Microwave Q-factor of a dielectric resonator sandwiched between two thin films of superconductor

This analysis provides a comparison between the computed and the measured Q-factors of a microwave resonator consisting of a dielectic resonator sandwiched between two thin films of superconductor using the Bose statistics and the Gorter and Casimir model for a two-fluid model. Such comparisons are helpful not only to design ultrahigh-Q resonators but also in considering the theory of thin films of HTS (high-temperature superconductors).     

Polyurethane‐incorporated chitosan/alginate core–shell nano‐particles for controlled oral insulin delivery

Chitosan (CS) and polyurethane‐chitosan (PU‐CS) nano‐particles (NPs) were prepared for the core formation by complex coacervation method whereas alginate (ALG) and PU‐ALG were crosslinked by ionic gelation method to form the protective shell‐layer over the core. Effects of PU incorporation either within the core or shell or both were investigated by different in vitro and in vivo parameters. Fourier transform infrared (FTIR) spectroscopy of different compositions of nano‐particles showed distinct characteristic peaks for CS, PU, and ALG, indicating their presence in variable ratios. Significance of polyurethane‐incorporated systems towards insulin encapsulation efficiency, swelling parameters, insulin release, and in vivo pharmacological effect were also studied. Particle sizes, zeta potential, morphological analysis, mucoadhesion study, and in vivo acute toxicity studies of these core–shell nano‐particles were also performed. Bioavailability of insulin ranged from 9.04% to 11.6% for polyurethane‐incorporated chitosan‐alginate core–shell nano‐particle formulations which was significantly higher than the insulin bioavailability of basic CS/ALG core–shell nano‐particle system. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46365.     

First-Principles Study of Electronic Structure,Mechanical, and Thermoelectric Properties of Ternary Palladates CdPd<Subscript>3</Subscript>O<Subscript>4</Subscript> and TlPd<Subscript>3</Subscript>O<Subscript>4</Subscript>

Ternary palladates CdPd₃O₄ and TlPd₃O₄ have been studied theoretically using the generalized gradient approximation (GGA), modified Becke–Johnson, and spin–orbit coupling (GGA–SOC) exchange–correlation functionals in the density functional theory (DFT) framework. From the calculated ground-state properties, it is found that SOC effects are dominant in these palladates. Mechanical properties reveal that both compounds are ductile in nature. The electronic band structures show that CdPd₃O₄ is metallic, whereas TlPd₃O₄ is an indirect-bandgap semiconductor with energy gap of 1.1 eV. The optical properties show that TlPd₃O₄ is a good dielectric material. The dense electronic states, narrow-gap semiconductor nature, and Seebeck coefficient of TlPd₃O₄ suggest that it could be used as a good thermoelectric material. The magnetic susceptibility calculated by post-DFT treatment confirmed the paramagnetic behavior of these compounds.     

Assessing the reTweet proneness of tweets: predictive models for retweeting

The problem of assessing the mechanisms underlying the phenomenon of virality of social network posts is of great value for many activities, such as advertising and viral marketing, influencing and promoting, early monitoring and emergency response. Among the several social networks, Twitter.com is one of the most effective in propagating information in real time, and the propagation effectiveness of a post (i.e., tweet) is related to the number of times the tweet has been retweeted. Different models have been proposed in the literature to understand the retweet proneness of a tweet (tendency or inclination of a tweet to be retweeted). In this paper, a further step is presented, thus several features extracted from Twitter data have been analyzed to create predictive models, with the aim of predicting the degree of retweeting of tweets (i.e., the number of retweets a given tweet may get). The main goal is to obtain indications about the probable number of retweets a tweet may obtain from the social network. In the paper, the usage of the classification trees with recursive partitioning procedure for prediction has been proposed and the obtained results have been compared, in terms of accuracy and processing time, with respect to other methods. The Twitter data employed for the proposed study have been collected by using the Twitter Vigilance study and research platform of DISIT Lab in the last 18 months. The work has been developed in the context of smart city projects of the European Commission RESOLUTE H2020, in which the capacity of communicating information is fundamental for advertising, promoting alerts of civil protection, etc.     

Full‐complexity polytopic robust control invariant sets for uncertain linear discrete‐time systems

This paper presents an algorithm for the computation of full‐complexity polytopic robust control invariant (RCI) sets, and the corresponding linear state‐feedback control law. The proposed scheme can be applied for linear discrete‐time systems subject to additive disturbances and structured norm‐bounded or polytopic uncertainties. Output, initial condition, and performance constraints are considered. Arbitrary complexity of the invariant polytope is allowed to enable less conservative inner/outer approximations to the RCI sets whereas the RCI set is assumed to be symmetric around the origin. The nonlinearities associated with the computation of such an RCI set structure are overcome through the application of Farkas' theorem and a corollary of the elimination lemma to obtain an initial polytopic RCI set, which is guaranteed to exist under certain conditions. A Newton‐like update, which is recursively feasible, is then proposed to yield desirable large/small volume RCI sets.     

Baseline rolling resistance for tires’ on-road fuel efficiency using finite element modeling

Calculation of truck tires rolling resistance, using the finite element method and considering variables such as incompressible visco-hyperelastic rubber materials, accurate tire geometry and steady temperature distribution, is presented. The model was validated using experimentally measured contact area and contact stresses. Rolling resistance was calculated for three values of axle load, tire inflation pressure, temperature and speed. In addition, regression analysis was used to propose a mathematical expression for predicting rolling resistance as a function of the considered variables. Finally, the contribution of tire’s rubber components to the internal energy was quantified, and it was found that sidewall and subtread were the most relevant. The results of this study will help differentiate the contribution of pavement parameters, such as mean profile depth and international roughness index, to fuel efficiency.     

A nonparametric probabilistic approach for quantifying uncertainties in low‐dimensional and high‐dimensional nonlinear models

A nonparametric probabilistic approach for modeling uncertainties in projection‐based, nonlinear, reduced‐order models is presented. When experimental data are available, this approach can also quantify uncertainties in the associated high‐dimensional models. The main underlying idea is twofold. First, to substitute the deterministic reduced‐order basis (ROB) with a stochastic counterpart. Second, to construct the probability measure of the stochastic reduced‐order basis (SROB) on a subset of a compact Stiefel manifold in order to preserve some important properties of a ROB. The stochastic modeling is performed so that the probability distribution of the constructed SROB depends on a small number of hyperparameters. These are determined by solving a reduced‐order statistical inverse problem. The mathematical properties of this novel approach for quantifying model uncertainties are analyzed through theoretical developments and numerical simulations. Its potential is demonstrated through several example problems from computational structural dynamics. Copyright © 2016 John Wiley & Sons, Ltd.     

A multilevel projection‐based model order reduction framework for nonlinear dynamic multiscale problems in structural and solid mechanics

A reduction/hyper reduction framework is presented for dramatically accelerating the solution of nonlinear dynamic multiscale problems in structural and solid mechanics. At each scale, the dimensionality of the governing equations is reduced using the method of snapshots for proper orthogonal decomposition, and computational efficiency is achieved for the evaluation of the nonlinear reduced‐order terms using a carefully designed configuration of the energy conserving sampling and weighting method. Periodic boundary conditions at the microscales are treated as linear multipoint constraints and reduced via projection onto the span of a basis formed from the singular value decomposition of Lagrange multiplier snapshots. Most importantly, information is efficiently transmitted between the scales without incurring high‐dimensional operations. In this proposed proper orthogonal decomposition–energy conserving sampling and weighting nonlinear model reduction framework, training is performed in two steps. First, a microscale hyper reduced‐order model is constructed in situ, or using a mesh coarsening strategy, in order to achieve significant speedups even in non‐parametric settings. Next, a classical offline–online training approach is performed to build a parametric hyper reduced‐order macroscale model, which completes the construction of a fully hyper reduced‐order parametric multiscale model capable of fast and accurate multiscale simulations. A notable feature of this computational framework is the minimization, at the macroscale level, of the cost of the offline training using the in situ or coarsely trained hyper reduced‐order microscale model to accelerate snapshot acquisition. The effectiveness of the proposed hyper reduction framework at accelerating the solution of nonlinear dynamic multiscale problems is demonstrated for two problems in structural and solid mechanics. Speedup factors as high as five orders of magnitude are shown to be achievable. Copyright © 2017 John Wiley & Sons, Ltd.     

In vitro germination and biochemical profiling of citrus reticulata in response to green synthesised zinc and copper nanoparticles

Green synthesis of nanoparticles by using plants is an emerging class of nanobiotechnology. It revolutionizes all the fields of nanobiotechnology by synthesizing chemical‐free nanoparticles for various purposes. In the present study, zinc and copper nanoparticles were synthesized by using the white leaves of Allium cepa and further characterized through Zeta analyzer and Scanning electron microscopy. Zeta analyzer elucidated that zinc nanoparticles ranged from 8‐32 nm while copper nanoparticles ranged from 15‐30 nm. Scanning electron microscopy clarified that zinc nanoparticles were irregular while copper nanoparticles were spherical in shape. The effects of green synthesized nanoparticles were evaluated on the germination frequency and biochemical parameters of plant tissues. The nucellus tissues were inoculated on Murashige and Skoog (MS) medium augmented with 30 µg/ml suspension of zinc and copper nanoparticles. Green synthesized nanoparticles enhanced the in vitro germination parameters because of their low toxicity and high efficacy. Significant results were obtained for germination parameters in response to the applications of zinc nanoparticles as compared to copper nanoparticles. These nanoparticles could also induce stress in plantlets by manipulating the endogenous mechanism as a result various defence compounds are produced which have potential in treating various human ailments. Copper nanoparticles showed higher toxicity as compared to zinc nanoparticles and triggered the production of antioxidative enzymes and non‐ enzymatic compounds.Inspec keywords: botany, zinc, copper, nanoparticles, nanofabrication, biochemistry, scanning electron microscopy, electrokinetic effects, biological tissues, toxicology, nanobiotechnology, biological techniquesOther keywords: in vitro germination, biochemical profiling, citrus reticulata, green synthesised zinc nanoparticles, green synthesised copper nanoparticles, green chemistry, secondary metabolites, nanoparticles synthesis, white leaves, Allium cepa, zeta analyser, scanning electron microscopy, onion extract, nucellus tissues, Murashige‐Skoog medium, biologically synthesised nanoparticles, toxicity, root length, shoot length, seedling vigour index, plantlets, endogenous mechanism, human ailments, antioxidative enzymes, nonenzymatic compounds, size 8 nm to 32 nm, Zn, Cu