石膏制硫酸的技术评述

介绍了国外利用石膏制硫酸联产水泥装置，对窑气组成，净化流程，干吸与水平衡、转化与热平衡、尾气回收等技术进行了重点评述。     

后掺粗骨料工艺对高性能泵送混凝土性能影响研究

为解决建筑工程中高性能泵送混凝土的环境问题、成本问题,基于粗骨料嵌锁效应和界面水灰比强度理论,在保证高性能和泵送要求的条件下,提出了后掺骨料工艺,并对不同粗骨料后掺比例下的混凝土进行了力学性能和抗冻性能的研究。结果表明,随着粗骨料后掺比例的增大,7 d、28 d混凝土抗压强度持续增大,而28 d混凝土劈裂抗拉强度和抗折强度均呈先增大后减小的趋势;在冻融循环作用下,质量损失率的上升速率呈先减小后增大的趋势,而抗压、抗拉、抗折强度、动弹性模量以及超声波速的下降速率均呈先减小后增大的趋势。据此建议本C30高性能泵送混凝土的粗骨料最佳后掺比例为20%。     

Visual tracking via weakly supervised learning from multiple imperfect oracles

Notwithstanding many years of progress, visual tracking is still a difficult but important problem. Since most top-performing tracking methods have their strengths and weaknesses and are suited for handling only a certain type of variation, one of the next challenges is to integrate all these methods and address the problem of long-term persistent tracking in ever-changing environments. Towards this goal, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth are provided by multiple imperfect oracles (i.e., different trackers). These trackers naturally have intrinsic diversity due to their different design strategies, and we propose a probabilistic method to simultaneously infer the most likely object position by considering the outputs of all trackers, and estimate the accuracy of each tracker. An online evaluation strategy of trackers and a heuristic training data selection scheme are adopted to make the inference more effective and efficient. Consequently, the proposed method can avoid the pitfalls of purely single tracking methods and get reliably labeled samples to incrementally update each tracker (if it is an appearance-adaptive tracker) to capture the appearance changes. Extensive experiments on challenging video sequences demonstrate the robustness and effectiveness of the proposed method.     

如何降低汽车涂装过程造成的污染

本文围绕汽车涂装过程中使用的涂装材料、涂装工艺及设备,阐述了降低汽车涂装过程中造成污染的措施,指出采用低污染材料、先进的生产设备,并优化生产工艺,是解决汽车涂装“三废”问题的有效途径。     

Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies

High-tech companies encounter intense competition in today's global economy. With rapid changes in working environments, high-tech employees must learn quickly and effectively to solve difficult problems and increase their productivity. Many large high-tech companies have recently implemented electronic learning (e-learning) for employee training. However, e-learning systems are expensive and often underutilised. Therefore, understanding the factors associated with acceptance to e-learning are of priority concern. By integrating locus of control, computer self-efficacy and technology acceptance model (TAM) into one model, this study examines the feasibility of the extended TAM to explain employee acceptance of e-learning systems. Data were collected from 223 employees at five high-tech companies located in the Hsinchu Science Park, Taiwan. Analytical results indicate that locus of control had significant direct effects on perceived usefulness and perceived ease of use. Computer self-efficacy had significant direct effects on perceived ease of use and behavioural intention to use. Overall, analytical results provide strong support for using the extended TAM to explain user acceptance of e-learning systems. The research and practical implications of findings are discussed.     

Enabling customers engagement and collaboration for small and medium-sized enterprises in ubiquitous multi-channel ecosystems

Over the last few years, we have encountered an exponential growth in online communication opportunities. Organizations have more and more ways to connect and engage with their current or future customers. The existence of more opportunities in connecting to people can be both an enabler and a burden. Being present at a multitude of different channels requires the effective management of a very large number of adapted contents, formats, and interaction patterns fulfilling the communication and cooperation needs of distributed target groups. In this respect, we integrate existing fragmented communication and monitoring approaches into a full-fledged communication model as a basis for an adequate engagement approach. We describe applications of our approach in both the eTourism and manufacturing domain. In this paper, we introduce an approach that will enable communication, collaboration and value exchange of users through a multitude of online interaction possibilities based on the use of semantic technology. Finally, we also compare our approach with existing solutions with respect to the identified challenges in this subject.     

LoVis: Local Pattern Visualization for Model Refinement

Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.     

Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each user's specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.     

Group‐in‐a‐Box Meta‐Layouts for Topological Clusters and Attribute‐Based Groups: Space‐Efficient Visualizations of Network Communities and Their Ties

An important part of network analysis is understanding community structures like topological clusters and attribute‐based groups. Standard approaches for showing communities using colour, shape, rectangular bounding boxes, convex hulls or force‐directed layout algorithms remain valuable, however our Group‐in‐a‐Box meta‐layouts add a fresh strategy for presenting community membership, internal structure and inter‐cluster relationships. This paper extends the basic Group‐in‐a‐Box meta‐layout, which uses a Treemap substrate of rectangular regions whose size is proportional to community size. When there are numerous inter‐community relationships, the proposed extensions help users view them more clearly: (1) the Croissant–Doughnut meta‐layout applies empirically determined rules for box arrangement to improve space utilization while still showing inter‐community relationships, and (2) the Force‐Directed layout arranges community boxes based on their aggregate ties at the cost of additional space. Our free and open source reference implementation in NodeXL includes heuristics to choose what we have found to be the preferable Group‐in‐a‐Box meta‐layout to show networks with varying numbers or sizes of communities. Case study examples, a pilot comparative user preference study (nine participants), and a readability measure‐based evaluation of 309 Twitter networks demonstrate the utility of the proposed meta‐layouts.