Corporate smart phones: professionals' conscious engagement in escalating work connectivity

This article explores how the adoption of company sponsored smart phones inflicts upon the lives of professionals. Drawing upon qualitative interviews at a law firm in Brazil, the experiences of new smart phone users are reported upon in detail. Increased accessibility, accuracy and speed in exchanges gave the users a sense of autonomy and flexibility. However, the technology also helped to intensify the organisation's hold on employees outside of regular working hours, reaching into new settings, time slots and social contexts. Employees expressed concerns regarding demands from superiors that negatively affected their private spheres, yet many of them paradoxically requested more efficient smart phone connectivity. The article focuses on the justifications, the different narrative strategies, employed by professionals for their conscious engagement in escalating work connectivity. It is suggested that these justifications display users' attempt to ‘dis‐identify’ with the role and practice they perform.     

基于HHT及主成分分析的光缆识别信号特征提取

为了解决当环境噪声较严重时光强难以维持稳定,容易误判目标光缆问题,根据干涉信号非线性、非平稳的时域特征,提出了一种希尔伯特-黄变换及主成分分析相结合的信号处理方法。探测器接收的干涉信号对进行经验模态分解,把分解得到的本征模态分量作为列向量构建主成分分析的样本矩阵,以奇异值的贡献率进行有效成分筛选;然后采用主成分分析算法完成降噪优化。最后将降噪后的信号进行希尔伯特-黄变换得到瞬时特征信号,完成目标光缆识别。结果表明,相同噪声环境下所提方法可有效提高回波干涉信号的信噪比。因此,研究结果可以降低误判目标光缆的概率,帮助工程人员更有效地完成光缆识别。     

基于数字图像处理技术的再生混凝土数值模拟

为了研究骨料形态对再生混凝土材料力学性能的影响,基于势能原理的基面力元法,利用图像处理技术和MATLAB软件,建立了再生混凝土真实骨料细观模型。应用FORTRAN分析软件对再生混凝土试件进行单轴拉伸试验的数值模拟,研究了真实骨料细观模型再生混凝土宏观应力-应变曲线变化规律和试件的破坏形态。结果显示,真实骨料模型得到的宏观应力-应变曲线和试验曲线吻合度较高,骨料形状对试件的破坏形态影响较小。模型可真实地模拟再生混凝土在单轴拉伸作用下的损伤破坏过程,准确地反映试件的实际骨料形态。     

基于模态应变能密度理论的网架结构损伤识别方法

为了研究网架结构的损伤检测方法,根据应变能密度理论,提出采用单元模态应变能密度差值作为网架结构损伤识别指标的方法,以损伤单元模态应变能密度差值的大小初步确定单元的损伤程度。分析了5种具有代表性的损伤工况,并在数值计算结果中引入了白噪声。结果显示,在一阶模态下,针对单损伤、多损伤和轻微损伤、严重损伤等不同损伤工况,损伤杆件的模态应变能密度差值均为高值。因此,该方法可以有效识别出网架结构的损伤位置,根据损伤单元的模态应变能密度差值大小初步确定单元的损伤程度,且在一定的噪声水平下具有较强的鲁棒性,对网架结构损伤识别具有参考价值。     

银行跨国并购中的文化差异识别体系研究

在跨国并购中,并购银行将面临民族文化和企业文化双重差异和冲突带来的挑战,而且缺乏对文化差异的理解和管理是并购失败的首要原因。在并购实施中,我们有必要把文化整合与业务技术整合看成截然不同但又紧密相关的两个领域,而构建文化差异识别体系是文化整合的基础。本文从感知跨国并购中的文化差异冲突和融合入手,提出构建文化差异识别体系应以整合战略执行为导向,突出促成行为改变的关键要素;提出了涵盖人本文化、处事文化、沟通文化等角度,共7个维度、22种偏好的文化差异识别体系的基础框架。     

模糊综合评价法在工程供应链风险管理中的应用

风险管理是工程项目管理的重要内容,是维持工程供应链稳定运作的重要保证。以工程供应链为研究视角,分析项目全寿命周期管理的四个主要周期阶段与工程供应链的相互关联。再对其供应链各环节中的风险进行识别,通过运用模糊数学的相关理论,对各阶段识别出的六大风险进行模糊综合评价,找出工程供应链风险特征,并提出相应的应对策略。     

百口泉71井区储层保护技术研究应用

通过室内实验研究,明确储层特征及敏感性情况,系统分析了储层损害机理,并结合超前注水,研究制定出先期防膨、早期分注与控制注水强度、保持注采平衡对策措施,形成了先期分层、梯度式防膨与后期注水过程中段塞脉冲式防膨相结合的工艺技术,确定了合理的注入压力和注入量等参数。投入现场实施后效果显著,百71井区白碱滩组油藏注入压力保持稳定,吸水状况良好,动用程度良好。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

地下洞室群围岩稳定性的三维损伤本构模型有限元研究

传统的弹塑性模型与工程实际存在较大差距,利用损伤模型进行围岩稳定性三维分析为地下洞室围岩稳定性分析研究提供了一种新的思路,具有较为重要的理论意义和工程实用价值。     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.