通过细乳液聚合方法制备自交联型有机硅乳液

苯乙烯与甲基丙烯酸-3-三甲氧基硅丙酯(M PS)经细乳液聚合,制得了稳定的自交联型的水分散涂料,用透射电子显微镜(TEM)、动态光散射仪(DLS)和红外光谱(IR)表征了乳胶粒的形态和结构。发现与传统乳液聚合相比,细乳液聚合法可显著提高乳液的稳定性,且M PS加入量的增加会导致乳液稳定性降低。     

Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training

Introduction

Virtual reality simulation training may improve knowledge of anatomy and surgical skills. We evaluated a 3-dimensional, haptic, virtual reality temporal bone simulator for dissection training.

Methods

The subjects were 7 otolaryngology residents (3 training sessions each) and 7 medical students (1 training session each). The virtual reality temporal bone simulation station included a computer with software that was linked to a force-feedback hand stylus, and the system recorded performance and collisions with vital anatomic structures. Subjects performed virtual reality dissections and completed questionnaires after the training sessions.

Results

Residents and students had favorable responses to most questions of the technology acceptance model (TAM) questionnaire. The average TAM scores were above neutral for residents and medical students in all domains, and the average TAM score for residents was significantly higher for the usefulness domain and lower for the playful domain than students. The average satisfaction questionnaire for residents showed that residents had greater overall satisfaction with cadaver temporal bone dissection training than training with the virtual reality simulator or plastic temporal bone. For medical students, the average comprehension score was significantly increased from before to after training for all anatomic structures. Medical students had significantly more collisions with the dura than residents. The residents had similar mean performance scores after the first and third training sessions for all dissection procedures.

Discussion

The virtual reality temporal bone simulator provided satisfactory training for otolaryngology residents and medical students.     

Security-aware intermediate data placement strategy in scientific cloud workflows

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy data-intensive applications without the infrastructure investment, where large application datasets can be stored in the cloud. Based on the pay-as-you-go model, data placement strategies have been developed to cost-effectively store large volumes of generated datasets in the scientific cloud workflows. As promising as it is, this paradigm also introduces many new challenges for data security when the users outsource sensitive data for sharing on the cloud servers, which are not within the same trusted domain as the data owners. This challenge is further complicated by the security constraints on the potential sensitive data for the scientific workflows in the cloud. To effectively address this problem, we propose a security-aware intermediate data placement strategy. First, we build a security overhead model to reasonably measure the security overheads incurred by the sensitive data. Second, we develop a data placement strategy to dynamically place the intermediate data for the scientific workflows. Finally, our experimental results show that our strategy can effectively improve the intermediate data security while ensuring the data transfer time during the execution of scientific workflows.     

Object Bank: An Object-Level Image Representation for High-Level Visual Recognition

It is a remarkable fact that images are related to objects constituting them. In this paper, we propose to represent images by using objects appearing in them. We introduce the novel concept of object bank (OB), a high-level image representation encoding object appearance and spatial location information in images. OB represents an image based on its response to a large number of pre-trained object detectors, or ‘object filters’, blind to the testing dataset and visual recognition task. Our OB representation demonstrates promising potential in high level image recognition tasks. It significantly outperforms traditional low level image representations in image classification on various benchmark image datasets by using simple, off-the-shelf classification algorithms such as linear SVM and logistic regression. In this paper, we analyze OB in detail, explaining our design choice of OB for achieving its best potential on different types of datasets. We demonstrate that object bank is a high level representation, from which we can easily discover semantic information of unknown images. We provide guidelines for effectively applying OB to high level image recognition tasks where it could be easily compressed for efficient computation in practice and is very robust to various classifiers.     

Use of mesoporous SBA-15 for nanostructuring titania for photocatalytic applications

SBA15–TiO₂ samples prepared by introducing titanium with a grafting method and having TiO₂ loadings below 15 wt.% have been characterized by XRF, XRD, IR, porosimetry, SEM, HRTEM, and UV–Visible diffuse reflectance. Differently from the samples reported in the literature characterized by a high TiO₂ loading, no evidences have been found for the presence of titania particles inside or outside the mesopores of SBA-15. Three different titanium species were instead evidenced to be present. The first two derive from the reaction of titanium with silanol groups in the corona area of inner SBA-15 walls leading to the formation of either TiO₄ tetrahedral sites (by reaction by hydroxyl nests of surface defect sites) and/or pseudo-octahedral surface sites anchored by two (or more) Si or Ti ions through bridging oxygens. The third species derives from the reaction of titanium in the regions with high sylanol density, e.g. in the micropores located in the corona of SBA-15 channels, leading to the formation of TiO₂-like nanoareas (probably Si-doped) with dimensions of around 1–2 nm maximum. The potential interest of these materials as photocatalysts, for the presence of a TiO₂-like nanoareas highly accessible by reactants, is discussed.     

Analysing neurobiological models using communicating automata

Two important issues in computational modelling in cognitive neuroscience are: first, how to formally describe neuronal networks (i.e. biologically plausible models of the central nervous system), and second, how to analyse complex models, in particular, their dynamics and capacity to learn. We make progress towards these goals by presenting a communicating automata perspective on neuronal networks. Specifically, we describe neuronal networks and their biological mechanisms using Data-rich Communicating Automata, which extend classic automata theory with rich data types and communication. We use two case studies to illustrate our approach. In the first case study, we model a number of learning frameworks, which vary in respect of their biological detail, for instance the Backpropagation (BP) and the Generalized Recirculation (GeneRec) learning algorithms. We then used the SPIN model checker to investigate a number of behavioral properties of the neural learning algorithms. SPIN is a well-known model checker for reactive distributed systems, which has been successfully applied to many non-trivial problems. The verification results show that the biologically plausible GeneRec learning is less stable than BP learning. In the second case study, we presented a large scale (cognitive-level) neuronal network, which models an attentional spotlight mechanism in the visual system. A set of properties of this model was verified using Uppaal, a popular real-time model checker. The results show that the asynchronous processing supported by concurrency theory is not only a more biologically plausible way to model neural systems, but also provides a better performance in cognitive modelling of the brain than conventional artificial neural networks that use synchronous updates. Finally, we compared our approach with several other related theories that apply formal methods to cognitive modelling. In addition, the practical implications of the approach are discussed in the context of neuronal network based controllers.     

Dissecting the protocol and network traffic of the OnLive cloud gaming platform

Cloud gaming is a new paradigm that is envisaged to play a pivotal role in the video game industry in forthcoming years. Cloud gaming, or gaming on demand, is a type of online gaming that allows on-demand streaming of game content onto non-specialised devices (e.g. PC, smart TV, etc.). This approach requires no downloads or game installation because the actual game is executed on the game company’s server and is streamed directly to the client. Nonetheless, this revolutionary approach significantly affects the network load generated by online games. As cloud gaming presents new challenges for both network engineers and the research community, both groups need to be fully conversant with these new cloud gaming platforms. The purpose of this paper is to investigate OnLive, one of the most popular cloud gaming platforms. Our key contributions are: (a) a review of the state-of-the-art of cloud gaming; (b) reverse engineering of the OnLive protocol; and (c) a synthetic traffic model for OnLive.     

空间资源索引与top-k查询研究

以空间资源索引结构R-tree为基础, 考虑人们对空间资源能力指标的要求, 利用道路网络模型进行空间距离的计算, 提出了一种包含能力维度信息的空间资源索引结构和top-k查询算法, 并与传统遍历算法进行对比实验, 验证了查询算法的有效性。     

Industrial TiO2 based nacreous pigments as functional building materials: Photocatalytic removal of NO

In this paper, the photocatalytic activity of industrial titanium dioxide (TiO₂) based nacreous pigments was researched as functional building materials for photocatalytic NO remove. Three industrial TiO₂ based nacreous pigments were selected to estimate the photocatalytic activity for NO remove. This study is a good proof that pearlescent pigments can eliminate NO, and its performance is positively correlated with its titanium dioxide content. And this research will widen the application of nacreous pigments in functional building materials, and provide a new way to eliminate in door nitric oxide pollution.