' = Te, B = N, and X = Ca. Single crystal X-ray diffraction analyses indicate the crystal with a composition of Ca7(Li0.32(1)Fe0.68(1))Te2N2 and the tetragonal space group I4/mmm (a = 4.7884(1) Å, c = 25.3723(4) Å, Z = 2). The structure features alternately stacking NaCl-type A'X slabs and the perovskite-type ABX3 slabs along the c axis. The Li/Fe atoms are located in cuboctahedral cavities surrounded by eight Ca6N octahedra in the ABX3 slab. This work demonstrates the viability of the Ca-rich ﬂux as a suitable solvent for the exploration of new complex nitrides with interesting crystal structure and properties. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Philosophical Magazine Letters

An inverse Ruddlesden-Popper nitride Ca7(Li1−xFex)Te2N2 grown from Ca ﬂux

Computing localizable entanglement for noisy many-particle quantum states is difficult due to the optimization over all possible sets of local projection measurements. Therefore, it is crucial to develop lower bounds, which can provide useful information about the behaviour of localizable entanglement, and which can be determined by measuring a limited number of operators, or by performing the least number of measurements on the state, preferably without performing a full state tomography. In this paper, we adopt two different yet related approaches to obtain a witness-based, and a measurement-based lower bounds for localizable entanglement. The former is determined by the minimal amount of entanglement that can be present in a subsystem of the multipartite quantum state, which is consistent with the expectation value of an entanglement witness. Determining this bound does not require any information about the state beyond the expectation value of the witness operator, which renders this approach highly practical in experiments. The latter bound of localizable entanglement is computed by restricting the local projection measurements over the qubits outside the subsystem of interest to a suitably chosen basis. We discuss the behaviour of both lower bounds against local physical noise on the qubits, and discuss their dependence on noise strength and system size. We also analytically determine the measurement-based lower bound in the case of graph states under local uncorrelated Pauli noise. © 2018 The Author(s). Published by IOP Publishing Ltd on behalf of Deutsche Physikalische Gesellschaft.

New Journal of Physics

Estimating localizable entanglement from witnesses

Ruthenium-catalysis enabled the C-5 selective C-H oxygenation of naphthoquinones, and also sets the stage for the site-selective introduction of a hydroxyl group into anthraquinones. A-ring modified naphthoquinoidal compounds represent an important class of bioactive quinones for which the present study encompasses the first C-H oxygenation strategy by weak O-coordination. © 2018 The Royal Society of Chemistry.

Department of Chemistry

Chemical Communications

Ruthenium-catalyzed C-H oxygenation of quinones by weak O-coordination for potent trypanocidal agents

Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels. © 2018 IFIP.

Department of Computer Science and Engineering

2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2018

Analysis of QoE for adaptive video streaming over wireless networks

In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and assume the stoichiometric steady state of the metabolic network. We will discuss a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also discuss a sparse variant of PMFA that favors flux modes that contain a small number of reactions. PMFA has several benefits: (1) it can be applied to large metabolic network in efficient way as PMFA does not enumerate elementary modes, (2) the method is more robust to the steady-state violations than competing approaches, and (3) can compactly capture the variation in the data by a few factors. This chapter will describe the detailed steps how to do the above task on experimental data from fluxomic and gene expression measurements. © Springer Science+Business Media, LLC, part of Springer Nature 2018.

Department of Data Science

Methods in Molecular Biology

Analysis of fluxomic experiments with principal metabolic flux mode analysis

We present SCCA-HSIC, a method for finding sparse non-linear multivariate relations in high-dimensional settings by maximizing the Hilbert-Schmidt Independence Criterion (HSIC). We propose efficient optimization algorithms using a projected stochastic gradient and Nyström approximation of HSIC. We demonstrate the favourable performance of SCCA-HSIC over competing methods in detecting multivariate non-linear relations both in simulation studies, with varying numbers of related variables, noise variables, and samples, as well as in real datasets. © 2018 IEEE.

Proceedings - IEEE International Conference on Data Mining, ICDM

Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion

Motivation: In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples. Results: We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches. Availability and implementation: Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA. © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.