贪心算法求解最少资源问题的探讨

面对资源的日益紧缺,如何有效合理的利用资源一直是专家学者研究和探讨的热点问题。最少资源问题是对初步资源规划问题的探讨,可以为多个资源组合规划问题的基础研究提供有效的参考作用。传统的回溯法穷举虽然能找到最少资源问题的最优解但其时间复杂度会高于o(n!),往往耗时太多,不能满足问题的及时性。提出了一种以最早开始时间为贪心策略的求解最少资源问题的贪心算法,不仅能够找到最优解,而且其时间复杂度仅为o(n2),极大提高了算法的效率。     

基于内容相似性的个性化推荐系统的应用

推荐系统应用于远程教育平台中，有助于学生学习。推荐系统具有在线和离线推荐两部分，在线推荐使用最近邻居完成，离线推荐采用基于语义相似的推荐算法，利用信息内容和用户兴趣的相似性来分析数据仓库中相关数据针对用户兴趣模型找到相似文档进行推荐。     

The impacts of CGIAR research: A review of recent evidence

We review evidence on the impacts of CGIAR research published since 2000 in order to provide insight into how successfully the CGIAR Centers have been in pursuing the System’s core missions. Our review suggests that CGIAR research contributions in crop genetic improvement, pest management, natural resources management, and policy research have, in the aggregate, yielded strongly positive impacts relative to investment, and appear likely to continue doing so. Crop genetic improvement research stands out as having had the most profound documented positive impacts. Substantial evidence exists that other research areas within the CGIAR have had large beneficial impacts although often locally and nationally rather than internationally. However, the “right-time, right-place” nature of successful policy research and the relatively limited geographic scale of much natural resource management research often limits the overall scale of impacts of these programmatic thrusts vis-à-vis genetic improvement research. We conclude that given the evidence available, the CGIAR’s portfolio of research allocations has become overly skewed toward natural resource management and policy research over time. Hence, restoring somewhat the share of resources allocated to crop genetic improvement is warranted. In addition, the CGIAR needs to prioritize impact assessment of resource management and policy research to deepen its understanding of the social and environmental impacts of its work.     

The impact of climate and price risks on agricultural land use and crop management decisions

This article aims to investigate the impacts of climate change and of lower and more volatile crop price levels as currently observed in the European Union (EU) on optimal management decisions, average income and income risks in crop production in Western Switzerland. To this end, a bioeconomic whole-farm model has been developed that non-parametrically combines the crop growth model CropSyst with an economic decision model using a genetic algorithm. The analysis focuses on the farm level, which enables us to integrate a wide set of potential adaptation responses, comprising changes in agricultural land use as well as crop-specific fertilization and irrigation strategies. Furthermore, the farmer's certainty equivalent is employed as objective function, which enables the consideration of not only impacts on average income but also impacts on income variability.The study shows that that the effects of EU crop prices on the optimal management decisions as well as on the farmer's certainty equivalent are much stronger than the effects of climate change. Furthermore, our results indicate that the impacts of income risks on the crop farm's optimal management schemes are of rather low importance. This is due to two major reasons: first, direct payments make up a large percentage of the agricultural income in Switzerland which makes Swiss farmers less vulnerable to market and climate volatility. Second, arable crop farms in Switzerland have by law to cultivate at least four different crops. Due to these diverse cropping systems and high government direct payments risk does neither under climate change, market liberalization nor combinations thereof, play a very decisive role in arable farming in Switzerland.     

The Geographic Extent of the Market for Soybeans and Soybean Products

This study looks at the question of whether soybeans, soybean meal, and soybean oil produced in the United States and exported were part of a single, world geographic market during the decade of the 1980s. An answer to this question is sought using an approach to defining a geographic market based on the notion of instantaneous causality. The empirical results suggest that there was but a single identifiable world market for these commodities over the period of study.     

Can NN-algorithms and macroeconomic data improve OLS industry returns forecasts?∗

Using an extensive range of macroeconomic indicators and a number of two-stage models mixing OLS and a non-parametric approach known as the nearest neighbour algorithm, the authors analyse the potential for improving forecasts of US industry returns over those built by OLS on industry-specific variables only. Basic performance is measured by the average cross-sectional correlation over time between the 55 forecasted returns and the realized returns across industries. Since investors and asset managers typically want a steady performance over time, the volatility of this cross-sectional correlation is further taken into account in an adaptation of the Sharpe Ratio. Strong evidence is found in favour of certain macroeconomic factors as dominant industry return predictors, and some two-stage models based either purely on OLS or a mix between OLS and the non-linear model can lift both cross-sectional forecasting correlation and Sharpe Ratio. However, viewed overall in relation to the benchmark OLS model, performance is not consistently improved by any particular model.     

现代生物技术在食品领域中的应用研究

现代生物学和分子生物学的发展,对基因工程、细胞工程、酶工程、发酵工程等现代生物技术工程产生重要影响,其在食品发酵生产中的应用越来越广。本文阐述了基因工程、细胞工程、酶工程等现代生物技术在食品发酵业的应用。     

基于遗传算法的城际客运专线列车开行方案研究

分析城际客运专线列车开行方案的特点。以旅客时间消耗最小为目标，建立城际客运专线旅客列车开行方案的最小费用流模型。根据模型的特点，设计相应的遗传算法，并对遗传算法的适应值函数、算法步骤和算法复杂度进行分析。分析结果表明，该算法是一个收敛于全局最优解的近似有效算法。     

Huntington's disease,critical illness insurance and life insurance

We describe briefly a model of Huntington's disease (HD), a highly penetrant, dominantly inherited, fatal neurological disorder. Although it is a single-gene disorder, mutations are variable in their effects, depending on the number of times that the CAG trinucleotide is repeated in a certain region of the HD gene. The model covers: (a) rates of onset, depending on CAG repeat length as well as age; (b) post-onset rates of mortality; and (c) the distribution of CAG repeat lengths in the population. Using these, we study the critical illness and life insurance markets. We calculate premiums based on genetic test results that disclose the CAG repeat length, or more simply on a family history of HD. These vary widely with age and policy term; some are exceptionally high, but in a large number of cases cover could be offered within normal underwriting limits. We then consider the possible costs of adverse selection, in terms of increased premiums, under various possible moratoria on the use of genetic information, including family history. These are uniformly very small, because of the rarity of HD, but do show that the costs would be much larger in relative terms if family history could not be used in underwriting. We point out some difficulties involved in applying a moratorium that recognises simply a dichotomy between ‘carriers’ and ‘non-carriers’ of any mutation in a gene when these mutations are, in fact, very variable in their effects. These complexities suggest that restrictions on the disclosure, rather than on the use, of genetic information, if it became established as a principle, could deprive insurers of information needed for risk management even if not used in underwriting.     

Multivariate Cox Hidden Markov models with an application to operational risk

ABSTRACT

Modeling multivariate time-series aggregate losses is an important actuarial topic that is very challenging due to the fact that losses can be serially dependent with heterogeneous dependence structures across loss types and business lines. In this paper, we investigate a flexible class of multivariate Cox Hidden Markov Models for the joint arrival process of loss events. Some of the nice properties possessed by this class of models, such as closed-form expressions, thinning properties and model versatility are discussed in details. We provide the expectation-maximization (EM) algorithm for efficient model calibration. Applying the proposed model to an operational risk dataset, we demonstrate that the model offers sufficient flexibility to capture most characteristics of the observed loss frequencies. By modeling the log-transformed loss severities through mixture of Erlang distributions, we can model the aggregate losses. Finally, out-of-sample testing shows that the proposed model is adequate to predict short-term future operational risk losses.