Effects of Solvent Exchange Period and Heat Treatment on Physical and Chemical Properties of Rice Husk Derived Silica Aerogels

In this study, hydrophobic silica aerogels were synthesized from rice husk ash-derived sodium silicate through sol-gel processing, solvent exchange, surface modification and ambient pressure drying. By volume, 10% of trimethylchlorosilane (TMCS) in 90% of n-hexane was used as a hydrophobic solution in the surface modification process. The physical and chemical properties of silica aerogels were characterized by density and porosity measurements, scanning electron microscopy (SEM), Fourier transforms infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller theory (BET) and dynamic scanning calorimetry (DSC). The hydrogels prepared were in the form of 2.5 ± 0.5 mm beads and then converted into alcogels through solvent exchange with ethanol for repetition of 3, 6 and 9 days. It is found that the optimal quality of silica aerogels with the BET surface area as high as 668.82 m²/g was obtained from the alcogels of the solvent exchange period of 9 days. Depending on the size of the gel’s block, a longer solvent exchange period will ensure adequate removal of pore water. Post heat treatment on silica aerogels obtained from the 9 days of solvent exchange at 200, 300 and 400 °C for 2 h results in slight decreased of aerogel’s density from 0.048 g/cm³ to 0.039 g/cm³ and the hydrophobicity of the aerogels is decreased above 380 °C as confirmed by DSC analysis.

     

Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks

This paper investigates the design of fault-tolerant TDMA-based data aggregation scheduling (DAS) protocols for wireless sensor networks (WSNs). DAS is a fundamental pattern of communication in wireless sensor networks where sensor nodes aggregate and relay data to a sink node. However, any such DAS protocol needs to be cognisant of the fact that crash failures can occur. We make the following contributions: (i) we identify a necessary condition to solve the DAS problem, (ii) we introduce a strong and weak version of the DAS problem, (iii) we show several impossibility results due to the crash failures, (iv) we develop a modular local algorithm that solves stabilising weak DAS and (v) we show, through simulations and an actual deployment on a small testbed, how specific instantiations of parameters can lead to the algorithm achieving very efficient stabilisation.     

Infrared spectroscopic investigation of tannins

Infrared spectroscopy is suggested as a diagnostic method for the characterisation and qualitative estimation of the two classes of tannins. Gallic acid, tannic acid and chebulinic acid have been taken as model compounds for the hydrolysable and catechin for the condensed tannins. The former class is marked by the presence of strong absorption maxima at 1710 – 35 cm^?1. The two classes have characteristic pattern of absorption, from which it is possible to characterise the particular type of tannin.     

Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal

The satellite-based regression model provides the data model that identifies water quality for inland and coastal waters. However, the satellite regression usually depends on the selection of observation, satellite data, and model type. A resampling simulation technique, such as sequential simulation using geographically weighted regression (GWR simulation), can be applied in generating multiple realizations for water quality estimation to reduce the sampling effect and consider spatial heterogeneity. Traditional models often result in considerable underestimation in extreme observations. The GWR simulation provides the best goodness of fit and spatial varying relationship between observed water quality and remote sensing considering parameter outlier and noise removal for parameter stability. This simulation model can increase the sampling diversity from various observations and reduce the neighboring effects of observations using outlier and noise removal. The model that handles spatial uncertainty and heterogeneity is a novel tool for inferring the characteristics of water quality from a series of sample subsets.

     

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

Magnetic Resonance Materials in Physics, Biology and Medicine - The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of...     

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.

     

Issues and strategies in composite fabrication via friction stir processing: A review

Friction stir processing (FSP) is an expeditiously emerging novel technique involving exterior layer modification, which enables one to successfully fabricate surface composites (SCs) as well as bulk composites of the metal matrix. SCs constitute an exclusive class of composites which exhibit improved surface properties while retaining the bulk properties unaltered. During initiative years, FSP was employed in development of SCs of light metal alloys like aluminum. But, nowadays, it has gained a shining role in the field of SC fabrication of various nonferrous alloys like aluminum, magnesium, copper, and even ferrous metals like steel etc. This article reviews the current trends, various issues, and strategies used to enhance the efficiency of the fabrication process of SCs. Factors involved in the process of SC fabrication are discussed and classified with a new approach. Also, variation of microstructural and mechanical characteristics with these factors is reviewed. In addition to a brief presentation on the interaction between various inputs and their effects on properties, a summary of literature on SC fabrication for different metals is tabulated with prominent results. Subsequently, shortfalls and future perspectives of FSP on SC fabrication domain are discussed.     

Stress Reduction of 3D Printed Compliance‐Tailored Multilayers

Multilayered multi‐material interfaces are encountered in an array of fields. Here, enhanced mechanical performance of such multi‐material interfaces is demonstrated, focusing on strength and stiffness, by employing bondlayers with spatially‐tuned elastic properties realized via 3D printing. Compliance of the bondlayer is varied along the bondlength with increased compliance at the ends to relieve stress concentrations. Experimental testing to failure of a tri‐layered assembly in a single‐lap joint configuration, including optical strain mapping, reveals that the stress and strain redistribution of the compliance‐tailored bondlayer increases strength by 100% and toughness by 60%, compared to a constant modulus bondlayer, while maintaining the stiffness of the joint with the homogeneous stiff bondlayer. Analyses show that the stress concentrations for both peel and shear stress in the bondlayer have a global minimum when the compliant bond at the lap end comprises ≈10% of the bondlength, and further that increased multilayer performance also holds for long (relative to critical shear transfer length) bondlengths. Damage and failure resistance of multi‐material interfaces can be improved substantially via the compliance‐tailoring demonstrated here, with immediate relevance in additive manufacturing joining applications, and shows promise for generalized joining applications including adhesive bonding.     

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.