数字花卉植物的全息可视化与交互实现

花卉植物形态、结构与生长过程的数字化和可视化是现代林业科研的重要研究内 容，数字花卉植物在科普、教育、展示等方面都有着广泛的应用与需求。全息影像技术是一种 新兴的前沿技术，其原理源自佩珀尔幻象，通常用于生成立体感强的影像，实现数字幻象与真 实世界的融合，拥有良好的观赏性，用户接受度较高。将全息影像技术与数字花卉植物相关研 究进行结合，可以加强数字花卉植物的可视化效果，从而实现更好的科普、教育、展示效果。 因此在现有的研究基础之上，考虑实现数字花卉植物的全息可视化，并在可视基础上增加了用 户交互实现，设计出一套兼具展示性和交互性的流程，力求总结出一种具有完整可行性的方案， 从而对数字花卉植物的新型表现形式进行深入研究，以期对数字植物研究与应用的进一步发展 提供参考。     

An EWMA signed ranks control chart with reliable run length performances

During the design phase of a control chart, the determination of its exact run length properties plays a vital role for its optimal operation. Markov chain or integral equation methods have been extensively applied in the design phase of conventional control charts. However, for distribution-free schemes, due to the discrete nature of the statistics being used (such as the sign or the Wilcoxon signed rank statistics, for instance), it is impossible to accurately compute their run length properties. In this work, a modified distribution-free phase II exponentially weighted moving average (EWMA)-type chart based on the Wilcoxon signed rank statistic is considered and its exact run length properties are discussed. A continuous transformation of the Wilcoxon signed rank statistic, combined with the classical Markov chain method, is used for the determination of the average run length in the in- and out-of control cases. Moreover, its exact performance is derived without any knowledge of the distribution of sample observations. Finally, an illustrative example is provided showing the practical implementation of our proposed chart.     

不同种源地香叶树籽的油脂含量及脂肪酸组成分析

为探明不同种源地香叶树作为油料树种的开发价值，采集了全国6省20县市的香叶树果实，对香叶树全果、果肉及籽的油脂含量及脂肪酸组成进行了分析，并采用ward法对不同种源地香叶树籽油脂肪酸进行了聚类分析。结果表明：香叶树全果、果肉以及籽的平均油脂含量分别为43.31%、42.38%及46.12%；全果油脂肪酸组成以月桂酸、油酸和癸酸为主；果肉油以油酸和棕榈酸为主；而籽油则以月桂酸和癸酸为主，月桂酸含量为53.20%~70.30%，月桂酸和癸酸总含量达76.40%~90.10%；聚类分析将不同种源地香叶树分为3大类，云南省、贵州省及广西省的香叶树属低月桂酸类型，福建省、江西省及湖南省的香叶树属中高月桂酸型。香叶树可作为生产中碳链脂肪酸的新晋树种，福建省的香叶树籽油脂含量高，且籽油中月桂酸含量高，作为中碳链脂肪酸的原料地具有明显的开发优势。     

A fast genetic algorithm for a critical protection problem in biomedical supply chain networks

In this paper, we present a new bilevel model for a biomedical supply chain network with capacity and budget constraint due to the protection and interdiction operations. The components considered in this model are biomedical devices, distribution centers (DCs), medical suppliers (MSs), and hospitals and patients as the demand points. On the other hand, two levels of decisions in the network planning is suggested: (1) the defender’s decision about protection operations of MSs and DCs, the assignment of clients to the DCs, and quantity of products shipped to DCs from MSs to minimize the demand-weighted traveling costs and transport costs and (2) the attacker’s decision about interdiction operations of MSs and DCs to maximize the capacity or service reduction and losses. Because of nondeterministic polynomial time (NP)-hardness of the problem under consideration, an efficient and fast approach based on a genetic algorithm and a fast branch and cut method (GA–FBC) was developed to solve the proposed model. Also, the efficiency via the comparison of results with the genetic algorithm based on CPLEX (GA-CPLEX) and decomposition method (DM) is investigated. In order to assess the performance of the presented GA–FBC, a set of 27 instances of the problem is used. Comprehensive analysis indicates that the proposed approach significantly solves the problem. In addition, the benefits and advantages of preference with running times and its accuracy is shown numerically. Simulation results clearly demonstrate that the defender’s objective effectively reduced and CPU time also within the large-sized instances of the problem in comparison with the GA-CPLEX and DM.     

大规模生物网络马尔可夫聚类的并行化算法

马尔可夫聚类算法（MCL）是在大规模生物网络中寻找模块的一个有效方法，能够挖掘网络结构和功能影响力较大的模块。算法涉及到大规模矩阵计算，因此复杂度可达立方阶次。针对复杂度高的问题，提出了基于消息传递接口（MPI）的并行化马尔可夫聚类算法以提高算法的计算性能。首先，生物网络转化成邻接矩阵；然后，根据算法的特性，按照矩阵的规模判断并重新生成新矩阵以处理非平方倍数矩阵的计算；其次，并行计算通过按块分配的方式能够有效地实现任意规模矩阵的运算；最后，循环并行计算直至收敛，得到网络聚类结果。通过模拟网络和真实生物网络数据集的实验结果表明，与全块集体式通信（FCC）并行方法相比，平均并行效率提升了10个百分点以上，因此可以将该优化算法应用在不同类型的大规模生物网络中。     

基于哈希链的软件定义网络路径安全

针对软件定义网络中，控制器无法保证下发的网络策略能够在转发设备上得到正确执行的安全问题，提出一种新的转发路径监控安全方案。首先以控制器的全局视图能力为基础，设计了基于OpenFlow协议的路径凭据交互处理机制；然后采用哈希链和消息验证码作为生成和处理转发路径凭据信息的关键技术；最后在此基础上，对Ryu控制器和Open vSwitch开源交换机进行深度优化，添加相应处理流程，建立轻量级的路径安全机制。测试结果表明，该机制能够有效保证数据转发路径安全，吞吐量消耗比SDN数据层可信转发方案（SDNsec）降低20%以上，更适用于路径复杂的网络环境，但时延和CPU使用率的浮动超过15%，有待进一步优化。     

Health-promoting effects of dietary polysaccharide extracted from Dendrobium aphyllum on mice colon,including microbiota and immune modulation

The health-promoting effects of a polysaccharide (DAP) purified from Dendrobium aphyllum, on mice colon were investigated. The results indicated that consumption of DAP facilitated the differentiation degree of CD4⁺ T cells to Th₁, Th₁₇ and Treg cells; and depressed the differentiation degree to Th₂ cells, since DAP up-regulated IL-6 expression (from 1.08 ± 0.24 to 3.40 ± 0.36). Besides, oral administration of DAP reduced the gastrointestinal transit time from 255 ± 1.21 to 164 ± 0.46 min, decreased the pH from 6.70 ± 0.27 to 5.54 ± 0.31, improved faecal water-binding capability from 59.4% ± 0.35 to 63.5% ± 0.22. Additionally, the increase in four health-promoting short chain fatty acids were observed, which might result from the enrichment of the relative abundance of health-promoting microbiota genera, including Porphyromonadaceae and Ruminococcaceae. The DAP-induced up-regulation of the genus Erysipelotrichaceae might be related to the homeostasis in both Th₁/Th₂ and Treg/Th₁₇.     

Extending social responsibility to small and medium-sized suppliers in supply chains: A fuzzy-set qualitative comparative analysis

Buying firms’ sales and reputation will be greatly damaged by any non-responsible behaviors on the part of suppliers, especially when those suppliers, like small and medium-sized enterprises (SMEs), have restricted resources and capabilities. To eradicate these risks, a growing number of buying firms have introduced socially responsible supplier development (SRSD). SRSD, including monitoring and evaluating suppliers, can provide them with incentives and assistance. Based on configuration theory and contingency theory, fuzzy-set qualitative comparative analysis (fsQCA) is adopted in this study to examine how SRSD practices adopted by buying firms, supply chain partnership, and market turbulence affect the corporate social responsibility (CSR) performance of their SME suppliers. We find that, as core factors, supplier monitoring, supplier assistance, and supply chain partnership can work together with peripheral conditions to achieve superior CSR performance. In addition, even at different levels of market turbulence, superior CSR performance can be realized through different causal configurations.     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

Biofuel supply chain optimal design considering economic,environmental, and societal aspects towards sustainability

Biofuel supply chain design plays a critical role in facilitating the large‐scale substitution of biofuel for traditional fossil fuels with a cost‐effective and environmentally friendly manner towards sustainability. This paper proposes a multiobjective optimization model for a 4‐layer biofuel supply chain network using mixed integer nonlinear programming while considering the benefits from economic, environmental, and societal aspects. The model can be used either to optimize an existing biofuel supply chain network or to guide the construction of a new biofuel supply chain network. The profit, the greenhouse gas emissions in transportation, and the market share of biofuel were set as targets for optimization. The selection of the participators at each layer, and the amount of the material flow between each pair of selected supplier and customer located at two adjacent layers were modeled as decision variables. The conventional weighted aggregation method was used to unify 3 objectives after normalization. Particle swarm optimization was used to solve this high‐dimension multiobjective problem to obtain a near optimal solution. A numerical case study based on the state of Missouri in the United States was implemented to verify the effectiveness of the proposed model. The results of the case study illustrate that the benefits in terms of transportation emission, profit, and market share can be achieved simultaneously. Using the equal weights configuration in conventional weighted aggregation as an example, a 21% reduction of the transportation emission, a 33% increase of the profit, and a 2% augmentation of the market share were achieved compared to the benchmark scenario.