Beyond Boundary Objects: Collaborative Reuse in Aircraft Technical Support

Boundary objects are a critical, but understudied, theoretical construct in CSCW. Through a field study of aircraft technical support, we examined the role of boundary objects in the practical achievement of safety by service engineers. Their resolution of repair requests was preserved in the organization’s memory via three compound boundary objects. These crystallizations did not manifest a static interpretation, but instead were continually reinterpreted in light of meta-negotiations. This suggests design implications for organizational memory systems which can more fluidly represent the meta-negotiations surrounding boundary objects.     

冶金炉渣氮化物容量研究的进展

本文总结了近年冶金炉渣氮酸盐容量的研究方法，氮在炉渣中存在形态与炉渣氧分压的关系，炉渣氮化物容量与温度及炉渣成分的关系，并给出设计钢液脱氮渣系的基本原则。     

TMS320 F206定点DSP芯片开发实践

以TMS320F206为例,着重探讨了DSP系统开发过程中的硬件设计与调试、软件设计中的流水线冲突、等待状态设置以及如何利用闪速存储器等相关问题.     

实行全面质量管理 做好江口水电站工程设计

江口水电站是芙蓉江干流开发中的骨干工程。为认真履行江口工程的设计合同，确保优质、高效地完成江口工程的各项设计工作，长江委设计院实行全面质量管理，建立了一套规范化的统计管理程序，制订详细的设计计划，落实江口工程项目各级责任人的质量责任制，采取特殊技术措施，对工程设计过程中的各项质量活动进行全过程的管理，使江口工程在满足质量标准、合同进度的情况下，工程投资最省。     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

离散Hopfield神经网络和联想记忆的开关电流技术实现

研究了离散Ｈｏｐｆｉｅｌｄ神经网络（ＤＨＮＮ）和联想记忆神经网络的开关电流技术实现，利用多权输入跨导，开关电流延迟器（ＳＩＤ）和可编程电流比较器（ＰＣＣ）实现了离散Ｈｏｐｉｅｌｄ神经网络，并提出了利用离散Ｈｏｐｆｉｅｌｄ神经网络实现自联想记忆时相应的开关电流电路，所提出了开关电流神经网络适宜于超大规模集成，能在低电压（如３．３Ｖ）下工作。     

对洪水分级的浅见

洪水的等级划分应考虑流量、水位、防洪能力及灾害等因素,综合这些因素把洪水分成5个等级。1、2级洪水,洪水流量小,水位低,基本无灾;3级洪水,洪水流量较大或水位较高,有一定灾害;4级洪水,为大洪水或高水位,有较大灾害;5级洪水,为特大洪水或高水位,灾情严重。     

Drugs, memory, and metamemory: A dose-effect study with lorazepam and scopolamine.

This experiment was designed to use the graded dose-related amnesia produced by the benzodiazepine lorazepam (1.0, 2.0 mg/70 kg, oral) and the anticholinergic scopolamine (0.3, 0.6 mg/70 kg, subcutaneous) as a tool to explore the cognitive and neurochemical mechanisms underlying metamemory in the judgment of learning paradigm, with a placebo-controlled independent groups design in healthy volunteers (n=12/group). Results provide evidence for a pharmacological dissociation between effects on memory versus metamemory (relative accuracy of item-by-item monitoring) across a range of levels of memory performance and suggest that the drugs selectively impair those aspects of metamnemonic monitoring that require participants' awareness of their overall current state of functioning (absolute accuracy of prospective item-by-item monitoring, prospective global monitoring) but not those that rely solely on assessment of individual item characteristics (relative accuracy of item-by-item monitoring). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

考虑初始椭圆度的套管抗载能力分析

针对套管的不同初始椭圆度建立了套管、水泥环、地层平面应变力学模型。分析认为，在非均匀地应力作用下，套管内壁的径向变形较大，抗载能力明显降低，破坏主要是由地下非均匀地应力引起的；随着初始椭圆度的增加，在地应力作用下套管各部位产生的应力分布不均程度增大，使得套管抗外载能力减弱。建议在生产和使用过程中应严格将套管椭圆度控制在规定的范围内，从而提高套管的抗载能力。     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.