关键词： 基性-超基性岩   定量分类模型   铜镍矿床   成矿预测   北山地区  

摘要： 岩浆岩分类是研究岩浆岩成因及其成矿作用的基础,传统的岩浆岩二维图解分类方法,因仅有个别元素参与分类、岩石矿物学定名无法保证统一性等因素,其分类结果不够精确。本文基于地质大数据研究基本思想、方法,应用多元统计"聚类分析"和"判别分析"的算法,分别构建起"北山地区基性-超基性岩氧化物定量分类系列模型"和"北山地区基性-超基性岩氧化物+微量元素定量分类系列模型"。前者将北山地区406件基性-超基性岩样本,在10个氧化物变量空间分为2大类共9个小类,其中89%的含矿样本分布在占总样本量44%的3个小类中。后者将北山地区266件基性-超基性岩样本,在34个氧化物及微量元素变量空间分为2大类共8个小类,其中98%的含矿样本分布在占总样本量33%的2个小类中,且81%的含矿样本分布在仅占总样本量21%的Ⅰ222分类中,在该分类中含矿样本数占该分类样本的61%,则有理由认为剩余39%的样本所在岩体为找矿的有利岩体。上述二个模型分类结果中含矿样本对定量分类的高度选择性,充分证明所建定量分类系列模型对北山地区基性-超基性岩成矿岩体分类的高度有效性,为北山地区基性-超基性岩成矿岩体预测研究提供了依据。

关键词： 机器学习   天然气水合物   稳定带   支持向量机  

摘要： 随着互联网大数据发展,人工智能算法逐渐能自动学习数据特征和挖掘大数据隐藏的信息,且其预测结果具有极高的准确度和可靠性。机器学习是人工智能的核心算法,目前已应用于多个领域,在地球科学领域的应用也已兴起。天然气水合物稳定带是评估水合物资源潜力的关键参数,其准确性直接影响水合物勘探进程和结果。前人在计算水合物稳定带厚度时往往采用较为简单的模型,忽略气体组分、热导率等因素的影响,计算结果存在较大偏差。基于不同海水盐度和气体组分条件下的天然气水合物实验数据构建机器学习模型,利用机器学习算法预测天然气水合物的相平衡条件;进而结合南海北部的气体组分、热流、热导率等数据,计算得到南海北部水合物稳定带厚度。结果分析表明机器学习模型预测的水合物相平衡曲线与实验数据高度吻合,决定系数高达0.997。计算的南海北部水合物稳定带厚度与水合物钻井和地震资料揭示的结果基本一致。本研究提供了一个机器学习算法在水合物稳定带厚度估算中的应用实例,表明人工智能算法在未来天然气水合物资源预测和潜力评价中具有较大的应用前景。

关键词： Exchange rates   Forecasting   Machine learning   Purchasing power parity   Uncovered interest rate parity   Taylor-rule exchange rate models  

摘要： Using methods from machine learning we show that fundamentals from simple exchange rate models (PPP or UIRP) or Taylor-rule based models lead to improved exchange rate forecasts for major currencies over the floating period era 1973-2014 at a 1-month forecast horizon which beat the no-change forecast. Fundamentals thus contain useful information and exchange rates are forecastable even for short horizons. Such conclusions cannot be obtained when using rolling or recursive OLS regressions as used in the literature. The methods we use - sequential ridge regression and the exponentially weighted average strategy, both with discount factors - do not estimate an underlying model but combine the fundamentals to directly output forecasts. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Anatomy   Molecular fingerprints   Molecular modeling   Machine learning   Receptors  

摘要： The farnesoid X receptor (FXR) emerges as a promising drug target involved in regulating various metabolic pathways, yet some xenobiotic compounds binding to FXR would be an important determinant to induce the receptor dysfunctions that lead to undesirable side effects. Thus, it is critical to identify potential xenobiotics that disrupt normal FXR functions. In this work, five machine learning methods coupled with eight molecular fingerprints and 20 molecular descriptors were used to develop classification models for prediction of FXR binders. The built models were evaluated using the test set and two external validation sets. The best model was obtained using a combination of molecular descriptors and fingerprints, which exhibited the AUC values of 0.83 and 0.92 for the test set and the first external validation set, respectively. The overall prediction accuracy for the second external validation set with the best model was over 85%. Furthermore, several representative privileged substructures that are essential for FXR binders, such as benzimidazole, indole, and stilbene moiety, were detected using information gain and substructure frequency analysis. The applicability domain analysis via the Euclidean distance-based approach demonstrated a marked impact on the improvement of prediction accuracy. Overall, our built models could be helpful to rapidly identify potential chemicals binding to FXR.

关键词： Forest Fire danger   Geographical Information System   Artificial Neural Network   Differential Flower Pollination   Backpropagation  

摘要： Forest fire danger map at regional scale is considered of utmost importance for local authority to efficiently allocate its resources to fire prevention measures and establish appropriate land use plans. This study aims at introduce a new machine learning method, named as DFP-MnBpAnn, based on Artificial Neural Network (Ann) with a novel hybrid training algorithm of Differential Flower Pollination (DFP) and mini-match backpropagation (MnBp) for spatial modeling of forest fire danger. Tropical forest of the Lam Dong province (Vietnam) was used as case study. To achieve this task, a Geographical Information System (GIS) database of the forest fire for the study area was established. Accordingly, DFP, as a metaheuristic method, is used to optimize the weights and structure of Ann to fit the GIS database at hand. Whereas, MnBp is employed periodically during the DFP-based optimization process, in which MnBp acts as a local search aiming to accelerate both the quality of the found solutions and the convergence rate. Experimental outcomes demonstrate that the proposed DFP-MnBpAnn model is superior to other benchmark methods with satisfactory prediction accuracy (Classification Accuracy Rate = 88.43%). This fact confirms that DFP-MnBpAnn is a promising alternative for the problem of large-scale forest fire danger mapping.

摘要： This paper investigates the possibility of predicting the time series of the geomagnetic index Dst. The prediction is based on parameters of the solar wind and interplanetary magnetic field measured at Lagrange point L1 within the Advanced Composition Explorer (ACE) spacecraft experiment using machine learning methodsartificial neural networks: classical perceptrons, recurrent networks of long short-term memory (LSTM), and committees of predictive models. Ultimately, the best results have been obtained using heterogeneous committees based on neural networks of both types.

关键词： Heterogeneity brain aging   Functional connectivity   Structural MRI   Functional connectivity   Resting-state fMRI  

摘要： Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns. (C) 2018 Elsevier Inc. All rights reserved.

关键词： agriculture   machine learning   climate change impacts  

摘要： Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant of a deep neural network, which can simultaneously account for complex nonlinear relationships in high-dimensional datasets, as well as known parametric structure and unobserved cross-sectional heterogeneity. Using data on corn yield from the US Midwest, we show that this approach outperforms both classical statistical methods and fully-nonparametric neural networks in predicting yields of years withheld during model training. Using scenarios from a suite of climate models, we show large negative impacts of climate change on corn yield, but less severe than impacts projected using classical statistical methods. In particular, our approach is less pessimistic in the warmest regions and the warmest scenarios.

关键词： Multiple stressors   Cumulative impacts   Regression   Spatial prediction   Model selection   Kelp forests  

摘要： Coastal ecosystems are exposed to multiple anthropogenic stressors such as fishing, pollution, and climate change. Ecosystem-based coastal management requires understanding where the combination of multiple stressors has large cumulative effects and where actions to address impacts are most urgently needed. However, the effects of multiple stressors on coastal and marine ecosystems are often non-linear and interactive. This complexity is not captured by commonly used spatial models for mapping human impacts. Flexible statistical and machine learning models like random forests have thus been used as an alternative modeling approach to identify important stressors and to make spatial predictions of their combined effects. However, tests of such models' prediction skill have been limited. Therefore, we tested how well ten statistical and machine learning methods predicted three ecological indicators of coastal marine ecosystem condition (kelp biodiversity, fish biomass, and rocky intertidal biodiversity) off California, USA. Spatial data representing anthropogenic stressors and ocean uses as well as natural gradients were used as predictors. The models' prediction errors were estimated by double spatial block cross-validation. The best models achieved mean squared errors about 25% lower than a null model for kelp biodiversity and fish biomass;none of the tested models worked well for rocky intertidal biodiversity. The models captured general trends, but not local variability of the indicators. For kelp biodiversity, the best performing method was principal components regression. For fish biomass, the best performing method was boosted regression trees. However, after tuning, this model did not include any interactions between stressors, and ridge regression (a constrained linear model) performed almost as well. While in theory flexible machine learning methods are required to represent the complex stressor-ecosystem state relationships revealed by experimental ecologis