Phase behavior of ternary polymer blends

The effect of varying interaction parameters on the phase diagrams of ternary polymer blends was explored by simulating spinodals through use of the Flory-Huggins lattice theory. Results indicate that miscibility is favored for the case of ternary mixtures of marginally miscible or marginally immiscible pairs where all pair interactions are nearly athermal. Miscibility is restricted for asymmetric ternary blends when one of the polymer pairs is either strongly miscible or strongly immiscible. For symmetric blends of partially immiscible pairs, both two-phase and three-phase miscibility gaps are predicted.     

环己烷液相无催化剂的氧化动力学研究

采用搅拌釜反应器,在确认已排除传质因素对反应速率影响的情况下,研究了环己烷液相无催化剂的氧化反应动力学。应用自由基理论及最优化计算技术,从导出的8个候选动力学模型中,确定了最佳的动力学模型,它能满意地描述环已烷氧化过程。该动力学可为环己烷氧化工业生产操作条件的优化、反应器的选型及工业设计提供依据。     

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。     

Improvement of Microwave Loss Tangent and Tunability of Ba0.55Sr0.45TiO3/MgO Composites Using the Heterogeneous Precipitation Method

Ba_0.55Sr_0.45TiO₃/MgO composites were successfully prepared by the heterogeneous precipitation method and their structural, surface morphological, tunable properties, and dielectric properties at microwave frequency were systemically investigated. Compared with the sample prepared by the traditional solid-state method, the sample prepared by the heterogeneous precipitation method exhibits a smaller grain size, more uniform microstructure, higher tunability, and lower microwave loss, and these properties are very beneficial to the development of the microwave tunable devices application. Moreover, the effects of La₂O₃ doping on the dielectric and tunable properties of BST/MgO composites are investigated. The result shows that the La³⁺-doped sample has higher tunability and lower microwave loss than the undoped one.     

基于充电设施型谐波补偿的研究及应用

本文通过对充电设施工作产生的谐波分析及仿真,得出采用基于SPWM脉宽调制闭环指令电流的跟踪控制方法,采用闭环能量回馈方案的装置对充电设施谐波补偿的高精度控制进行验证,有效检测抵消充电设施引起的谐波,实现对充电设施谐波污染的治理,提升电能质量。     

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.     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition

Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods.     

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.