Surface modification and properties analysis of shell powder with sodium stearate surface modifier

In this study, shell powder was modified by sodium stearate surface modifier for improving the compatibility of SP with polymer materials. The surface modifiers influence on the physical and chemical properties of SP were studied by scanning electron microscope(SEM), fourier infrared spectrum(FT-IR), surface contact angle meter, XRD diffraction analysis meter and other modern instruments and analysis method. The results showed that the surface modifier was successfully coupled to the shell powder surface. After surface modifier modification, the interfacial compatibility of the shell powder with polymer materials was effectively improved. The contact angle of shell powder surface increased from 73.5 ° to 110.8 °, along with the dosage of sodium stearate surface modifier was 4.0%. All results suggested that modified shell powder is promising for using as a reinforcement filler in polymer materials. © (2014) Trans Tech Publications, Switzerland.

On the multicast lifetime of WANETs with multibeam antennas : Formulation, algorithms, and analysis

We explore the multicast lifetime capacity of energy-limited wireless ad hoc networks using directional multibeam antennas by formulating and solving the corresponding optimization problem. In such networks, each node is equipped with a practical smart antenna array that can be configured to support multiple beams with adjustable orientation and beamwidth. The special case of this optimization problem in networks with single beams have been extensively studied and shown to be NP-hard. In this paper, we provide a globally optimal solution to this problem by developing a general MILP formulation that can apply to various configurable antenna models, many of which are not supported by the existing formulations. In order to study the multicast lifetime capacity of large-scale networks, we also propose an efficient heuristic algorithm with guaranteed theoretical performance. In particular, we provide a sufficient condition to determine if its performance reaches optimum based on the analysis of its approximation ratio. These results are validated by experiments as well. The multicast lifetime capacity is then quantitatively studied by evaluating the proposed exact and heuristic algorithms using simulations. The experimental results also show that using two-beam antennas can exploit most lifetime capacity of the networks for multicast communications. © 2013 IEEE.

Silk powder for regenerative medicine

Two approaches are used for silk particle production: bottom up and top down. In the bottom up approach, different liquid-solid phase transfer techniques are adapted to fabricate particles from silk solution. In the top down approach, silk fibres are milled by various means to prepare ultrafine silk particles. Many important properties of particles such as size, geometry, porosity, stability and biodegradability are dependent on the specific methods of particle production. These properties influence drug loading and release, delivery modes, biocompatibility and their clearance from the body. Particle properties also determine biomechanical properties of particle reinforced composite scaffolds. Thus correlation between preparation, characterisation and application of silk particles for a specific biomedical application is critical. Progress made in this direction and challenges ahead are discussed in this chapter. © 2014 Woodhead Publishing Limited. All rights reserved.

Synchrotron powder diffraction study of cements pastes

 Knowledge of the degree of hydration of cement pastes is critical for determining properties such as the durability of concrete. As part of an integrated study on the prediction of chloride ingress in reinforced concrete, synchrotron Xray powder diffraction was used to estimate the degree of hydration of cement pastes. While for the past 20 years the composition of Portland cement has been determined by Rietveld analysis of X-ray diffraction, nevertheless there are a number of factors, including the amorphous content of the cement and relative proportion of mineral polymorphs present in the initial clinker, whose impact on the analysis are still not completely understood. Analysis of the resulting diffraction patterns indicated enhanced identification of polymorphs of alite, belite, ferrite and aluminate, which are present in the initial unhydrated cement and clinker, as well as improved quantification of hydrated crystalline phases such as calcium hydroxide and ettringite, which are key phases determining the speed of the chemical reactions in cement. In this paper we describe the experience that we have gained in the determination of the degree of hydration of cement pastes. We detail the standards and precautions that we took to characterize production cements and their hydration products.

Effects of milling time on powder packing characteristics and compressive mechanical properties of sintered Ti-10Nb-3Mo alloy

The influence of milling time on the powder packing characteristics and compressive mechanical properties of a biomedical Ti-10Nb-3Mo alloy (wt.%) was investigated. Ball milling was performed on elemental metal powders at different milling times of 0 (blended), 2, 4, 6, 8, and 10 h. This article demonstrates that despite the beneficial effects of ball milling technique in the mechanical alloying of the Ti-based alloy, the ball-milled powders synthesized at longer milling times can adversely affect the packing density and significantly diminish the compressive mechanical properties of the sintered powders. Crown

What shall i share and with whom? A multi-task learning formulation using multi-faceted task relationships

Multi-task learning is a learning paradigm that improves the performance of "related" tasks through their joint learning. To do this each task answers the question "Which other task should I share with"? This task relatedness can be complex - a task may be related to one set of tasks based on one subset of features and to other tasks based on other subsets. Existing multi-task learning methods do not explicitly model this reality, learning a single-faceted task relationship over all the features. This degrades performance by forcing a task to become similar to other tasks even on their unrelated features. Addressing this gap, we propose a novel multi-task learning model that leams multi-faceted task relationship, allowing tasks to collaborate differentially on different feature subsets. This is achieved by simultaneously learning a low dimensional sub-space for task parameters and inducing task groups over each latent subspace basis using a novel combination of L1 and pairwise L∞ norms. Further, our model can induce grouping across both positively and negatively related tasks, which helps towards exploiting knowledge from all types of related tasks. We validate our model on two synthetic and five real datasets, and show significant performance improvements over several state-of-the-art multi-task learning techniques. Thus our model effectively answers for each task: What shall I share and with whom?