Electrochemical behavior of Sn8Hg (gamma-2) and dental amalgam in a phosphate buffer solution

The gamma-2 phase (Sn₈Hg) and a dental amalgam in phosphate buffer have been studied by means of potentiostatic and galvanostatic techniques, along with a rotating ring-disc electrode (RRDE). The analysis of results has shown that phosphate ions play an important role in the corrosion of amalgam. The anodic reaction leads to the formation of soluble species and a passivating film, which is probably composed of tin hydroxide and tin phosphate.     

Synthesis and Characterization of Segmented Block Copolymers Based on Hydroxyl‐Terminated Liquid Natural Rubber and α,ω‐Diisocyanato Telechelics

Summary: Segmented block copolymers, consisting of non‐polar soft segments from hydroxyl‐terminated liquid natural rubber (HTNR) and polar hard segments from α,ω‐diisocyanato telechelics obtained by “criss‐cross”‐cycloaddition, have been synthesized. The block copolymer formation took place under relatively mild reaction conditions at 80 °C in dichloroethane in the presence of dibutyltin dilaurate as a catalyst. The resulting block copolymers were characterized by spectroscopic techniques (¹H NMR, FTIR, UV‐vis spectroscopy) as well as GPC for molar mass determination. The block copolymers were compression molded in a hot stage press, and the resulting samples were characterized by DSC and stress‐strain measurement. The solubility and phase morphology of the materials have also been studied.

Segmented block copolymer from HTNR and α,ω‐diisocyanato telechelics     

PSA performances and viscoelastic properties of SIS‐based PSA blends with H‐DCPD tackifiers

Hotmelt pressure sensitive adhesives (PSAs) usually contain styrenic block copolymers like styrene–isoprene–styrene (SIS), SBS, SEBS, tackifier, oil, and additives. These block copolymers individually reveal no tack. Therefore, a tackifier is a low molecular weight material with high glass transition temperature (T_g), and imparts the tacky property to PSA. The SIS block copolymer with different diblocks was blended with hydrogenated dicyclopentadiene (H‐DCPD tackifier), which has three kinds of T_g. PSA performance was evaluated by probe tack, peel strength, and shear adhesion failure temperature. PSA is a viscoelastic material, so that its performance is significantly related to the viscoelastic properties of PSAs. We tested the viscoelastic properties by dynamic mechanical analysis and the thermal properties by differential scanning calorimeter to investigate the relation between viscoelastic properties and PSA performance. © 2006 Wiley Periodicals, Inc. J Appl PolymSci 102: 2839–2846, 2006     

Integration Concepts for the Fabrication of LTCC Structures

At the Keck Smart Materials Integration Laboratory at Penn State University, low-temperature co-fired ceramic (LTCC) material systems have been used to fabricate a number of devices for a variety of applications. This article presents an overview of the integration of the concepts and materials that we have used to achieve miniaturization and unique device function. Examples of microwave filters, metamaterial antennas, and a dielectrophoretic cell sorter will be presented, with emphasis on device modeling and design, prototype construction methods, and test results.     

GPU-OSDDA: a bit-vector GPU-based deadlock detection algorithm for single-unit resource systems

This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm matrices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents.     

A study on effects of security risks on acceptance of enterprise cloud service: moderating of employment and non-employment using PLS multiple group analysis

This paper analyzed the effects of security risk factors fit for cloud computing paradigm on the acceptance of enterprise cloud service with intent to illuminate the factors for vitalizing the adoption of corporate cloud services in the future. The acceptance intention was set as a dependent variable. Independent variables were set in reference to the technology acceptance theory. Security risks were categorized into compliance risk, information leakage risk, troubleshooting risk and service discontinuation risk to design a model for analysis.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.