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。     

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.     

Optimal granularity for portfolio choice

Many optimization-based portfolio rules fail to beat the simple 1/N rule out-of-sample because of parameter uncertainty. In this paper we suggest a grouping strategy in which we first form groups of equally weighted stocks and then optimize over the resulting groups only. This strategy aims at balancing the trade-off between the benefits from optimization and the losses from estimation risk. We rely on Monte-Carlo simulations to illustrate the performance of the strategy, and we derive the optimal group size for a simplified setup. Furthermore, we show that estimation risk also has an impact via the criterion by which the assets are sorted into groups (like the expected excess returns or betas), but does not negate the grouping approach. We relate our work to linear asset pricing models, and we conduct out of sample back-tests in order to confirm the validity of our grouping strategy empirically.     

基于运行规划的能源综合服务系统能效管理研究

以浙江电网企业转型现代能源综合服务的具体战略与发展规划为研究背景，针对能源综合服务系统的运行规划目标，开展基于运行规划的能源综合服务系统能效管理研究。从系统工程的视角出发，以能源流为纽带，基于多尺度能源系统建模方法，构建了能源综合服务系统模型，并结合系统综合能效，采用粒子群优化算法来优化系统各设备单元的运行策略，以实现基于运行规划的最优能效管理。通过能源综合服务系统模型计算表明，大规模清洁能源和可再生能源的接入，可以明显提高系统综合能效；随着区域用户总冷量和/或总热量需求占总电力需求的比值增大，系统最优综合能效逐渐增大；与使用内燃机、燃气轮机及汽轮机为原动机的热电联产系统相比，使用燃料电池为原动机的热电联产系统可以使能源综合服务系统综合能效更优。     

基于预约策略的医院CT设备使用优化研究

预约检查是对有限的医疗检查资源的一种调度优化。以攀枝花市A医院CT检查为例,通过实地调研,统计CT设备的服务能力,门诊患者,住院患者和急诊患者的平均到达率,接受患者的单位检查收益和拒绝患者的单位检查损失。通过调整预约策略使有限的资源设备产生更多的收益,并运用仿真模拟不同预约策略下的收益效果。研究结果对医院重要设备的预约使用提供了一定参考价值。     

Diversified behavioral portfolio as an alternative to Modern Portfolio Theory

The traditional mean–variance approach has been complemented by alternative theories that use risk measures different from standard deviation of returns or involve additional distributional features of returns like skewness and kurtosis. We propose a portfolio choice model that combines different distributional characteristics of the returns in the decision-making making process, considering preferences of investors which are modeled as non-statistical uncertainties of investors using fuzzy theory. We use 20 stocks of the S&P500 from January 2013 to December 2017. We assess the obtained portfolios’ performance, and the diversified behavioral portfolios outperform than the mean–variance portfolio. This methodological proposal can be seen as a strong managerial tool to make investment portfolio decisions.