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.     

高温作用后砂岩物理力学性质阈值温度的确定——基于机器学习算法

通过采用机器学习算法，提出了高温作用后砂岩物理力学性质变化的阈值温度的确定方法。基于Python语言，利用K-Means、SVM算法，实现了对样本数据的分类和聚类，确定了阈值温度区间，验证了二分类法的合理性和准确性.结果表明，砂岩样本的阈值温度区间为400~600 ℃，阈值温度上下砂岩的物理力学性质存在显著差异，体现为阈值温度区间以下的岩样物理力学性质较为分散，而阈值温度区间以上的岩样物理力学性质较为集中.     

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.     

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.     

Notch Effect and Frequency Compensation of Dual-Sensor OBC Data in Shallow Water

Reverberation is significant in shallow water and produces obvious notches in OBC spec-tra. It also degrades the quality of sections and increases the difficulty of processing and interpretation. This ...     

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.     

The expansion of GPCR transactivation-dependent signalling to include serine/threonine kinase receptors represents a new cell signalling frontier

G protein-coupled receptor (GPCR) signalling is mediated through transactivation-independent signalling pathways or the transactivation of protein tyrosine kinase receptors and the recently reported activation of the serine/threonine kinase receptors, most notably the transforming growth factor-β receptor family. Since the original observation of GPCR transactivation of protein tyrosine kinase receptors, there has been considerable work on the mechanism of transactivation and several pathways are prominent. These pathways include the “triple membrane bypass” pathway and the generation of reactive oxygen species. The recent recognition of GPCR transactivation of serine/threonine kinase receptors enormously broadens the GPCR signalling paradigm. It may be predicted that the transactivation of serine/threonine kinase receptors would have mechanistic similarities with transactivation of tyrosine kinase pathways; however, initial studies suggest that these two transactivation pathways are mechanistically distinct. Important questions are the relative importance of tyrosine and serine/threonine transactivation pathways, the contribution of transactivation to overall GPCR signalling, mechanisms of transactivation and the range of cell types in which this phenomenon occurs. The ultimate significance of transactivation-dependent signalling remains to be defined but it appears to be prominent and if so will represent a new cell signalling frontier.     

基于多变量希尔伯特频域模型的癫痫发作预测

癫痫发作具有突发性和反复性,对患者生命安全构成巨大威胁.为了对癫痫发作进行有效地预测,提出了多变量希尔伯特频域模型的癫痫发作预测方法.将希尔伯特边际谱、希尔伯特边际谱的变化方向和希尔伯特加权频率组成一个三维特征向量作为多变量希尔伯特频域模型,输入到支持相量机中,实现癫痫的发作预测,最后采用癫痫发作预测特征方法对预测结果进行评估.实验结果表明:采用多变量希尔伯特频域模型分析方法预测δ波和θ波的癫痫发作,癫痫预测范围在30~45 min,患者有足够的时间采取措施应对;癫痫发作周期在5~10 min,缩短患者等待时间,降低焦虑程度;与多种相关方法进行比较,该方法具有较低的错误预报率和较高的预测敏感度.