The role of public space in constructing experience capital: A longitudinal analysis in the hotel context

Public space plays a primary role in shaping customers' hospitality experiences. Yet how public space conditions customers' experiential outcomes in accumulating capital for hospitality organizations remains underexplored. Inspired by the theory of psychological ownership, this research presents an in-depth analysis of the impacts of customers' public space experiences on their experiential outcomes using a longitudinal hotel industry dataset merging information from customer surveys, property performance, and surrounding accessibility insights. Findings revealed the positive effects of customers’ public space experiences on their overall service experiences, the perceived value of the experience, revisit intentions, and recommendation intentions. Moreover, hotel class, other customers, and surrounding accessibility were empirically verified as moderators conditioning the positive impact of public space. These findings offer valuable implications for theory and practice that are worthy of further exploration.     

空间目标的联合矩阵束极点估计

极点作为谐振区雷达目标的最主要电磁特性，具有很大的空间目标识别前景。但是在使用频域观测数据提取目标极点时，存在虚假极点和估计精度问题。针对上述问题，首先通过加窗和截断改进了后时响应信号的获取流程，抑制了虚假极点影响；然后采用最小描述长度法（MDL）估计极点个数，避免了定阶错误引起的虚假极点；最后采用联合矩阵束法估计极点，有效减少了单角度数据估计极点时存在的遗漏极点和虚假极点问题。FEKO软件验证表明，所提算法具有更高的精度和抗噪能力。     

Factor state–space models for high-dimensional realized covariance matrices of asset returns

We propose a dynamic factor state–space model for high-dimensional covariance matrices of asset returns. It makes use of observed risk factors and assumes that the latent integrated joint covariance matrix of the assets and the factors is observed through their realized covariance matrix with a Wishart measurement density. For the latent integrated covariance matrix of the assets we impose a strict factor structure allowing for dynamic variation in the covariance matrices of the factors and the residual components as well as in the factor loadings. This factor structure translates into a factorization of the Wishart measurement density which facilitates statistical inference based on simple Bayesian MCMC procedures making the approach scalable w.r.t. the number of assets. An empirical application to realized covariance matrices for 60 NYSE traded stocks using the Fama–French factors and sector-specific factors represented by Exchange Traded Funds (ETFs) shows that the model performs very well in- and out of sample.     

我国林业扶贫工作的主要问题及优化措施研究

林业扶贫是当前我国扶贫工作的重要内容。通过介绍开展林业扶贫工作的政策背景、理论背景以及经济背景,分析林业扶贫工作开展过程中存在的基础设施落后、针对性差等问题,提出了因地制宜,互联网+林业扶贫,鼓励社会资本进入扶贫工作、志智双扶等应对措施。     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Forecast of realized covariance matrix based on asymptotic distribution of the LU decomposition with an application for balancing minimum variance portfolio

We derive the asymptotic distribution for the LU decomposition, that is, the Cholesky decomposition, of realized covariance matrix. Distributional properties are combined with an existing generalized heterogeneous autoregressive (GHAR) method for forecasting realized covariance matrix, which will be referred to as a generalized HARQ (GHARQ) method. An out-of-sample forecast comparison of a real data set shows that the proposed GHARQ method outperforms other existing methods in terms of optimizing the variances of portfolios.     

机器类设备基于不同时延需求分组的随机接入方法

针对大规模机器类通信中拥塞导致的时延敏感设备时延高和接入成功率低的问题，提出将小区中设备按时延要求分组，对不同组设备引入不同的退避模型，分析时延敏感设备的时延和吞吐量，按照不同组中设备的时延需求动态分配前导数目，同时通过调整接入类限制因子实现吞吐量的优化。仿真结果表明，在给定时延敏感设备的时延限制条件时，与统一退避的机制对比，所提分组机制的时延敏感设备能够满足时延要求，并且提高了接入效率。     

基于底线预警的国土空间生态修复规划实施监督体系研究

国土空间生态修复是推进生态文明建设的重大举措，构建基于底线预警的国土空间生态修复规划实施监督体系是完善国土空间规划“一张图”信息系统的重要组成部分和政策支撑。通过对生态修复规划实施监督体系建设的研究，把握底线控制与监测预警机制，对接国土空间生态修复业务管理各项需求，搭建“五梯度四体系”的生态修复规划动态监测实施监督信息系统，构建以“七个一”为主要内容的生态修复规划底线预警监测评估机制，并与各层级国土空间规划“一张图”实施监督信息系统进行衔接，实现对生态修复规划实施的动态监测和预警。     

从土地资源优势区配置到主体功能区管理：一个国土空间治理的逻辑框架

研究目的:以土地资源配置方式遵从优势区开发原则到服从主体功能区分工协作规则演进规律,解释现行中国治理模式下国土空间管理逻辑。研究方法:文献分析法和理论分析法。研究结果:传统优势区开发理念为早期区域发展带来了集聚与规模经济效益,但其内部仍存在生产、生活、生态空间结构失衡等问题,降低了土地资源空间配置效率。主体功能区战略是传统优势区土地资源空间配置理念转型,是中国特定时期具有国家特色的经济发展模式,关键在于明确地方土地资源空间配置核心定位,应以法律规范"央地"土地关系、差别化实施政策制度安排、引导公众参与规划决策、创新生态(经济)补偿机制。研究结论:不同分区土地资源空间配置关系协调是世界性问题,转型时期中国国土空间优化和规制治理重点在于厘清中央与地方事权关系,精准落地县域主体功能区建设,健全主体功能配套政策体系。     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.