中外CBD研究分析与比较

从文献统计和综述的角度入手,通过样本数据特征和关注度图谱分析相结合的方法,对CBD的研究阶段进行了划分,对国内外关于CBD研究的特征、内涵、演变规律进行了研究.认为国外CBD研究的演变过程和动力机制与国内存在着较大差异,研究内容出现了同构性和异质性并存的特征,故须理性对待我国CBD的研究与实践探索.     

Multi-disciplinary collaborative building design—A comparative study between multi-agent systems and multi-disciplinary optimisation approaches

The importance of collaboration between different design disciplines has been widely recognised by the building industry. Traditionally this is achieved through physical meetings between the representatives of different design groups. However, this is facing more challenges when dealing with large and complex design problems. Various approaches have been introduced to tackle this problem, such as multi-agent systems (MAS) and the multi-disciplinary design optimisation (MDO) approaches. The former represents an advanced, ICT-based framework which facilitates collaborative design through communication, data and knowledge sharing and negotiation while the latter represents a theoretical modelling approach which facilitates collaborative design through a thorough analysis of the technical problems. By comparing the strengths and weaknesses of these approaches in facilitating collaborative design, this paper provides a roadmap study for the development of collaborative building design.     

How Modifying Third‐Party Information Affects Interpersonal Impressions and the Evaluation of Collaborative Online Media

Previous research has drawn upon warranting theory to help explain how viewers evaluate people and entities online. Extending previous research, this study assesses how the ability of a target to modify third‐party information affects perceptions of warranting value, and in turn, interpersonal impressions and the perceived legitimacy of online media that host evaluations. Additionally, this work explores how the perceived objectivity of a third‐party evaluator affects impressions in online settings. The results provide support for warranting theory and help clarify how impressions are formed in online environments when people have the ability to generate and modify content collectively. The theoretical implications this study has for warranting theory and future research directions are discussed.     

Beyond Cognitive Framing Processes: Anger Mediates the Effects of Responsibility Framing on the Preference for Punitive Measures

A new stream of research indicates that framing effects are based on emotional as well as cognitive processes. However, it is not entirely clear whether emotions mediate framing effects and what the moderators of emotional mediation processes are. To address these questions, we conducted an experiment in which the framing of responsibility for a social problem was manipulated (ambivalent vs. high‐responsibility frame). We find that the high‐responsibility frame increased the preference for punitive measures by increasing responsibility beliefs and eliciting anger. Furthermore, we find that trait anger moderates the framing effect on anger and that responsibility beliefs are positively associated with anger intensity. The significance of these findings for framing research and suggestions for future studies are discussed.     

业务系统网络访问质量用户感知关键指标探索

在互联网经济时代,人们对使用的网络质量要求越来越高,用户体验将提升为网络建设者的核心目标;网络业务的规划与优化,都要以用户体验为中心,用户体验将是数据业务的核心。让网络运维管理者清楚的知道自己的网络质量的好坏,让管理人员不再视"宽带网络"为一个黑箱,摆脱疲于应对客户投诉的被动局面。     

Integration of Production Steps on a Single Equipment

The article describes a new method for integrated processing which combines high-energy heating by high frequency currents (HEH HFC) and abrasive grinding. Research clearly showed that implementation of integrated processing may lead to 2 … 2.5-fold increasing of the processing performance. It was established experimentally that the proposed integrated treatment results in increasing of the microhardness and the level of residual compressive stresses in the surface layer. This increases the contact–fatigue strength of steel parts up to 10% … 16%.     

On the analysis of fracture mechanisms and mechanical behavior of AA5083-based tri-modal composites reinforced with 5 wt.%B4C and toughened by AA5083 and AA2024 coarse grain phases

In this article, the influence of AA2024 and AA5083 coarse grains on mechanical properties and failure mechanisms of AA5083-5wt. %B₄C tri-modal composite has been discussed. AA2024 and AA5083 powders (<100 µm) were added to mechanically milled AA5083-5 wt.%B₄C powders in 25 and 50 wt.% and the mixtures were consolidated using the hot press and hot extrusion techniques. Results indicated that by adding AA2024 and AA5083 powders as coarse grains, hardness and tensile strength of AA5083-5 wt.%B₄C composite decreased but ductility increased. Moreover, by adding AA2024 powders as coarse grains, fracture mode changed and cracks tended to grow through along AA2024/AA5083-5 wt.%B₄C interface rather than being arrested or deflected. It seemed that dislocation mobility and the interface between coarse grains and ultra-fine grains had the main role in determining the mechanical properties and failure mechanisms in tri-modal AA5083-B₄C composites.     

A collaborative contour detector by gradient and active contours for ultrasound kidney images

In the process of urinary system disease diagnosis, a complete kidney contour is crucial to estimate its size, area, volume and other properties. These properties can effectively help doctors diagnosis and prepare treatment plans. However, ultrasound images suffer from low signal-to-noise ratio, speckle, missing boundaries and other artefacts. Traditional contour detection algorithms can hardly extract a continuous and accurate kidney contour. To solve the problem, we propose a collaborative contour detector by gradient and active contour. It not only can make sure that the extracted contour is continuous and accurate but also is simple and suitable to use in practice. Both the simulated experiments and clinical experiments show that the proposed algorithm achieves a good performance in ultrasound kidney images and can effectively assist doctors in diagnosis.     

基于深度神经网络和加权隐反馈的个性化推荐

改进的矩阵分解(SVD++)将用户和物品特征向量的内积作为用户对物品的评分,而内积无法捕捉用户与物品之间复杂的高阶非线性关系.此外,SVD++在融入用户隐式反馈时,未区分不同交互物品对于用户特征表达的贡献.针对上述问题,文中提出基于深度神经网络和加权隐反馈的推荐算法(DeepNASVD++),采用深度神经网络建模用户与物品之间的关系,使用注意力机制计算历史交互物品在建模用户隐式反馈时的权重.在公开数据集上的实验验证文中算法的有效性.     

基于Flexsim模型的煤炭主生产物流系统仿真与优化

本文以淮南某煤矿煤炭主生产物流系统作为研究对象并收集相关数据,为找出主生产物流系统生产瓶颈,提高设备工作效率和相互匹配程度,建立了基于主生产物流系统的TTPN模型和Flexsim仿真模型。结果显示:在生产过程中主井提升设备提升能力不足是生产物流系统的瓶颈,根据仿真结果提出系统优化方案。优化后210103工作面产量提高了1 494t;主要运输设备的停机率降低,利用率达到90%以上;煤仓空仓率大幅度降低,缓冲煤仓充分起到缓冲作用;主井提升设备运煤量增加1 485t,设备之间的契合程度明显提高。