Kinetics of Thermal Dehydroxylation and Carbonation of Magnesium Hydroxide

The kinetics of simultaneous dehydroxylation and carbonation of precipitated Mg(OH)₂ were studied using isothermal and nonisothermal thermogravimetric analyses. Specimens were analyzed using X-ray diffraction, transmission electron microscopy, and through measurements of the volume of carbon dioxide evolved in a subsequent reaction with hydrochloric acid. From 275° to 475°C, the kinetics of isothermal dehydroxylation in helium were best fit to a contracting-sphere model, yielding an activation energy of 146 kJ/mol, which was greater than values reported in the literature for isothermal dehydroxylation under vacuum (53–126 kJ/mol). The carbonation kinetics were complicated by the fact that dehydroxylation occurred simultaneously. The overall kinetics also could be fit to a contracting-sphere model, yielding a net activation energy of 304 kJ/mol. The most rapid carbonation kinetics occurred near 375°C. At this temperature, Mg(OH)₂ underwent rapid dehydroxylation and subsequent phase transformation, whereas thermodynamics favored the formation of carbonate. During carbonation, MgCO₃ precipitated on the surface of disrupted Mg(OH)₂ crystals acting as a kinetic barrier to both the outward diffusion of H₂O and the inward diffusion of CO₂.     

Ultrahigh molecular weight polyethylene with improved crosslink density,oxidation stability,and microbial inhibition by chemical crosslinking and tea polyphenols for total joint replacements

Highly crosslinked ultrahigh molecular weight polyethylene (UHMWPE) stabilized by vitamin E (VE) is widely applied in artificial joints as the bearings. Despite the approval, there is a discord that VE lowers the crosslinking efficiency, limiting its use at high concentration. In this work, we aim to obtain highly crosslinked and oxidation resistant UHMWPE through the conjunction of tea polyphenol and chemical crosslinking. We hypothesized that highly incorporated tea polyphenol with multiple reactive sites can ameliorate crosslinking efficiency of chemical crosslinked UHMWPE in comparison to VE. Epigallocatechin gallate (EGCG) as representative tea polyphenol was incorporated into UHMWPE at high concentration (2–8 wt%), followed by chemical crosslinking with 2 wt% organic peroxide. Unlike VE/UHMWPE blends as the control, chemical crosslinking achieved an increasing trend in crosslink density of EGCG/UHMWPE blends with increasing antioxidant concentration. High concentration of EGCG also enhanced the oxidation stability of UHMWPE. Intriguingly, EGCG endowed UHMWPE with an excellent antimicrobial property, which was inefficient in VE/UHMWPE. Cell viability was hardly affected by the high loaded antioxidant and peroxide. The chemically crosslinked UHMWPE blended with EGCG is proved to be a reasonable, cost effective and realistic alternative for use in artificial joints.     

Your comments matter: incorporating viewers’ comments for ranking online video content using bibliometrics

ABSTRACT

The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices h_vc, g_vc and m_vc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.     

Adaptive dynamic surface control of a hypersonic flight vehicle with improved tracking

This work presents a nonlinear adaptive dynamic surface air speed and a flight path angle control design procedure for the longitudinal dynamics of a generic hypersonic flight vehicle. The proposed design scheme takes into account the magnitude, rate, and bandwidth constraints on the actuator signals. A new approach is used to enhance tracking performance and avoid a large initial control signal. The uncertain nonlinear functions in the flight vehicle model are approximated by using radial basis function neural networks. A detailed stability analysis of the designed controllers shows that all the signals of the closed‐loop system are uniformly ultimately bounded. The robust performance of the design scheme is verified through numerical simulations of the flight vehicle model for various parameter variation test cases. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Three-qubit Grover’s algorithm using superconducting quantum interference devices in cavity-QED

We present a scheme for the implementation of three-qubit Grover’s algorithm using four-level superconducting quantum interference devices (SQUIDs) coupled to a superconducting resonator. The scheme is based on resonant, off-resonant interaction of the cavity field with SQUIDs and application of classical microwave pulses. We show that adjustment of SQUID level spacings during the gate operations, adiabatic passage, and second-order detuning are not required that leads to faster implementation. We also show that the marked state can be searched with high fidelity even in the presence of unwanted off-resonant interactions, level decay, and cavity dissipation.     

Extended Kalman filter based speeds estimation of series-connected five-phase two-motor drive system

The multi-phase machines enables independent control of a few number of machines that are connected in series in a particular manner, and the supply is fed from a single voltage source inverter (VSI). The idea was first implemented for a five-phase series-connected two-motor drive system, but is now applicable to any number of phases. The number of series-connected machines is a function of the phase number of VSI. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. Open-loop estimators or closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of an EKF-based sensorless control of vector controlled series-connected two-motor five-phase drive system with current control in the stationary reference frame. Results, obtained with fixed voltage and fixed frequency supply fed and hysteresis current control, is presented for various operating conditions on the basis of simulation. The purpose of this paper is to report first time, the simulation results on a sensorless control of a five-phase two-motor series-connected drive system using EKF estimator.     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Biosynthesis and characterisation of nano‐silica as potential system for carrying streptomycin at nano‐scale drug delivery

A growing trend within nanomedicine has been the fabrication of self‐delivering supramolecular nanomedicines containing a high and fixed drug content ensuring eco‐friendly conditions. This study reports on green synthesis of silica nanoparticles (Si‐NPs) using Azadirachta indica leaves extract as an effective chelating agent. X‐ray diffraction analysis and Fourier transform‐infra‐red spectroscopic examination were studied. Scanning electron microscopy analysis revealed that the average size of particles formed via plant extract as reducing agent without any surfactant is in the range of 100–170 nm while addition of cetyltrimethyl ammonium bromide were more uniform with 200 nm in size. Streptomycin as model drug was successfully loaded to green synthesised Si‐NPs, sustain release of the drug from this conjugate unit were examined. Prolong release pattern of the adsorbed drug ensure that Si‐NPs have great potential in nano‐drug delivery keeping the environment preferably biocompatible, future cytotoxic studies in this connection is helpful in achieving safe mode for nano‐drug delivery.Inspec keywords: silicon compounds, nanofabrication, nanomedicine, drug delivery systems, nanoparticles, X‐ray diffraction, Fourier transform infrared spectra, scanning electron microscopyOther keywords: nanosilica, streptomycin, nanoscale drug delivery, nanomedicine, silica nanoparticles, Azadirachta indica leaves extract, X‐ray diffraction analysis, Fourier transform‐infrared spectroscopy, scanning electron microscopy, cetyltrimethyl ammonium bromide, SiO₂      

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.