智能数据分区与布局研究

大数据时代,数据规模庞大,由数据进行驱动的应用分析场景日益增多.如何快速、高效地从这些海量数据中提取出用以分析决策的信息,给数据库系统带来重大挑战.同时,现代商业分析决策对分析数据的实时性要求数据库系统能够同时快速处理ACID事务和复杂的分析查询.然而,传统的数据分区粒度太粗,且不能适应动态变化的复杂分析负载;传统的数据布局单一,不能应对现代大量增加的混合事务分析应用场景.为了解决以上问题,“智能数据分区与布局”成为当前的研究热点之一,它通过数据挖掘、机器学习等技术抽取工作负载的有效特征,设计最佳的分区策略来避免扫描大量不相关的数据,指导布局结构设计以适应不同类型的工作负载.首先介绍了智能数据分区与布局的相关背景知识,然后对智能数据分区与布局技术的研究动机、发展趋势、关键技术进行详细的阐述.最后,对智能数据分区与布局技术的研究前景做出总结与展望.     

Crystalline lens thickness change is associated with axial length elongation and myopia progression in orthokeratology

AimsConsidering individual variability in regards to the effects of orthokeratology (ortho-k) on myopia progression and controversies regarding the precise underlying mechanism, the aim of this study was to investigate several ocular measurements associated with axial length (AL) growth in children wearing ortho-k lenses.MethodsIn this retrospective chart review, medical records of 53 Chinese children who wore ortho-k lenses over the course of 12 months were reviewed. Baseline variables included age at initiation of ortho-k wear, refractive error (spherical equivalent, SE), central corneal thickness (CCT), and flat and steep keratometry of corneal principal meridians. The change of anterior chamber depth (ACD) and the change of crystalline lens thickness (CLT) between baseline and the 12-month follow-up were also analyzed. The contributions of all analyzed variables to AL change were assessed using univariate and multivariate regression analyses.ResultsInitially, the results of paired t-test showed that CLT and AL were significantly increased after 12 months of ortho-k wear compared with that at baseline (P = 0.001 and < 0.001). The ACD did not change significantly after 12 months compared with that at baseline (P = 0.491). Subsequently, univariate analyses showed that a reduced rate of AL elongation was found in children who were older age at initiation of ortho-k wear (P = 0.028), had greater SE (higher degree of myopia) at baseline (p = 0.006), had thicker CCT at baseline (P = 0.04), and had greater increase of CLT (P = 0.001) in 12 months. At last, only greater SE (higher degree of myopia) and greater increase of CLT were associated with smaller increases of AL in multivariable analyses, (P = 0.003 and 0.001).ConclusionsBoth CLT and AL were significantly increased in children with overnight ortho-k wear after 12 months of follow-up. Greater baseline SE and greater increase of CLT were associated with less increase in AL during ortho-k wear in children with myopia.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO₂-NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.     

我国中空吹塑行业发展现状及“十四五”期间重点产品、工艺和设备发展方向

从原料、中空成型机和吹塑工艺等方面介绍了我国中空吹塑行业发展现状，并对电子化学品专用超洁净桶、高压储氢四型瓶、全电动中空成型机、微发泡中空成型技术和挤吹聚对苯二甲酸乙二醇酯（PET）容器等在“十四五”期间的重点产品、工艺和设备发展方向进行了展望。     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法

针对液晶屏(LCD)导光板表面缺陷检测方法存在漏检率和误检率较高,对产品表面复杂渐变的纹理结构适应性差的问题,提出一种基于改进相干增强扩散(ICED)与纹理能量测度和高斯混合模型(TEM-GMM)的LCD导光板表面缺陷检测方法。首先,构建ICED模型,基于结构张量引入平均曲率流扩散(MCF)滤波,使得相干增强扩散(CED)模型对缺陷的细线状纹理有良好的边缘保持效果,并利用相干性得到缺陷纹理增强和背景纹理抑制的滤波后图像;然后,根据Laws纹理能量测度(TEM)提取图像纹理特征,将图像的背景纹理特征作为离线阶段高斯混合模型(GMM)的训练数据,使用期望最大化(EM)算法估计GMM参数;最后,计算待检测图像各像素的后验概率,并将其作为在线检测阶段缺陷像素的判断依据。实验结果表明,该检测方法在导光颗粒随机、规则两种分布的缺陷图像测试数据组上的漏检率和误检率分别为3.27%、4.32%和3.59%、4.87%。所提检测方法适用范围广,可有效检测出LCD导光板表面划痕、异物、脏污和压伤等类型的缺陷。     

异质性环境规制对绿色全要素生产率的影响机制——基于能源消费结构的调节作用

为探讨能源消费结构调节下异质性环境规制对绿色全要素生产率的影响机制，论文利用SBM方向距离函数测得全国各省市“经济-资源-环境-社会”四位一体的绿色全要素生产率，并将环境规制工具分为命令控制型、经济激励型、治理投入型和公众参与型4类，构建了空间杜宾模型以及门槛效应模型，进而得出在能源消费结构调节下，异质性环境规制对绿色全要素生产率的直接作用、间接作用以及空间溢出效应。研究结果表明：1）经济激励型和公众参与型环境规制通过调节能源消费结构对当地和邻地的绿色全要素生产率产生影响；2）在门槛检验方面，能源消费结构对命令控制型、经济激励型和公众参与型环境规制影响绿色全要素生产率存在门槛效应，同时在能源消费结构调节下，命令控制型和经济激励型环境规制对其本身对绿色全要素生产率的间接作用存在门槛效应。建议政府在制定环境规制时要考虑到其与能源消费结构的交互作用，并且在不同能源消费水平地区采用适宜的规制方法和强度，以更好地发挥环境规制作用。     

铅锌多金属矿分段浓缩选别技术的研究与应用

通过多碎细磨、原矿分级溢流优先浓缩脱水,进行了高浓度选铅银、选锌,锌尾矿再浓缩脱水选硫、选锰,同时匹配合理的选矿药剂,原矿浓缩水回用于磨矿分级和选铅,锌尾浓缩水回用于选锌,尾矿浓缩水回用于选硫、选锰,剩余各种废水经适度处理回用于选铅和磨矿分级等,使一段磨矿细度-74μm从70%提高到80%,铅、锌、硫入选初始浓度分别提高到50%、40%、50%,选矿废水全部分质回用。铅锌硫银回收率分别提高了1.5、2.83、13.57、1.01个百分点,硫精矿主品位从38.9%提高到46.5%,设备减少40%、能耗降低25%,节约了选矿药剂消耗和废水处理费用,实现了铅锌多金属矿产资源的高效回收和节能环保。