金属－橡胶硫化粘接过程中分子取向行为的间接证据

对金属－橡胶热硫化粘接复合体的剥离行为进行了研究，同一粘接试样的双向１８０°剥离试验显示出了巨大的差异。本文分析了弹性材料在硫化过程中的分子行为，并提出了相应的新假设，认为这是由于金属－橡胶在硫化粘接过程中分子取向交联行为所致。这种双向１８０°剥离破坏现象可被看作是金属－橡胶硫化粘接过程中分子取向行为的间接证据。     

Age effects in coding tasks: Componential analysis and test of the sensory deficit hypothesis.

Multiple forms of a symbol-digit substitution task were used to provide a componential analysis of age differences in coding task performance. The results demonstrated age differences in feature encoding, memory, and visual search. A 2nd experiment was conducted with young adults to investigate a sensory deficit as a locus of age differences. The spatial contrast sensitivity deficit of older adults was simulated on forms by applying a digital filter. Persons in the age-simulated contrast condition performed worse than those in the normal contrast condition. The stimulus degradation effect was linked to visual search speed. The study illustrates the utility of componential analysis and offers direct support for the hypothesis that sensory deficits affect performance on tasks used to assess intelligence (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Alzheimer’s Disease Animal Models: Elucidation of Biomarkers and Therapeutic Approaches for Cognitive Impairment

Alzheimer’s disease (AD) is an age-related and progressive neurodegenerative disorder. It is widely accepted that AD is mainly caused by the accumulation of extracellular amyloid β (Aβ) and intracellular neurofibrillary tau tangles. Aβ begins to accumulate years before the onset of cognitive impairment, suggesting that the benefit of currently available interventions would be greater if they were initiated in the early phases of AD. To understand the mechanisms of AD pathogenesis, various transgenic mouse models with an accelerated accumulation of Aβ and tau tangles have been developed. However, none of these models exhibit all pathologies present in human AD. To overcome these undesirable phenotypes, APP knock-in mice, which were presented with touchscreen-based tasks, were developed to better evaluate the efficacy of candidate therapeutics in mouse models of early-stage AD. This review assesses several AD mouse models from the aspect of biomarkers and cognitive impairment and discusses their potential as tools to provide novel AD therapeutic approaches.     

Novel Apriori-Based Multi-Label Learning Algorithm by Exploiting Coupled Label Relationship

It is a key challenge to exploit the label coupling relationship in multi-label classification (MLC) problems. Most previous work focused on label pairwise relations, in which generally only global statistical information is used to analyze the coupled label relationship. In this work, firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples, which combines global and local statistical information, and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels, which can exploit the label coupling relations more accurately and comprehensively. The experimental results on text, biology and audio datasets shown that, compared with the state-of-the-art algorithm, the proposed algorithm can obtain better performance on 5 common criteria.     

虚拟维修模型的校验方法研究

介绍了虚拟雏修与建模仿真的关系,分析了虚拟维修模型校验的特点,研究了虚拟维修模型校验的方法,分别应用置信区间法、假设检验法、Bayes法、TIC法、频谱分析法从静态性能和动态性能两个方面对模型实施校验,从而为虚拟维修模型准确性、实时性提供了较为可靠的验证方法.为虚拟维修模型的校验提供一套系统的理论和方法,有利于虚拟维修模型得到更好应用.     

Revisiting Statistical Design and Analysis in Scientific Research

Statistics is essential to design experiments and interpret experimental results. Inappropriate use of the statistical analysis, however, often leads to a wrong conclusion. This concept article revisits basic concepts of statistics and provides a brief guideline of applying the statistical analysis for scientific research from designing experiments to analyzing and presenting the data.     

Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.     

“SCENARIO”应用初探

"scenario"又称"情景故事",是设计方法中常用的一种假设即定场景故事的方法,是对所设计的产品或策略的未来发展前景进行假设后所提出的设计方案。本文主要是从"scenario"的涵义、发展历史及其基本要素几个方面对"scenario"的运用进行初步的探讨,以期能为产品设计活动提供更多更有意义的帮助。