Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.     

澜湄命运共同体建设的意义、动因和路径选择

构建澜湄国家命运共同体是中国周边外交和亚洲命运共同体构想的具体实践，将助力东盟共同体建设，促进次区域稳定和繁荣。本文通过对澜湄命运共同体建设的意义、动因和影响因素分析，提出了澜湄命运共同体建设的路径选择。     

Evaluation of multi-objective optimization approaches for solving green supply chain design problems

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO₂ and fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker׳s (DM׳s) indirect preferences into the search process. Within this case study, the CO₂ emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased.     

农村劳动力转移就业的多元路径选择

长期以来，我国农村劳动力转移就业基本上是以流向东部沿海经济发达地区为主的单一模式。近年来，受东部沿海地区产业向中西部地区转移进程加快和内地经济快速发展的影响，区域间的比较优势逐渐弱化，农村劳动力远距离就业的选择空间越来越大，当地政府必须通过调整农业经济结构、鼓励农民自主创业、加大招商引资力度和开辟国外劳务市场等途径探索多元转移就业路径，实现农村劳动力合理转移就业。     

Optimal determination of the parameters of some biased estimators using genetic algorithm

In this paper, some new algorithms for estimating the biasing parameters of the ridge, Liu and two-parameter estimators are introduced with the help of genetic algorithm (GA). The proposed algorithms are based on minimizing some statistical measures such as mean square error (MSE), mean absolute error (MAE) and mean absolute prediction error (MAPE). At the same time, the new algorithms allow one to keep the condition number and variance inflation factors to be less than or equal to ten by means of the GA. A numerical example is presented to show the utility of the new algorithms. In addition, an extensive Monte Carlo experiment is conducted. The numerical findings prove that the proposed algorithms enable to eliminate the problem of multicollinearity and minimize the MSE, MAE and MAPE.     

新农村建设中农民主体地位实现的信息化路径

农民是实践主体与价值主体的统一体,其主体地位的实现与否直接关系到社会主义新农村建设的成败。信息化与农民主体地位的实现具有一定的内在联结,实现农民主体地位是信息化在农村的发展目标,信息化在农村的发展要以农民主体地位的实现为基础,两者统一于社会主义新农村建设。在新农村建设中,应该以信息化激发农民的主体意识,提高农民的文化素质,拓宽农民利益表达渠道,改善农村公共服务水平,以此推动农民主体地位的实现。     

中国—东盟政治合作机制研究

本文根据中国—东盟区域合作发展的政治合作机制的概念及呈现的多维特性,运用政治合作的目标分解和构成要素,对政治合作机制进行综合的结构分析。通过利益泛化(低度)、制度分化(中度)和价值固化(深度)生成的三个阶段性使命目标(和睦共享、和平共处与和谐共荣)合作过程的剖析,对中国—东盟政治合作机制建设的结构性问题进行探讨,以此助推区域政治合作机制的改进和优化。     

考虑储存成本的集装箱公铁联运路径优化

联运企业的效益受运输路径和运输方式的影响。如何降低联运成本、提高联运企业的效益,已成为学术研究的重中之重。针对集装箱公铁联运路径优化问题,以运输成本、换装成本、储存成本、时间惩罚成本构成的运输总成本为目标函数,建立集装箱公铁联运路径优化模型,考虑运输时间限制、班期限制、节点储存成本,得出使运输总成本最低的运输路径及运输方式,并利用算例验证模型的有效性。     

新时代侨校思想政治工作的价值定位和路径优化

侨校思想政治工作是高校思想政治工作的有机构成。进入新时代，要准确把握侨校思想政治工作的价值定位，直面当前侨校思想政治工作中所面临的价值观念多元化、工作队伍薄弱化和工作对象差异化的挑战，创新思路，统筹着力，牢牢掌握侨校意识形态工作的话语权，汇聚侨校思想政治工作的强大合力，构建分层化差异化的教育模式，增强侨校思想政治工作的时代性、针对性和实效性。     

生态视域下大学生理想信念教育优化路径探讨

理想信念教育是高校思政工作的核心，文章从生态学的视角分析了大学生理想信念教育的现状，并提出从教育目标、主体关系、客体选择、介体形成、环体营造等方面优化大学生理想信念教育生态体系，以达到培养社会主义合格建设者和可靠接班人的目的。