手性指数的研究及其应用

该工作对手性化合物的构效关系进行了研究,其内容包括:(1)对二维拓扑指数Ami进行了扩展,即在指数的衍生中加入手性碳原子的因素,使之成功地应用于14个手性化合物活性的预测;(2)描述了构象独立的手性指数(CICC法)和构象依赖的手性指数(CDCC法),采用CDCC和CICC预测了80个二级醇的1H NMR化学位移差的符号,并由此可以确定该类化合物的绝对构型;(3)描述了原子构象依赖的手性指数(aCDCC),并将这种方法用于手性仲醇的α-碳原子的13C NMR在不同手性溶剂中的化学位移差的预测,获得了满意的结果.     

基于蕴涵算子族L-λ-R_0的模糊推理α-三I约束算法

提出了基于蕴涵算子族L-λ-R0的模糊推理的思想,这将有助于提高推理结果的可靠性。针对蕴涵算子族L-λ-R0给出了模糊推理的FMP模型及FMT模型的α-三I约束算法。     

基于蕴涵算子族Lλ0λG的反向三I支持算法

提出了基于蕴涵算子族Lλ0λG的模糊推理的思想,这将有助于提高推理结果的可靠性。针对蕴涵算子族Lλ0λG给出了模糊推理的FMP模型及FMT模型的反向三I支持算法。     

基于蕴涵算子族L-λ-R_0的模糊推理FMT三Ⅰ算法

提出了基于蕴涵算子族L-λ-R0的模糊推理的思想,这将有助于提高推理结果的可靠性。针对蕴涵算子族L-λ-R0已给出的FMP模型[1]的三Ⅰ支持算法、α-三Ⅰ支持算法进一步给出了模糊推理的FMT模型的三Ⅰ支持算法、α-三Ⅰ支持算法。     

基于数据库ADO技术的动态零件族的研究与建立

为了有效地管理和利用标准化设计资源,真正意义上实现数据共享,对零件族的层次结构和事物特性表进行了分析,提出了一种基于数据库ADO技术的动态零件族的信息建模方法。它是一种零件族表达与信息模型无关、信息容量大且实用性强的零件族信息模型的建模方法,能有效地解决零件信息来源不同的问题。运用VC 的MFC AppWizard和数据库ADO等知识,给出了零件族的建立过程,实现了对零件族进行添加、修改和删除等动态信息维护。这为零件库信息模型更高层次的实现提供了基础。     

Liquid crystal oligo(cyclosiloxanes)

The synthesis and properties of individual liquid crystal oligo(cyclosiloxanes),, wherex=4–8 andR is a 4-methoxyphenyl-4-alkyloxybenzoate type of mesogenic group, is described. Preliminary results concerning the effect of ring size on the mesophase formation are included. X-ray diffraction studies have shown that the cyclosiloxanes form a smectic A₁ phase.     

Methane coupling at low temperatures on Ru(0001) and Ru(11¯20) catalysts

The conversion of methane to higher hydrocarbons on single crystal Ru catalysts has been investigated using combined elevated-pressure kinetic measurements/surface science studies. The reaction consists of activation of methane on Ru(0001) and Ru(11¯20) surfaces to produce carbonaceous intermediates at temperatures between 350 and 700 K and rehydrogenation of these species to ethane and propane at 370 K. It is found that under the reaction conditions employed, the maximum yield in ethane/propane production occurs at 500 K on both surfaces. Influence of the hydrogenation temperature on the production of ethane and propane is also examined. On Ru(0001), the yields of ethane and propane maximize at = 400 K, whereas no maximum yield was observed on Ru(11 0) in the 300–500 K temperature range. Under optimum reaction conditions, hydrocarbon products consist of 16% ethane and 2% propane. High-resolution electron energy-loss spectroscopy (HREELS) has been used to identify various forms of hydrocarbonaceous intermediates following methane decomposition. An effort is made to relate the hydrocarbon intermediates identified by HREELS to the gas phase products observed in the elevated pressure experiments.     

Rigor, rigor mortis, and conspiratorial views of depression research.

The authors reassert the need for methodological changes in depression research appearing in the Journal of Personality and Social Psychology and other personality and social psychology journals. In this rejoinder the authors update their earlier literature review (H. Tennen, J. Hall, & G. Affleck; see record 1995-31710-001) and respond to the commentaries by P C. Kendall and E. C. Flannery-Schroeder (see record 1995-31700-001) and G. Weary, J. A. Edwards, and J. A. Jacobson (see record 1995-31713-001). The authors notice that G. Weary et al.'s own findings demonstrate the need to change how depression is measured and participants are assigned to experimental groups. The authors also challenge G. Weary et al.'s contention that structured interviews are limited because they require interviewer judgments, and they urge personality and social psychologists to learn more about these interviews. Finally, G. Weary et al.'s suspicion that depression research guidelines reflect professional parochialism is disputed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Belief Decay or Persistence? A Mixed-method Study on Belief Movement Over Time

When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual's beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will “decay” back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants' reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual's belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one's belief system.     

融合MAML和CBAM的安卓恶意应用家族分类模型

为满足对新兴安卓恶意应用家族的快速检测需求,提出一种融合MAML(model-agnostic meta-learning)和CBAM(convolutional block attention module)的安卓恶意应用家族分类模型MAML-CAS。将安卓恶意应用样本集中的DEX文件可视化为灰度图,并构建任务集;融合混合域注意力机制CBAM,设计两个具有同等结构的卷积神经网络,分别作为基学习器和元学习器,这两个学习器在自动提取任务集中样本特征的同时,可从通道和空间两个维度来增强关键特征表达;利用元学习方法 MAML对两个学习器进行训练,其中基学习器完成特定恶意家族分类任务的属性学习,元学习器则学习不同任务的共性;在两个学习器训练完成后,MAML-CAS将获得初始化参数,在面对新的安卓恶意应用家族分类任务时,不需要重新训练,只需要少量样本就可以快速迭代;利用训练完成的基学习器提取安卓恶意应用家族特征,并利用SVM进行恶意家族分类。实验结果表明,MAML-CAS模型对新兴小样本安卓恶意应用家族具有良好的检测效果,检测速度较快,并具有较好的稳定性。