Solesolevaki as social capital: a tale of a village,two tribes,and a resort in Fiji

This paper advances understanding of the complex and adaptive nature of indigenous Fijian communities involved in tourism. It examines how tourism-related development has set the people of one Fijian village along two separate development pathways, and explores how preferential access to tourism benefits has created disparities within the community. Complex Adaptive Systems theory and Social Capital theory are used to conceptualise how over 40 years of tourism involvement has influenced development within an indigenous Fijian community. The findings argue that indigenous Fijian communities are non-homogeneous entities, which are constantly in transition, responding and adapting integratively to both internal and external changes over time. The findings show that the emergence of new behaviours and ways of life has led to the collapse of the pre-existing systems of social capital. As a response, community members retreated and regrouped, strengthening internal bonds and social capital in their smaller social units, leading to both dependency and opportunity-seeking behaviours among participants. Ultimately, the paper asserts that money alone does not lead to development, but rather tourism and access to a variety of capital do.     

Hedging and pricing early-exercise options with complex fourier series expansion

We introduce a new numerical method called the complex Fourier series (CFS) method proposed by Chan (2017) to price options with an early-exercise feature—American, Bermudan and discretely monitored barrier options—under exponential Lévy asset dynamics. This new method allows us to quickly and accurately compute the values of early-exercise options and their Greeks. We also provide an error analysis to demonstrate that, in many cases, we can achieve an exponential convergence rate in the pricing method as long as we choose the correct truncated computational interval. Our numerical analysis indicates that the CFS method is computationally more comparable or favourable than the methods currently available. Finally, the superiority of the CFS method is illustrated with real financial data by considering Standard & Poor’s depositary receipts (SPDR) exchange-traded fund (ETF) on the S&P 500® index options, which are American options traded from November 2017 to February 2018 and from 30 January 2019 to 21 June 2019.     

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.     

区域贫困程度测度研究:甘肃案例

区域贫困程度测度是精准施策,打赢脱贫攻坚战的重要依据。在对已有贫困程度测度方法回顾的基础上,文章通过分析深度贫困地区的贫困特征,提出区域贫困程度测度原则,构建区域贫困程度测度指标体系,并以甘肃为例对深度贫困县贫困程度进行测算,进一步了解深度贫困县的贫困现状、集中区域、影响因素及贫困差距等,以期为地方打赢脱贫攻坚战提供借鉴。     

Employee performance,well‐being,and differential effects of human resource management subdimensions: Mutual gains or conflicting outcomes?

The human resource management (HRM) literature supports the idea that coherent systems of HRM practices can induce attitudinal effects when perceived subjectively by employees. Recently, scholars have proposed that subdimensions of HRM systems exist and account for variance in outcomes. This study explores differential effects of three subdimensions of HRM systems (skill‐, motivation‐, and opportunity‐enhancing HRM practices) on employee innovative behaviours and well‐being. Our predictions are based on the mutual gains perspective, which specifies positive relationships between HRM practices and employee performance, and the conflicting outcomes perspective that links HRM practices to higher job demands and stress. Using data from the Finnish 2012 Practices of Working Life Survey, we find support for both the mutual gains and conflicting outcomes perspectives; however, we also show that the effects of the subsets of HRM practices are heterogeneous.     

强化食品抽样检验推动食品安全深度监管

做好食品抽样检验工作是确保食品质量和安全的关键,本研究对目前国内食品抽样检验管理现状进行了分析,针对食品安全抽样检验管理存在的问题提出要强化制度建设、强化技术团队建设、制定统一检测标准等方法,并提出进一步深化改革食品安全抽样检验管理办法,旨在保障公众食品安全,提高食品抽样监管力度。     

“With a young spirit,we will be young forever”: Exploring the links between tourism and ageing well in contemporary China

This research explores the entangled relationships between tourism, ageing and well-being in later life. We challenge the universal connotations of old age and “ageing well” and see their relationships with tourism as dynamic processes, in which older people are continuously producing different meanings and fulfilling personal quests over life courses. Using the contextualist and temporal lens, four scenarios of the evolving relationships between tourism and ageing well were identified from the lived experiences and travel histories of 48 Chinese senior travelers. The results demonstrate that how tourism affects ageing well are shaped by senior travellers' early life and travel experiences, the personalised conceptions attached to tourism and well-being, which are indeed complex responses to their personal, social and cultural contexts. This research unveils vivid pictures of Chinese older adults’ leisure lives, and highlights the urgency to develop relevant tourism policies and marketing strategies to cater for their changing needs.     

基于MBP算法和深度学习的人脸识别

为了解决在深度学习提取人脸图像特征时,易忽略其局部结构特征和缺乏对其旋转不变性学习的问题,提出了一种基于单演局部二值模式(MBP)与深度学习相结合的高效率人脸识别方法。首先,用多尺度单演滤波器对图像进行滤波,得到幅值和方向信息;其次,用LBP算法和象限比特的方法进行编码,分块计算组合其直方图特征;然后,将提取的单演特征作为深度信念网络(DBN)的输入,逐层训练优化网络参数,得到优异的网络模型;最后,将训练好的DBN网络在ORL人脸数据库上进行人脸识别实验,进行识别率计算,其识别率为98.75%。所提出的方法使用无监督的贪婪算法,隐藏层设定为2层,使用反向传播算法优化网络。相较于已知的人脸识别方法,MBP+DBN算法对光照、表情和部分遮挡变化具有较好的鲁棒性,在人脸识别中识别率较高,具有一定的优势,为图像特征提供了一种新的识别方法。     

Neighbors' Income,Public Goods,and Well‐Being

How does neighbors' income affect individual well‐being? Our analysis is based on rich U.S. local data from the Behavioral Risk Factor Surveillance System, which contains information on where respondents live and their self‐reported well‐being. We find that the effect of neighbors' income on individuals' self‐reported well‐being varies with the size of the neighborhood included. In smaller areas such as ZIP codes, we find a positive relationship between median income and individuals' life satisfaction, whereas it is the opposite at the county, MSA, and state levels. We provide evidence that local public goods and local area characteristics such as unemployment, criminality, and poverty rates drive the association between satisfaction and neighbors' income at the ZIP code level. The neighbors' income effects are mainly concentrated among poorer individuals and are as large as one quarter of the effect of own income on self‐reported well‐being.     

基于遥测信号的测距技术及其发展趋势

基于遥测信号的测距技术可以有效解决深空测距下行链路同时传输遥测、测距信号时出现的遥测数据传输速率受限、频谱带宽增加等问题，应用前景广阔。介绍了基于遥测信号的测距技术发展历程，重点总结了其中的关键技术，包括上行测距码的选择、遥测帧的设计和传输、PN码相位估计算法，最后探讨了其发展趋势。基于遥测信号的测距技术适应未来深空测距的发展需求，是深空测距的重要发展方向之一。