Hierarchical Community Detection Based on Partial Matrix Convergence Using Random Walks

Random walks are a standard tool for modeling the spreading process in social and biological systems.But in the face of large-scale networks,to achieve convergence,iterative calculation of the transition matrix in random walk methods consumes a lot of time.In this paper,we propose a three-stage hierarchical community detection algorithm based on Partial Matrix Approximation Convergence(PMAC) using random walks.First,this algorithm identifies the initial core nodes in a network by classical measurement and then utilizes the error function of the partial transition matrix convergence of the core nodes to determine the number of random walks steps.As such,the PMAC of the core nodes replaces the final convergence of all the nodes in the whole matrix.Finally,based on the approximation convergence transition matrix,we cluster the communities around core nodes and use a closeness index to merge two communities.By recursively repeating the process,a dendrogram of the communities is eventually constructed.We validated the performance of the PMAC by comparing its results with those of two representative methods for three real-world networks with different scales.     

Fuzzy Views on Black-Litterman Portfolio Selection Model

In this paper, views of investor are described in fuzzy sets, and two fuzzy Black-Litterman models are constructed with fuzzy views and fuzzy random views respectively. In the models, expected returns and uncertainty matrix of views are redefined and the views are formulated by fuzzy approaches suitably. Then the models are tested with data from Chinese financial markets. Empirical results show that the fuzzy random views model performs the best, and both the fuzzy models are better than the traditional ones, demonstrating that the fuzzy approaches can contain more information in the views and measure the uncertainty more correctly.     

人类卫生健康共同体视域下的全球科技合作研究——以新冠肺炎疫情为背景

新冠疫情暴发以来,中国政府和人民秉持人类命运共同体理念,运用一系列先进技术,在较短时间内扭转了国内疫情的严峻形势.在此背景下,为了更好地发挥中国方案在全球抗疫中的作用,文章运用内容分析法和归纳分析法,首先梳理了从人类命运共同体到人类卫生健康共同体的理论逻辑,其次探究了人类卫生健康共同体、全球科技合作与疫情防控之间的结构关系,最后分析了中国疫情防控的有效举措,进而得出结论:中国疫情防控工作的成效离不开人类命运共同体理念的指引和全球科技合作的开展,而中国在平台建设、机制整合及合作网络等领域的方案共享将有助于遏制全球疫情的蔓延.     

Aristotle Said “Happiness is a State of Activity” — Predicting Mood Through Body Sensing with Smartwatches

We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people’s geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies.     

页岩气低伤害超临界CO2凝胶压裂液研究

针对常规水压裂液会对页岩造成伤害，容易产生水锁，不易返排，还造成水资源消耗和污染环境等问题，制备了低伤害二氧化碳凝胶压裂液。将自备的F2EU和F4EU增稠剂加入到超临界CO₂基液中，探究两种增稠剂的加入量对CO₂凝胶压裂液黏度的影响，综合考虑成本与效果，优选了2%的F4EU增稠剂，可将CO₂的黏度增至15.4 mPa·s。研究了温度、压力以及剪切速率对凝胶压裂液黏度的影响。实验结果表明，随着温度升高黏度总体降低，但中间出现一个短暂升高阶段；随着压力上升压裂液黏度增加；随着剪切速率的增加压裂液黏度下降，属于一种典型的剪切变稀的假塑性流体。F4EU超临界CO₂凝胶压裂液的平均伤害率为1.39%，远远小于常规压裂液对岩芯的伤害率。实验表明，F4EU超临界CO₂凝胶压裂液在页岩气压裂开采中具有良好的应用前景。     

Early SIV and HIV infection promotes the LILRB2/MHC-I inhibitory axis in cDCs

Classical dendritic cells (cDCs) play a pivotal role in the early events that tip the immune response toward persistence or viral control. In vitro studies indicate that HIV infection induces the dysregulation of cDCs through binding of the LILRB2 inhibitory receptor to its MHC-I ligands and the strength of this interaction was proposed to drive disease progression. However, the dynamics of the LILRB2/MHC-I inhibitory axis in cDCs during early immune responses against HIV are yet unknown. Here, we show that early HIV-1 infection induces a strong and simultaneous increase of LILRB2 and MHC-I expression on the surface of blood cDCs. We further characterized the early dynamics of LILRB2 and MHC-I expression by showing that SIVmac251 infection of macaques promotes coordinated up-regulation of LILRB2 and MHC-I on cDCs and monocytes/macrophages, from blood and lymph nodes. Orientation towards the LILRB2/MHC-I inhibitory axis starts from the first days of infection and is transiently induced in the entire cDC population in acute phase. Analysis of the factors involved indicates that HIV-1 replication, TLR7/8 triggering, and treatment by IL-10 or type I IFNs increase LILRB2 expression. Finally, enhancement of the LILRB2/MHC-I inhibitory axis is specific to HIV-1 and SIVmac251 infections, as expression of LILRB2 on cDCs decreased in naturally controlled chikungunya virus infection of macaques. Altogether, our data reveal a unique up-regulation of LILRB2 and its MHC-I ligands on cDCs in the early phase of SIV/HIV infection, which may account for immune dysregulation at a critical stage of the anti-viral response.     

基于小波分析对地面电场信号的去噪及闪电信息提取

基于小波分析方法,通过对7种小波函数在误差控制和阈值方法选取上的对比,参考信噪比及均方误差等计算结果,选取了较为合理的Rigrsure阈值分析方法和sym5小波函数作为地面大气电场信号的去噪处理方法.该方法不仅能够较好地对电场信号波形平滑处理,降低电场波形中噪声信号的叠加度,还可保留闪电引起的电场的快速变化.在此基础上,利用差分计算方法实现了对闪电信号的自动识别,引入了总闪电频数误差及分时段闪电频数与人工分析结果的相关系数等2个参考量来选取差分计算方法中电场差分阈值,通过该方法可以实现闪电频数和极性等有关信息的提取.     

EVOKE: A Value-Driven Concept Selection Method for Early System Design

The development of new technologically advanced products requires the contribution from a range of skills and disciplines, which are often difficult to find within a single company or organization. Requirements establishment practices in Systems Engineering (SE), while ensuring coordination of activities and tasks across the supply network, fall short when it comes to facilitate knowledge sharing and negotiation during early system design. Empirical observations show that when system-level requirements are not available or not mature enough, engineers dealing with the development of long lead-time sub-systems tend to target local optima, rather than opening up the design space. This phenomenon causes design teams to generate solutions that do not embody the best possible configuration for the overall system. The aim of this paper is to show how methodologies for value-driven design may address this issue, facilitating early stage design iterations and the resolution of early stage design trade-offs. The paper describes how such methodologies may help gathering and dispatching relevant knowledge about the ‘design intent’ of a system to the cross-functional engineering teams, so to facilitate a more concurrent process for requirement elicitation in SE. The paper also describes EVOKE (Early Value Oriented design exploration with KnowledgE maturity), a concept selection method that allows benchmarking design options at sub-system level on the base of value-related information communicated by the system integrators. The use of EVOKE is exemplified in an industrial case study related to the design of an aero-engine component. EVOKE’s ability to raise awareness on the value contribution of early stage design concepts in the SE process has been further verified with industrial practitioners in ad-hoc design episodes.