Characterisation of unbound aggregate materials considering physical and morphological properties

The objective of this paper is to evaluate the factors affecting resilient and permanent deformation behaviour of unbound granular materials, with a focus on the aggregate physical and morphological characteristics. To evaluate the behaviour of base course, repeated load triaxial testing is commonly used to establish the stress-dependent resilient modulus properties of unbound aggregate base and subbase materials. Although resilient modulus of aggregates is a critical input into mechanistic-empirical pavement design methods, the resilient modulus of unbound base material is often estimated from empirical correlations with index properties in the AASHTOWare Pavement ME design procedure for its simplicity. Since actual field stress conditions and resilient modulus stress states are generally quite different from those generated in the empirical test methods, use of an empirical correlation could lead to an unreliable prediction of resilient modulus and permanent deformation. In order to properly assess the stability of an unbound aggregate layer, it is necessary to establish a proper process to understand the factors affecting fundamental and performance-related properties of unbound granular materials. In this study, aggregate samples from four different sources were tested for resilient modulus and Poisson’s ratio measurements using the Precision Unbound Material Analyzer equipment. Morphological or shape properties of aggregate samples were also measured using an image analysis device. The results demonstrate that aggregate physical and morphological properties affect aggregate resilient and permanent deformation. Further, it is suggested that the resilient modulus of the aggregate should not be used as the sole indicator of rutting performance of aggregate base.     

Camphor‐Enabled Transfer and Mechanical Testing of Centimeter‐Scale Ultrathin Films

Camphor is used to transfer centimeter‐scale ultrathin films onto custom‐designed substrates for mechanical (tensile) testing. Compared to traditional transfer methods using dissolving/peeling to remove the support‐layers, camphor is sublimed away in air at low temperature, thereby avoiding additional stress on the as‐transferred films. Large‐area ultrathin films can be transferred onto hollow substrates without damage by this method. Tensile measurements are made on centimeter‐scale 300 nm‐thick graphene oxide film specimens, much thinner than the ≈2 μm minimum thickness of macroscale graphene‐oxide films previously reported. Tensile tests were also done on two different types of large‐area samples of adlayer free CVD‐grown single‐layer graphene supported by a ≈100 nm thick polycarbonate film; graphene stiffens this sample significantly, thus the intrinsic mechanical response of the graphene can be extracted. This is the first tensile measurement of centimeter‐scale monolayer graphene films. The Young's modulus of polycrystalline graphene ranges from 637 to 793 GPa, while for near single‐crystal graphene, it ranges from 728 to 908 GPa (folds parallel to the tensile loading direction) and from 683 to 775 GPa (folds orthogonal to the tensile loading direction), demonstrating the mechanical performance of large‐area graphene in a size scale relevant to many applications.     

Social Comparison,Goal Contagion,and Adoption of Innovative Information Technology

When adopting a new, innovative information technology (IT), most institutions tend to lack knowledge about it initially. However, they often adopt new ITs despite their illiteracy. This behavior is difficult to explain using rational IT adoption theories. Focusing on the organizational decision-making process behind adoption of innovative IT, we examine the strategy by which some companies compensate for their lack of knowledge: collection of information from other companies that have successfully adopted IT previously. We develop a new IT adoption model for which goal contagion theory and social comparison theory are combined to interpret the circumstances under which organizations tend to adopt new, innovative IT despite limited knowledge about it. Big data, cloud services, and smart mobile systems are considered as examples of innovative IT in the empirical study.     

Fine metal mask material and manufacturing process for high‐resolution active‐matrix organic light‐emitting diode displays

Manufacturing fine metal mask (FMM) is one of the biggest hurdles to realize the ultra‐high definition (UHD) grade AMOLED displays for smartphone and augmented reality (AR). We have developed the state‐of‐the‐art material and processing technology to achieve 800ppi or higher‐resolution FMMs. The Invar thinning and the thermal damage‐free laser ablation process realized us achieving the FMM for UHD displays.     

Toward high‐resolution,inkjet‐printed,quantum dot light‐emitting diodes for next‐generation displays

We have investigated the possibility of fabricating quantum dot light‐emitting diodes (QLEDs) using inkjet printing technology, which is the most attractive method for the full‐color patterning of QLED displays. By controlling the quantum dot (QD) ink formulation and inkjet printing condition, we successfully patterned QLED pixels in the 60‐in ultrahigh definition TV format, which has a resolution of 73 pixels per inch. The inkjet‐printed QLEDs exhibited a maximum luminance of 2500 cd/m². Although the performance of inkjet‐printed QLEDs is low compared with that of QLEDs fabricated using the spin‐coating process, our results clearly indicate that the inkjet printing technology is suitable for patterning QD emissive layers to realize high‐resolution, full‐color QLED displays.     

PARADISE: Big data analytics using the DBMS tightly integrated with the distributed file system

There has been a lot of research on MapReduce for big data analytics. This new class of systems sacrifices DBMS functionality such as query languages, schemas, or indexes in order to maximize scalability and parallelism. However, as high functionality of the DBMS is considered important for big data analytics as well, there have been a lot of efforts to support DBMS functionality in MapReduce. HadoopDB is the only work that directly utilizes the DBMS for big data analytics in the MapReduce framework, taking advantage of both the DBMS and MapReduce. However, HadoopDB does not support sharability for the entire data since it stores the data into multiple nodes in a shared-nothing manner—i.e., it partitions a job into multiple tasks where each task is assigned to a fragment of data. Due to this limitation, HadoopDB cannot effectively process queries that require internode communication. That is, HadoopDB needs to re-load the entire data to process some queries (e.g., 2-way joins) or cannot support some complex queries (e.g., 3-way joins). In this paper, we propose a new notion of the DFS-integrated DBMS where a DBMS is tightly integrated with the distributed file system (DFS). By using the DFS-integrated DBMS, we can obtain sharability of the entire data. That is, a DBMS process in the system can access any data since multiple DBMSs are run on an integrated storage system in the DFS. To process big data analytics in parallel, our approach use the MapReduce framework on top of a DFS-integrated DBMS. We call this framework PARADISE. In PARADISE, we employ a job splitting method that logically splits a job based on the predicate in the integrated storage system. This contrasts with physical splitting in HadoopDB. We also propose the notion of locality mapping for further optimization of logical splitting. We show that PARADISE effectively overcomes the drawbacks of HadoopDB by identifying the following strengths. (1) It has a significantly faster (by up to 6.41 times) amortized query processing performance since it obviates the need to re-load data required in HadoopDB. (2) It supports query types more complex than the ones supported by HadoopDB.     

Entropy difference-based early skip detection technique for high-efficiency video coding

Recently, high-efficiency video coding (HEVC) has been developed as a new video coding standard focusing on the coding of ultrahigh definition videos as high-resolution and high-quality videos are getting more popular. However, one of the most important challenges in this new standard is its encoding time complexity. Due to this it is quite difficult to implement the HEVC encoder as a real-time system. In this paper, we have addressed this problem in a new way. Generally, for a natural video sequence good amount of coding blocks are “skip” in nature, which need not be transmitted and can be generated in the decoder side using the reference pictures. In this paper, we propose an early skip detection technique for the HEVC. Our proposed method is based on identifying the motionless and homogeneous regions in a video sequence. Moreover, a novel entropy difference-based calculation is proposed in this paper which can predict the skip coding blocks more accurately in a natural video sequence. The experimental result shows our proposed technique can achieve more than 30 % encoding time reduction than the conventional HEVC encoder with negligible degradation in video quality.     

Optimization techniques to enable execution offloading for 3D video games

Nowadays, mobile devices are becoming the most popular computing device as their computing capabilities increase rapidly. However, it is still challenging to execute highly sophisticated applications such as 3D video games on mobile devices due to its constrained key computational resources. Execution offloading approaches have been proposed to resolve this problem by strengthening mobile devices with powerful cloud. Unfortunately, the existing offloading approaches are not suitable for 3D video games because of the unique execution characteristics of them. In this paper, we propose a streaming-based execution offloading framework to enable execution offloading for 3D video games. The experiments show that our framework successfully guarantees 20 frames per second for our benchmark.