广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中，最大似然（ML）检测可以取得最优的检测性能，然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题，提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引，接着对候选天线索引按正则化标准进行可靠性验证，剔除错误索引，缩小信号的搜索空间，最后通过求解最小二乘问题估计信号。仿真结果表明，与经典的正交匹配追踪（OMP）算法相比，所提算法以少许复杂度的增加为代价极大提升了检测性能，能够在检测性能与复杂度之间取得更好的折中。     

利用信道状态信息的室内定位技术

多径效应导致基于信号接收强度（RSSI）的室内定位方法精度不高，采用更细粒度的物理层信道状态信息（CSI）可以区分不同路径，提高定位精度。在已有基于CSI室内定位方法的基础上，通过改进对数距离路径损耗模型，得到CSI与传输距离的关系，并结合目标位置所测得的CSI值回归出目标与发射端的距离，最后通过三边定位法预测出目标的位置坐标。实验表明，相比基于RSSI的定位方法以及已有的基于CSI的定位方法，所提方法2 m以内的误差概率提高了将近40%和20%，有效提高了定位精度。     

福鼎市桐江溪卤乙酸分布特征及潮汐影响分析

为了掌握福鼎市桐江溪卤乙酸(HAAs)的含量、时空分布规律及其来源,对水体中HAAs化合物进行取样调查。根据桐江溪水文情势及沿岸特点设置10个取样点,于2017年12月及2018年7月按照涨潮、退潮情况分别对水样进行采集,检测HAAs分布情况,同时将卤乙酸(HAAs)与水质特性、涨退潮进行了相关性分析。研究发现,HAAs是普遍存在于桐江溪中的污染物质。其中,一氯乙酸(MCAA)、二氯乙酸(DCAA)、三氯乙酸(TCAA)、一溴乙酸(MBAA)、二溴乙酸(DBAA)等5种卤乙酸(HAA_5)质量浓度为0.44～3.39μg/L;一氯乙酸(MCAA)、二氯乙酸(DCAA)、三氯乙酸(TCAA)、一溴乙酸(MBAA)、二溴乙酸(DBAA)、三溴乙酸(TBAA)、一溴一氯乙酸(BCAA)、一溴二氯乙酸(BDCAA)、二溴一氯乙酸(CBDAA)等9种卤乙酸(HAA_9)质量浓度为0.83～56.15μg/L。桐江溪中HAAs主要为DCAA,TCAA,TBAA 3种,其中DCAA为制药厂及医院排水导致,主要分布在下游;TCAA主要来源于河段上游的农业生产活动;TBAA为上游沸石矿尾矿库受雨水冲刷而流入的Br~-所生成,主要分布于河流中下段。相关性分析结果表明,温度与HAA_5,HAA_9质量浓度呈现正相关,pH值与HAA_5,HAA_9质量浓度呈现负相关;TCAA质量浓度于相同季节不同潮汐及不同季节相同潮汐时均呈现上游高于下游的现象,其他HAAs均不因涨潮、退潮的差异造成质量浓度分布的变化。掌握桐江溪HAAs的含量、时空分布规律及来源,探讨其与水质的相关性,对于净水工艺选择、水厂出水HAAs含量的溯源分析、水体环境风险评估以及研究水生生物体内HAAs的累积效应等有重要意义。     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

What determines international tourist arrivals in India?

Using data for 18 major tourist originating countries to India from 2001 to 2015, this study examines the major determinants of international tourist arrivals in India. The results indicate that past experiences of the tourists, per capita income in the tourist originating country, relative costs of living between India and the country of origin, and the level of infrastructure development in India are key determinants of international tourist arrivals in India. Furthermore, both transportation and communication infrastructure are important in attracting tourists to India. In particular, evidence suggests that availability of road and air network and telephone connections has favourable impacts on international tourist arrivals in India. These results are robust to the inclusion of additional variables. These results have important policy implications.     

Prioritising alternatives for sustainable end-of-life vehicle disassembly in China using AHP methodology

The projection on Chinese increasing end-of-life vehicle (ELV) volumes indicates that the volume in 2020 will be about threefold that in 2015. The issue of scrapping vehicle upsurge relative to capacity crunches and environmental impacts perplexes Chinese dismantlers and it is intractable and urgent to choose an appropriate dismantling mode. The purpose of this study is to prioritise four potential dismantling modes and provide decision-making reference for dismantlers with a view to such criteria as environmentally sustainable considering constraints involving economy, technique, ecology and flexibility over changing condition. The conducted evaluation by the analytical hierarchy process (AHP) methodology takes sensitive and problematic aspects into account through questionnaires. The whole evaluation process supported by expert preferences, provides a simple and intuitive knowledge to construct arguments for ELV decision-making process. Results show that disassembly line involves the highest global weight of 0.363 and is concluded to be the best compromised ecological alternative.     

Innovation in the bioeconomy – dynamics of biorefinery innovation networks

The bioeconomy has become a central concept in many strategies for future economic development, emphasising an increasing need for collaboration across industries and sectors for innovation. This paper unpacks aspects of collaboration in the bioeconomy by looking at the development of innovation networks for biorefinery technologies from 2004 to 2014 based on innovation project data from Swedish public funding agencies using a stochastic actor-oriented model for network analysis. The analysis shows that although the network grew significantly during the time period, indicating an increasing interest in biorefinery technology innovation, inter-sectoral collaboration is not favoured over intra-sectoral collaboration. As is known from previous work on social networks trust-building is a key driver for collaboration, as actors tend to form collaborations with previous partners or indirectly connected partners, creating clustered networks.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Spatial movement patterns among intra-destinations using social network analysis

To explore popularly visited tourist locations, travel movement patterns, and movement points, this study collected samples of 321 Chinese tourists and 337 Japanese tourists who were visiting major tourist destinations in Seoul and its vicinity in South Korea. Results of analyzing movement patterns showed that Japanese tourists tend to be clustered around popular attractions, whereas Chinese tourists tend to spread over a larger area of attractions. Some specific shopping and amusement attractions were the locations most popularly visited by both groups. The start points and end points in the two groups’ itineraries were dissimilar overall, even though their patterns were similar in regard to major preferred tourist attractions. Thus, the findings of this study have the potential to contribute to understanding spatial mobility in a tourism destination through tracking tourists’ movement patterns.     

Do ongoing networks block out new friends? Reconciling the embeddedness constraint dilemma on new alliance partner addition

Research Summary: This study addresses a theoretical dilemma regarding how alliance network constraint (reflected by network cohesion) affects a firm’s alliance formation with new partners. Using a network pluralism approach, we separate a firm’s ego alliance network into two activity‐based networks—an exploratory network and an exploitative network—based on the primary value chain activity involved in each alliance. We argue that the cohesion of exploratory or exploitative networks has an inverted U‐shaped effect on the addition of new partners in the same activity‐based network, and a positive effect on the addition of new partners in the other network. Results based on data from the biotechnology industry largely support our predictions with one exception. Our study contributes to both scholarly understanding of network embeddedness and alliance practice. Managerial Summary: The structure of firms’ ongoing alliance networks may have paradoxical implications for their efforts to search for and form alliance with new partners. That is, when a firm’s alliance partners are tightly connected with each other, the cohesive network tends to both encourage and impede the focal firm to add new partners. We resolve this dilemma by showing that when a firm is deeply entrenched in a cohesive alliance network conducting a certain type of activities (e.g., R&D activities), it may not easily add new R&D alliance partners. However, it may still be able to escape from the cohesive R&D alliance network by seeking new partners conducting other activities (e.g., manufacturing activities).