关键词： DERA   Aldolase   Protein engineering   Machine learning   Crystal structure determination   C–   C bond formation   Biocatalysis  

摘要： In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.***.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broad set of mutations was directed to 24 amino acid positions in the active site or in the close vicinity, based on the 3D complex structure of the E. coli DERA wild-type aldolase. The specific activity of the DERA variants containing one to three amino acid mutations was characterised using three different substrates. A novel machine learning (ML) model utilising Gaussian processes and feature learning was applied for the 3rd mutagenesis round to predict new beneficial mutant combinations. This led to the most clear-cut (two- to threefold) improvement in acetaldehyde (C2) addition capability with the concomitant abolishment of the activity towards the natural donor molecule glyceraldehyde-3-phosphate (C3P) as well as the non-phosphorylated equivalent (C3). The Ec DERA variants were also tested on aldol reaction utilising formaldehyde (C1) as the donor. Ec DERA wild-type was shown to be able to carry out this reaction, and furthermore, some of the improved variants on acetaldehyde addition reaction turned out to have also improved activity on formaldehyde.

关键词： Shock-shock interaction   Machine learning   Hypersonic flow   Impinging jet   Genetic programming   Triple point  

摘要： To predict the maximum heating load induced by shock-shock interaction more reliably and accurately, the geometrical scale of the overall wave configuration of shock-shock interaction is very useful. However, it is hard to be solved with traditional shock theory due to its complexity. The results of numerical and experimental studies are case-by-case. Concise formulas correlating the geometrical scales of shock-shock interaction with the given flow parameters are desired but still unavailable. In the present work, a set of correlative formulas for the triple-points' coordinates of type IVa, IV, and III shock-shock interaction are derived by multilevel block building algorithm, a functional machine learning method. The key flow structure of shock-shock interaction, i.e., the supersonic impinging jet, can be determined with the help of shock theories and the formulas. In addition, the transition criteria respectively for the overall wave configuration transitions of type IVa <-> type IV and type IV <-> type III shock-shock interaction can be obtained by the machine learning based method. (c) 2020 Elsevier Masson SAS. All rights reserved.

关键词： Partial discharges   Industries   Condition monitoring   Training   Machine learning algorithms   Focusing   Real-time systems   condition monitoring   deep learning   partial discharge   intelligent fault diagnostics   machine learning  

摘要： This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

关键词： soil salinity   remote sensing   machine learning   predictive mapping  

摘要： Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil;however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R-2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of

关键词： land subsidence   persistent scatters interferometry   remote sensing   machine learning  

摘要： In the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land subsidence. Here we choose groundwater level change, thickness of the Quaternary deposit and index-based built-up index (IBI) as influencing factors, and we use the influence factors to predict the subsidence amount in the Beijing Plain. The Sentinel-1 radar images and the persistent scatters interferometry (PSI) were adopted to obtain the information of land subsidence. By using Google Earth Engine platform and Landsat8 optical images, IBI was extracted. Groundwater level change and thickness of the Quaternary deposit were obtained from hydrogeological data. Machine learning algorithms Linear Regression and Principal Component Analysis (PCA) were used to investigate the relationship between land subsidence and influencing factors. Based on the results obtained by Linear Regression and PCA, a suitable machine learning algorithm was selected to predict the subsidence amount in the Beijing Plain in 2018 through influencing factors. In this study, we found that the maximum subsidence rate in the Beijing Plain had reached 115.96 mm/y from 2016 to 2018. The land subsidence was serious in eastern Chaoyang and northwestern Tongzhou. In addition, the area where thickness of the Quaternary deposit reached 150-200 m was prone to more serious land subsidence in the Beijing Plain. In groundwater exploitation, the second confined aquifer had the greatest impact on land subsidence. Through Linear Regression and PCA, we found that the relationship between land subsidence and influencing factors was nonlinear. XGBoost was feasible to predict subsidence amount. The prediction accuracy of XGBoost on the subsidence amount reached 0.9431, and the mean square error was controlled at 15.97. By u

关键词： Sulfur solubility   Natural gas   Sour gases   Multilayer perceptron (MLP)   Cascaded forward neural network (CFNN)   Gene expression programming (GEP)  

摘要： Accurate determination of sulfur solubility in pure hydrogen sulfide (H2S) and sour gas mixtures has a leading role and a fundamental importance in handling and addressing sulfur deposition issues. In this study, rigorous paradigms based on two artificial neural network (ANN) types, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN), optimized by Levenberg-Marquardt (LM) algorithm were proposed as machine learning (ML) modeling tools to predict the solubility of sulfur in sour gas mixtures and pure H2S. Besides, explicit and simple-to-use correlations were established using gene expression programming (GEP). The paradigms derived from the methods aforementioned were developed using widespread experimental database. The obtained results indicated that the outcomes gained from the proposed MLP, CFNN and GEP-based correlations are in a high coherence and agreement with the experimental data. In addition, it was found that among the all suggested schemes, CFNN models are the most accurate paradigms for estimating the solubility of sulfur in sour gas mixtures and pure H2S with root mean square error (RMSE) of 0.0232 and 3.8101, respectively. Furthermore, a comparison between the performance of CFNN and the prior alternatives demonstrated that the CFNN models predict the solubility of sulfur in sour gas mixtures and pure H2S more accurately. Moreover, based on the trend analysis, it was concluded that the predictions of CFNN follow the real tendency of sulfur solubility in pure H2S and sour gas mixtures with respect to the input parameters. Besides, the sensitivity analysis dictated that pressure and temperature have the most significant impact on sulfur solubility calculation in pure H2S and sour gas mixtures. The results reported in this investigation revealed that implication of the considered soft computing approaches in the estimation of sulfur solubility in sour gas mixtures and pure H2S can lead to the generation of more reliable predicti

关键词： Coronary artery disease   fluid-structure interaction   machine learning   plaque vulnerability prediction   intravascular ultrasound  

摘要： Plaque vulnerability prediction is of great importance in cardiovascular research. In vivo follow-up intravascular ultrasound (IVUS) coronary plaque data were acquired from nine patients to construct fluid-structure interaction models to obtain plaque biomechanical conditions. Morphological plaque vulnerability index (MPVI) was defined to measure plaque vulnerability. The generalized linear mixed regression model (GLMM), support vector machine (SVM) and random forest (RF) were introduced to predict MPVI change (Delta MPVI = MPVIfollow-up-MPVIbaseline) using ten risk factors at baseline. The combination of mean wall thickness, lumen area, plaque area, critical plaque wall stress, and MPVI was the best predictor using RF with the highest prediction accuracy 91.47%, compared to 90.78% from SVM, and 85.56% from GLMM. Machine learning method (RF) improved the prediction accuracy by 5.91% over that from GLMM. MPVI was the best single risk factor using both GLMM (82.09%) and RF (78.53%) while plaque area was the best using SVM (81.29%).

关键词： 链路状态   网络链路   网络可靠性   机器学习方法   编队控制   无人机编队   量化计算   预警信息  

摘要： 无人机编队网络具有高动态特性,链路状态变化频繁,影响网络可靠性。本文提出以投递率表征链路状态,通过量化计算,采用机器学习方法拟合链路时变规律,预测未来某时段链路状态,为编队控制体系提供预警信息。本文算法经QualNet仿真实现,预测效果较好。

关键词： 石油化工   有毒有害气体监测   权值模型   消防指挥  

摘要： 针对近年来石油化工厂区、危险化学品存放港口火灾事故多发,消防员在灭火救援过程中多有人员伤亡的现状,提出一种专用于消防救援现场的有毒有害气体监测系统,阐述了系统的构成、工作原理,建立了现场有毒有害气体监测区域危险性和消防员现场作业危险性权值模型。应用机器学习方法对数据模型进行训练,在灭火救援现场为指挥员调度指挥提供科学的辅助决策支撑,有利于保障现场参战消防员的生命安全。

关键词： 傅立叶级数   机器学习   预测   小行星  

摘要： 利用傅立叶级数对散点小行星光变数据进行曲线拟合,以获得小行星表示参数,选取傅立叶级数为6的拟合方式对单体或双体小行星光变曲线进行分类,并用机器学习算法中的SVM和决策树建立预测模型。检验结果表明,SVM模型对单体和双体小行星的预测正确率达到95%,相较于决策树正确率提高了10%,为从小行星实际观测数据直接推测双体小行星潜在相关应用提供了参考。