关键词： Electrons  

摘要： Electron stopping power (SP) is of great importance in theoretical and applied research areas specifically for Monte Carlo simulation studies in many microanalysis and surface analysis techniques, radiation dosimetry, and the design of particle detectors. However, experimental data are available for a dozen elemental materials only. On the other hand, the Bethe analytical expression of the SP is applicable at high energies only whereas no generally accepted formula exists at lower energies. We employed ensemble machine learning (ML) methods with the available experimental database for the prediction of SPs of electrons with energies from 100 keV down to 1 eV, in elements over the entire periodic table. With a small training database for electron SPs, we applied various algorithms individually as well as their ensembles, which have the credibility to enhance the prediction accuracy in the case of a small training database. Based on the model's performance evaluation tests, we concluded that the stacked generalization is more accurate than the individual algorithms. Using this method, we were able to predict the electron SPs for 54 elements (in total) including 12 elements that were present in the training database as well as for 42 elements beyond the training database over a wide energy range (1 eV to 100 keV). Compared to other theoretical approaches, the ML predicted SPs show very good agreement with the available experimental data at all energies. Moreover, unlike other theoretical approaches, the ML model does not need dielectric function data and other physical parameters which involve complex calculations. Using our ML model, we have predicted SPs for a further 14 elements for which no theoretical SPs are available because of the lack of good dielectric function data.

关键词： 冲击地压   长期预测   短期预警   机器学习   数据驱动  

摘要： 传统冲击地压预警预测通常利用地球物理方法监测冲击地压的一些前兆信号,采用人为定义和提取参数的综合指数法对冲击地压发生的可能性进行评估。目前,研究人员尝试采用数据驱动方法,利用机器学习技术,克服传统冲击地压长期预测所带来的问题,通过分析国内外相关文献,对长期预测和短期预警方法及存在的问题、机器学习方法、冲击地压短期预警及长期预测的应用进展进行概述,同时分析了传统的经验驱动和机理驱动与机器学习的数据驱动在冲击地压预测预警方面的联系与差异,最后,总结机器学习在该领域存在的问题及挑战,并对未来冲击地压预测预防的技术进行展望。

关键词： 商铺租金   空间布局   机器学习   广州市  

摘要： 研究目的:通过引入机器学习方法,利用地理时空大数据研究精细尺度的商铺租金分布,以弥补传统基于计量模型的租金分析中样本量不足、要素量化方式主观、模型形式僵化等问题。研究方法:研究利用网络爬虫技术获取相关时空大数据,随后采用密度制图、空间句法分析等量化相关指标,并通过LASSO模型筛选影响因子,最后采用机器学习方法对社区尺度商铺租金进行空间布局分析。研究结果:基于机器学习方法的广州市中心城区社区尺度商铺租金布局分析能较好拟合估计值和观测值,其分析结果显示广州市中心城区中荔湾区、越秀区、天河区和海珠区各自形成了成熟的高租金核心,商铺租金从高值区域向外逐渐下降。研究结论:该研究方法可以应用于房租、房价空间分布估计等领域,研究成果可作为基准地价更新、城市异值空间发现等的参考。

关键词： 机器学习   粒子重构   层析粒子图像测速   卷积神经网络   重构质量  

摘要： 通过三维粒子重构获取粒子场的分布情况是层析粒子图像测速的关键步骤,有限二维投影下的三维粒子重构是一个欠定的反问题,其精确解往往很难得到。一般情况下,可以通过优化方法得到近似解。为了获取质量更高的粒子场并用于层析粒子图像测速,提出了一种基于卷积神经网络(Convolutional Neural Networks,CNN)的粒子重构方法。所提出的技术可以从基于传统的代数重构技术(Algebraic Reconstruction Technique,ART)的方法所得到的粗略粒子分布中进一步提高粒子重构质量。与现有的基于ART的算法相比,新技术在重构质量方面有了显著的改进,可以有效剔除虚假粒子并更准确地还原粒子形状,并且在粒子浓度较稠密的情况下计算速度至少快了一个数量级。

关键词： ncRNA   Protein   ncRNA-protein interaction   Machine learning methods   Predictive models  

摘要： Recent transcriptomics and bioinformatics studies have shown that ncRNAs can affect chromosome structure and gene transcription, participate in the epigenetic regulation, and take part in diseases such as tumorigenesis. Biologists have found that most ncRNAs usually work by interacting with the corresponding RNA-binding proteins. Therefore, ncRNA-protein interaction is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, machine-learning methods for predicting ncRNA-protein interactions are increasingly favored by the researchers. In this review, we summarize several machine learning predictive models of ncRNA-protein interactions over the past few years, and briefly describe the characteristics of these machine learning models. In order to optimize the performance of machine learning models to better predict ncRNA-protein interactions, we give some promising future computational directions at the end.

关键词： hepatocellular carcinoma (HCC)   contrast-enhanced ultrasound (CEUS) images   multimodal combined CNN classifiers   feature level fusion   classifier level fusion   decision level fusion  

摘要： Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The most reliable method for HCC diagnosis is the needle biopsy, which is an invasive, dangerous method. In our research, specific techniques for non-invasive, computerized HCC diagnosis are developed, by exploiting the information from ultrasound images. In this work, the possibility of performing the automatic diagnosis of HCC within B-mode ultrasound and Contrast-Enhanced Ultrasound (CEUS) images, using advanced machine learning methods based on Convolutional Neural Networks (CNN), was assessed. The recognition performance was evaluated separately on B-mode ultrasound images and on CEUS images, respectively, as well as on combined B-mode ultrasound and CEUS images. For this purpose, we considered the possibility of combining the input images directly, performing feature level fusion, then providing the resulted data at the entrances of representative CNN classifiers. In addition, several multimodal combined classifiers were experimented, resulted by the fusion, at classifier, respectively, at the decision levels of two different branches based on the same CNN architecture, as well as on different CNN architectures. Various combination methods, and also the dimensionality reduction method of Kernel Principal Component Analysis (KPCA), were involved in this process. These results were compared with those obtained on the same dataset, when employing advanced texture analysis techniques in conjunction with conventional classification methods and also with equivalent state-of-the-art approaches. An accuracy above 97% was achieved when our new methodology was applied.

关键词： weighted mean temperature   Global Navigation Satellite System   machine learning   random forest   backpropagation neural network   generalized regression neural network  

摘要： As a crucial parameter in estimating precipitable water vapor from tropospheric delay, the weighted mean temperature (T-m) plays an important role in Global Navigation Satellite System (GNSS)-based water vapor monitoring techniques. However, the rigorous calculation of T-m requires vertical profiles of temperature and water vapor pressure that are difficult to acquire in practice. As a result, empirical models are widely used but have limited accuracy. In this study, we use three machine learning methods, i.e., random forest (RF), backpropagation neural network (BPNN), and generalized regression neural network (GRNN), to improve the estimation of empirical T-m in China. The basic idea is to use the high-quality radiosonde observations estimated T-m to calibrate and optimize the empirical T-m through machine learning methods. Validating results show that the three machine learning methods improve the T-m accuracy by 37.2%, 32.6%, and 34.9% compared with the global pressure and temperature model 3 (GPT3). In addition to the overall accuracy improvement, the proposed methods also mitigate the accuracy variations in space and time, guaranteeing evenly high accuracy. This study provides a new idea to estimate T-m, which could potentially contribute to the GNSS meteorology.

关键词： vegetation growth   forecasting   XGBoost   growing season   crop yield   food security  

摘要： To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted;however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China;the model represented seasonal variations in NDVI well. The coefficient of determination (R-2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R-2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.

关键词： gross primary production (GPP)   sun-induced chlorophyll fluorescence (SIF)   GOSIF  

摘要： The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05 degrees spatial resolution for the period 2001-2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R-2) = 0.58, root mean square error (RMSE) = 2.74 g C center dot m(-2), bias = -0.34 g C center dot m(-2)) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.

关键词： Acute hypotensive episodes (AHE)   Non-invasive physiological parameters   (NIPPs)   Machine learning algorithms   Observation window   Prediction gap   Feature extraction methods   Prediction   Data mining  

摘要： Background and objectives: Accurate prediction of acute hypotensive episodes (AHE) is fundamentally important for timely and appropriate clinical decision-making, as it can provide medical professionals with sufficient time to accurately select more efficient therapeutic interventions for each specific condition. However, existing methods are invasive, easily affected by artifacts and can be difficult to acquire in a pre-hospital setting. Methods: In this study, 1055 patients' records were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database (MIMIC II), comprising of 388 AHE records and 667 non-AHE records. Six commonly used machine learning algorithms were selected and used to develop an AHE prediction model based on features extracted from seven types of non-invasive physiological parameters. Results: The optimal observation window and prediction gap were selected as 300 minutes and 60 minutes, respectively. For GBDT, XGB and AdaBoost, the optimal feature subsets contained only 39% of the overall features. An ensemble prediction model was developed using the voting method to achieve a more robust performance with an accuracy (ACC) of 0.822 and area under the receiver operating characteristic curve (AUC) of 0.878. Conclusion: A novel machine learning method that uses only noninvasive physiological parameters offers a promising solution for easy and prompt AHE prediction in widespread scenario applications, including pre-hospital and in-hospital care. (c) 2020 Elsevier B.V. All rights reserved.