4D打印形状记忆聚合物在生物医疗领域的研究进展

形状记忆聚合物是一种在外界刺激条件下产生形状变化的智能材料, 4D打印是基于可变形材料和3D打印技术的一种综合性技术,可变形材料中形状记忆聚合物的应用最为广泛,目前4D打印形状记忆聚合物在各个领域都有应用,尤其是在生物医疗领域具有巨大的应用价值. 4D打印技术突破了传统医学领域个性化订制的技术瓶颈,为生物医疗领域的进一步发展提供了新的契机.本文首先综述了形状记忆聚合物、3D打印技术以及4D打印形状记忆聚合物在生物医疗领域的国内外研究进展,并介绍了4D打印形状记忆聚合物在生物医疗领域的实例和应用价值,最后总结了4D打印形状记忆聚合物在生物医疗领域的应用前景、存在的问题以及未来的发展方向.     

茶多酚对裸鼠喉鳞癌的抑制作用及机制研究

目的观察萘多酚对裸鼠喉鳞癌的抑制作用，并初步探讨其机制。方法将接种Hep22细胞后的裸鼠随机分为4组，其中对照组8只，每天给予生理盐水1ml灌胃；茶多酚高、中、低剂量组各8只，每天分莉给予茶多酚80、160、320mg／kg。20d后处死。分离肿瘤测量其体积，real—time RT—PCR法检测肿癌组织中Src、HK Ⅱ mRNA的表这。己糖激酶活性测定试剂盒测定肿瘤组织中己糖激酶活性。结果茶多酚能硬显降低肿瘤体积，降低肿瘤组织中Src、HK Ⅱ mRNA的表达和己糖激酶活性。结论茶多酚能抑制裸鼠喉鳞癌的生长，其机制可能与抑制肿瘤糖酵解有关。     

基于文献计量的模糊数学在安全系统工程中的应用研究

为探究模糊数学在安全系统工程领域的应用现状,以进一步丰富安全系统工程的内涵和方法体系,首先运用文献计量法和内容分析法,从时间视角出发分析模糊数学应用于安全领域的相关文献数量的宏观分布特征,然后在此基础上从研究内容、方法类别和应用行业3方面分析其微观分布特征,最后针对模糊数学的具体方法及其在安全领域的应用现状进行归纳分析和展望。结果表明,模糊数学在安全系统工程领域的研究热点集中于多维度因素、人因关联、因素可变等条件下的安全问题评估等方面,但由于模糊数学方法的不完备性、应用混乱等不足,模糊数学在安全系统工程中的应用逐渐进入瓶颈期,需从模糊数学方法体系、评价指标体系建设等方面进行改进。     

材料科技领域发展态势与趋势

材料的创新进步很大程度上推动了各领域的重大科技突破,是现代科技发展之本。世界上许多发达国家已经认识到材料研发的重要性,并制定了相应的国家发展战略规划。从设计、制备、表征、应用的链条看,近年来材料领域热点频现,人工智能技术的发展也渗入到了本领域成为新的关注点,涌现出诸多新型材料,性质与结构研究获得新的突破,应用成果丰硕。本文利用情报研究方法,梳理了部分国家在材料领域的重要规划,以及取得的最新进展和突破,并初步提出了对我国的启示与建议。     

基于中美基金项目数据的深度学习领域研究热点对比分析

与传统研究成果类型不同，基金项目数据中蕴含的潜在情报更具有战略性和前瞻性。本文使用文本挖掘方法，对中美深度学习领域基金项目数据进行挖掘与分析，从资助强度、发展态势、主题聚类及热度演化角度进行分析，对比中美深度学习领域研究热点的异同及热点演化情况。研究发现，中美深度学习领域基金项目数量自2010年后呈上升趋势，发展势头较好，且两国均出台相关政策支持该领域发展。但中美两国在研究的侧重方向上有所不同，美国侧重深度学习基础理论、算法研究，中国在深度学习更关注应用层面的落地情况。在应用层面上，美国更重视生物医学、经济领域、图像识别领域及硬件设备应用，中国不仅重视生物医学领域、同时使用深度学习相关算法对地学领域、多媒体领域的数据应用较多。新兴研究方向上，对深度学习硬件设备及应用方向成为美国的近年来研究热点方向，而中国较新的研究热点方向在于将深度学习应用到生物信息领域。未来我国应加大科研经费的投入，支持人工智能领域的自主创新发展；发挥在地学领域应用、图像和计算机视觉、多媒体领域、以及中医领域应用的科技布局优势，形成成果转化新生态；应关注和加强对理论算法的研究，提高技术实力并掌握科技主动权；对软硬件及...     

基于JASMIN框架的快速多极子并行解法器

快速多极子方法将N体问题的计算复杂度从O(N2)降到O(NlogN)或O(N),已应用于电磁散射和位错动力学等领域.在将快速多极子方法分离为共性和个性两部分后,设计了可供多个领域应用程序共享使用的快速多极子并行解法器,并在JASMIN框架内实现.该解法器封装共性部分,提供抽象接口支持用户按串行方式实现个性部分.共性部分包括多个网格层的分布存储、层间和层内数据通信以及组织计算等.个性部分包括与应用紧密相关的多极展开和局部展开以及转移算子等.该解法器已应用于两个领域的并行程序.数值模拟测试表明,它在1024个处理器上的并行效率可达到80%以上.     

磁性纳米颗粒的磁矩测量

磁性纳米材料在生物医学领域内有着较为广泛的应用,具有较大研究价值,如何测量磁性纳米材料的磁学参数成为一个重要的研究课题.本文介绍了几种目前实验室中较为常见的测量磁性纳米材料磁矩的方法:磁力显微镜、透射电子显微镜、超导量子干涉仪、巨磁电阻传感器、振动样品磁强计、磁天平以及电子顺磁共振仪,分别阐述了它们的原理、发展现状及应用前景,并介绍了它们在研究磁性纳米材料磁学性质上的新进展,分析了它们的优缺点以及在生物医学领域相关应用的各自适用范围与局限性.最后介绍了一种对类生理环境中的活体磁性材料的磁矩测量新方法:基于动力学分析测量材料磁性的方法.     

金属-有机框架材料的合成及其在水中重金属吸附的应用研究现状

金属-有机框架材料(MOFs)具有设计合成方法多样、合成后易于改性、对重金属吸附量大、平衡时间短等优点,已被广泛研究和应用。近年来,MOFs在水中重金属吸附中的应用研究,成为了十分热门的课题。水稳性是反映吸附材料在水中稳定性的重要参数,具有良好的水稳性是MOFs成功地应用于去除水中重金属离子的前提。本文结合近十年MOFs在重金属吸附领域的研究情况,从MOFs的合成、水稳性、在重金属吸附中的应用、再生与重复利用几个方面,综述了MOFs的合成方法、MOFs的合成后修饰、MOFs的水稳性、MOFs对水中As、Pb、Hg、Cd、Cr、Cu等重金属离子的吸附,以及MOFs的再生等的研究现状,并提出了今后MOFs在重金属吸附领域的研究方向,为MOFs在重金属吸附领域中合成与应用提供参考。     

我国近期激光前沿若干重要进展评述

结合2012年10月20~21日在北京召开的以"激光前沿"为主题的科学与技术前沿论坛讨论的问题,在概述国际激光发展现状和前沿趋势的基础上,重点评述了我国国内激光领域涉及基础、应用科研以及产业发展的若干前沿进展.以此为基础,对我国激光技术和产业发展,尤其是在激光技术与国民经济密切相关的应用及产业领域提出了多方面的相关发展建议.     

复杂人机系统设计中认知工作分析理论、技术与应用研究进展

认知工作分析是人机系统设计中的一种重要研究方法.目前在航空、军事、交通运输、能源、石化和医疗等复杂人机系统领域开始得到了推广应用,它对于提高复杂人机系统的安全、扩展系统功能、提高人机交互的适应性、降低系统全生命周期的成本有着十分重要的意义.首先通过对国内外相关文献的总结,论述了认知工作分析理论体系中约束、生态内涵两个核心内容并阐述了两者之间的内在联系;随后详细分析了认知工作分析构建过程中的关键技术及其各维度建模工具的内涵和使用方法;最后针对复杂人机系统设计的发展,采用范围综述的方法对6个复杂人机系统领域内的223篇文献进行了研究,分析了认知工作分析在这些领域中的研究现状、应用中的优势以及在未来的发展趋势.     

Catechine und Hydroxy-flavandiole als Gerbstoffbildner

Summary From the ranks of the colourless hydroxyflavans, four differenthydroxyflavan-3-ols(catechins and epi-catechins), as well as eight hydroxyflavan-3,4-diols, have so far been encountered in nature. These substances are also procurable by synthesis. On treatment with hot water or dilute acids, they are easily converted into soluble tannins or insoluble phlobaphenes. The mechanism of the self-condensation is explained using catechin via the isolation of a dimerie product. A probable path for the self-condensation of the diols has been indicated. In contrast to the products of their self-condensation, the monomeric polyhydroxyflavans are not real tannins. A particular accumulation of phenolhydroxyl groups in the molecule, the tendency to form supersaturated solutions and low solubility in water in the — generally not attainable — crystalline state are prerequisites for tanning properties. In nature, the self-condensation of polyhydroxyflavans proceeds (e. g. in the wood ofAcacia catechu or in Quebracho Wood) without the assistance of enzymes. Dehydrogenative polymerisation occurs with formation of brown or red phlobaphenes (e.g. in cocoa beam) which are generally insoluble.     

Bayesian Assessment of Dynamic Quantile Forecasts

Bayesian methods for assessing the accuracy of dynamic financial value‐at‐risk (VaR) forecasts have not been considered in the literature. Such methods are proposed in this paper. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, dynamic quantile forecasts are developed. To evaluate the relevant marginal likelihoods, analytic integration methods are utilized when possible; otherwise multivariate adaptive quadrature methods are employed to estimate the required quantities. The usual Bayesian interval estimate for a proportion is also examined in this context. The size and power properties of the proposed methods are examined via a simulation study, illustrating favourable comparisons both overall and with their frequentist counterparts. An empirical study employs the proposed methods, in comparison with standard tests, to assess the adequacy of a range of forecasting models for VaR in several financial market data series. Copyright © 2016 John Wiley & Sons, Ltd.     

农田渍害遥感监测方法研究应用进展

首先介绍了农田渍害的表现特征;其次根据监测原理,将国内外农田溃害遥感监测的主要方法分为地表指示标志法、地下水位反演法和与非遥感方法相结合的综合分析法,并分别对各类方法的原理、技术、适用范围及优缺点进行分析,发现各种方法均能解决特定的农田渍害监测问题,同时也存在着缺陷和局限性;最后,进行了总结并指出了两种潜在的遥感监测方...     

Time-series analysis supported by power transformations

This paper presents some procedures aimed at helping an applied time-series analyst in the use of power transformations. Two methods are proposed for selecting a variance-stabilizing transformation and another for bias-reduction of the forecast in the original scale. Since these methods are essentially model-independent, they can be employed with practically any type of time-series model. Some comparisons are made with other methods currently available and it is shown that those proposed here are either easier to apply or are more general, with a performance similar to or better than other competing procedures.     

Time‐simultaneous prediction band for a time series

I propose principles and methods for the construction of a time‐simultaneous prediction band for a univariate time series. The methods are entirely based on a learning sample of time trajectories, and make no parametric assumption about its distribution. Hence, the methods are general and widely applicable. The expected coverage probability of a band can be estimated by a bootstrap procedure. The estimate is likely to be less than the nominal level. Expected lack of coverage can be compensated for by increasing the coverage in the learning sample. Applications to simulated and empirical data illustrate the methods. Copyright © 2007 John Wiley & Sons, Ltd.     

Outliers,level shifts,and variance changes in time series

Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.     

Disaggregation of annual time-series data to quarterly figures: A comparative study

In this paper we consider some of the prominent methods that are available in the literature for the problem of disaggregating annual time-series data to quarterly figures. The procedures are briefly described and illustrated through a real data set. The performances of the methods are compared in a Monte Carlo study. The results indicate that the complicated model-based procedure is usually superior to other non-model-based alternatives in the large sample situations. Based on the simulation results, we make some recommendations regarding the use of these methods.     

Forecasting for business planning: A case study of IBM product sales

This is a case study of a closely managed product. Its purpose is to determine whether time-series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two time-series methods, Box-Jenkins modeling and Holt-Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box-Jenkins transfer-function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next-quarter, next-half-year and next-full-year forecasts. We find that the best Box-Jenkins models give forecasts which are clearly better than those obtained from Holt-Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box-Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business-as-usual annual and monthly forecasts for the next year, and in mid-year for resetting the remaining monthly forecasts.     

Evidence for the selection of forecasting methods

Reid (1972) was among the first to argue that the relative accuracy of forecasting methods changes according to the properties of the time series. Comparative analyses of forecasting performance such as the M‐Competition tend to support this argument. The issue addressed here is the usefulness of statistics summarizing the data available in a time series in predicting the relative accuracy of different forecasting methods. Nine forecasting methods are described and the literature suggesting summary statistics for choice of forecasting method is summarized. Based on this literature and further argument a set of these statistics is proposed for the analysis. These statistics are used as explanatory variables in predicting the relative performance of the nine methods using a set of simulated time series with known properties. These results are evaluated on observed data sets, the M‐Competition data and Fildes Telecommunications data. The general conclusion is that the summary statistics can be used to select a good forecasting method (or set of methods) but not necessarily the best. Copyright © 2000 John Wiley & Sons, Ltd.     

Intermittent demand forecasting in the Enterprise: Empirical verification

Forecasting methods are often valued by means of simulation studies. For intermittent demand items there are often very few non–zero observations, so it is hard to check any assumptions, because statistical information is often too weak to determine, for example, distribution of a variable. Therefore, it seems important to verify the forecasting methods on the basis of real data. The main aim of the article is an empirical verification of several forecasting methods applicable in case of intermittent demand. Some items are sold only in specific subperiods (in given month in each year, for example), but most forecasting methods (such as Croston's method) give non–zero forecasts for all periods. For example, summer work clothes should have non–zero forecasts only for summer months and many methods will usually provide non–zero forecasts for all months under consideration. This was the motivation for proposing and testing a new forecasting technique which can be applicable to seasonal items. In the article six methods were applied to construct separate forecasting systems: Croston's, SBA (Syntetos–Boylan Approximation), TSB (Teunter, Syntetos, Babai), MA (Moving Average), SES (Simple Exponential Smoothing) and SESAP (Simple Exponential Smoothing for Analogous subPeriods). The latter method (SESAP) is an author's proposal dedicated for companies facing the problem of seasonal items. By analogous subperiods the same subperiods in each year are understood, for example, the same months in each year. A data set from the real company was used to apply all the above forecasting procedures. That data set contained monthly time series for about nine thousand products. The forecasts accuracy was tested by means of both parametric and non–parametric measures. The scaled mean and the scaled root mean squared error were used to check biasedness and efficiency. Also, the mean absolute scaled error and the shares of best forecasts were estimated. The general conclusion is that in the analyzed company a forecasting system should be based on two forecasting methods: TSB and SESAP, but the latter method should be applied only to seasonal items (products sold only in specific subperiods). It also turned out that Croston's and SBA methods work worse than much simpler methods, such as SES or MA. The presented analysis might be helpful for enterprises facing the problem of forecasting intermittent items (and seasonal intermittent items as well).