Vero细胞蛋白过敏原性研究

目的　研究Vero细胞蛋白的过敏原性。方法　取不同剂量Vero细胞宿主细胞蛋白和裂解蛋白致敏豚鼠 ,隔日 1次 ,共 3次 ,每组 3只 ,以牛血清及生理盐水分别为阳性及阴性对照 ,末次致敏后 2 1d攻击 ,并观察攻击后的反应。结果　攻击后 30min ,10 0ng 只以上剂量组均出现过敏反应 ,且反应强度与剂量呈正相关。 2 4h各剂量组过敏反应的恢复情况各不相同。结论　Vero细胞宿主细胞蛋白及裂解蛋白均可以引起过敏反应 ,裂解蛋白的反应强度高于宿主细胞蛋白。     

国外过滤技术的最新发展与应用

本文综述了国外过滤技术的最新发展，重点介绍了动态过渡技术、膜分离技术及综合过滤技术的发展及其工业应用。     

Nonextensive lattice gauge theories: Algorithms and methods

High-energy phenomena presenting strong dynamical correlations, long-range interactions and microscopic memory effects are well described by nonextensive versions of the canonical Boltzmann–Gibbs statistical mechanics. After a brief theoretical review, we introduce a class of generalized heat-bath algorithms that enable Monte Carlo lattice simulations of gauge fields on the nonextensive statistical ensemble of Tsallis. The algorithmic performance is evaluated as a function of the Tsallis parameter q$q$ in equilibrium and nonequilibrium setups. Then, we revisit short-time dynamic techniques, which in contrast to usual simulations in equilibrium present negligible finite-size effects and no critical slowing down. As an application, we investigate the short-time critical behaviour of the nonextensive hot Yang–Mills theory at q$q$-values obtained from heavy-ion collision experiments. Our results imply that, when the equivalence of statistical ensembles is obeyed, the long-standing universality arguments relating gauge theories and spin systems hold also for the nonextensive framework.     

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.     

Dynamics of the wet-end section in paper mills

A model representing the wet-end section of a paper mill has been developed to characterize its dynamic behavior during the grade change. The model is based on the mass balance relationships written for the simplified wetend white water network. From the linearization of the dynamic model, higher-order Laplace transfer functions were obtained followed by the reduction procedure to give simple lower-order models in the form of 1^st-order or 2^nd-order plus dead times. The dynamic response of the wet-end is influenced both by the white water volume and by the level of wire retention. Effects of key manipulated variables such as the thick stock flow rate, the ash flow rate and the retention aid flow rate on the major controlled variables were analyzed by numerical simulations. The simple dynamic model developed in the present study can be effectively used in the operation and control. This paper is dedicated to Professor Se Ki Moon on the occasion of his retirement from Hanyang University.     

Dynamic replica placement and selection strategies in data grids— A comprehensive survey

Data replication techniques are used in data grid to reduce makespan, storage consumption, access latency and network bandwidth. Data replication enhances data availability and thereby increases the system reliability. There are two steps involved in data replication, namely, replica placement and replica selection. Replica placement involves identifying the best possible node to duplicate data based on network latency and user request. Replica selection involves selecting the best replica location to access the data for job execution in the data grid. Various replica placement and selection algorithms are available in the literature. These algorithms measure and analyze different parameters such as bandwidth consumption, access cost, scalability, execution time, storage consumption and makespan. In this paper, various replica placement and selection strategies along with their merits and demerits are discussed. This paper also analyses the performance of various strategies with respect to the parameters mentioned above. In particular, this paper focuses on the dynamic replica placement and selection strategies in the data grid environment.     

某星载天线复合材料骨架的动态特性

本文介绍了纤维缠绕复合材料壳体的刚度的计算方法，采用有限元工程分析软件ANSYS对某星载雷达碳纤维缠绕复合材料天线骨架结构的动态特性进行了分析和计算，所得结果为天线结构设计提供了参考依据。     

Emissions control policies under uncertainty and rational learning in a linear-state dynamic model

We compare the use of price-based policies or taxes, and quantity-based policies or quotas, for controlling emissions in a dynamic setup when the regulator faces two sources of uncertainty: (i) market-related uncertainty; and (ii) ecological uncertainty. We assume that the regulator is a rational Bayesian learner and the regulator and firms have asymmetric information. In our model the structure of Bayesian learning is general. Our results suggest that the expected level of emissions is the same under taxes and quotas. However, the comparison of the total benefits related to these policies suggests that taxes dominate quotas, that is, they provide a higher social welfare. Even though taxes have some benefits over quotas, neither learning nor ecological uncertainty affect the choice of policy, i.e., the only factor having such an impact is uncertainty in the instantaneous net emissions benefits (market-related uncertainty). Besides, the more volatile is this uncertainty, the more benefits of taxes over quotas. Ecological uncertainty leads to a difference between the emissions rule under the informed and the rational learning assumptions. However, the direction of this difference depends on the beliefs bias with regard to ecological uncertainty. We also find that a change in the regulator’s beliefs toward more optimistic views will increase the emissions.     

Feature extraction of the first difference of EMG time series for EMG pattern recognition

This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2–8%.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.