关键词： active thermography (AT)   finite element method (FEM)   non-destructive testing (NDT)   quality assessment (QA)   machine learning (ML)   additive materials (AM)  

摘要： The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

关键词： MeSH terms   Electronic health records   Health behavior   Hypertension   Life style   Machine learning  

摘要： Just under half of the 85.7 million US adults with hypertension have uncontrolled blood pressure using a hypertension threshold of systolic pressure ≥ 140 or diastolic pressure ≥ 90. Uncontrolled hypertension increases risks of death, stroke, heart failure, and myocardial infarction. Guidelines on hypertension management include lifestyle modification such as diet and exercise. In order to improve hypertension control, it is important to identify predictors of lifestyle modification assessment or advice to tailor future interventions using these effective, low-risk interventions. Electronic health record data from 14,360 adult hypertension patients at an academic medical center were analyzed using statistical and machine learning methods to identify predictors and timing of lifestyle modification. Multiple variables were statistically significant in analysis of lifestyle modification documentation at multiple time points. Random Forest was the best machine learning method to classify lifestyle modification documentation at any time with Area Under the Receiver Operator Curve (AUROC) 0.831. Logistic regression was the best machine learning method for classifying lifestyle modification documentation at ≤3 months with an AUROC of 0.685. Analyzing narrative and coded data from electronic health records can improve understanding of timing of lifestyle modification and patient, clinic and provider characteristics that are correlated with or predictive of documentation of lifestyle modification for hypertension. This information can inform improvement efforts in hypertension care processes, treatment implementation, and ultimately hypertension control.

关键词： artificial intelligence   supervised machine learning   classification   phenotypic profiles  

摘要： There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for building the machine learning models. We propose that unsupervised exploratory methods should first be applied to the data set to gain a better insight into the quality of the data. This improves the selection and labeling of data for creating training sets before the application of machine learning. We demonstrate this using a high-content genome-wide small interfering RNA screen. We perform an unsupervised exploratory data analysis to facilitate the identification of four robust phenotypes, which we subsequently use as a training set for building a high-quality random forest machine learning model to differentiate four phenotypes with an accuracy of 91.1% and a kappa of 0.85. Our approach enhanced our ability to extract new knowledge from the screen when compared with the use of unsupervised methods alone.

关键词： Decision trees  

摘要： Attempts using geochemical data to classify quarry sources which provided reactive rock aggregate, composed of Carboniferous aged pyritic mudrocks and limestones, which has caused structural damage to over 12, 500 homes across Ireland have not yet succeeded. In this paper, a possible solution to this problem is found by performing machine learning models, such as Logistic regression and Random Forest, upon a geochemical dataset obtained through the scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) and Laser ablation-quadrupole-inductively couple plasma mass spectrometry (LA-Q-ICPMS) of pyrite and Isotope ratio mass spectrometry (IRMS) of bulk rock aggregate, to predict quarry source location. When comparing the classification scores, the LA-Q-ICPMS dataset achieved the highest average classification score of 55.38% for Random Forest and 67.73% for Logistic regression based on 10-fold cross validation testing. As a result, this dataset was then used to classify a set of known unknown samples and achieved average classification accuracies of 40.30% for random forest and 66.80% for logistic regression, based on a systematic train-test procedure. There is scope to enhance these classification scores to an accuracy of 100% by combining the geochemical datasets together. However, due to the difficulty in linking pyrites analysed by SEM-EDS to those analysed by LA-Q-ICPMS, and relating a bulk rock analytical technique (IRMS) to mineral geochemistry (SEM-EDS, LAQ-ICPMS), median values have to be used when combining IRMS (Fe, S) and SEM-EDS (TS and delta S-34) datasets with LA-Q-ICPMS data. Therefore, if these combined datasets were used as part of an applied quarry classification system, statistically meaningful mean values taken from a near normally distributed dataset would have to be used in order to accurately represent the quarry composition.

关键词： GF-5   LAI   feature selection   machine learning  

摘要： Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluat

关键词： Species occupancy   Imperfect detection   Geographic filtering   Spatial and detection sampling bias   Boosted regression trees   Species distribution modeling   Aedes albopictus  

摘要： The identification and mitigation of sampling biases is commonly overseen in species distribution modeling, even though bias can seriously compromise the validity of modeling outcomes. Here we propose methods to 1) detect and mitigate spatial and detection sampling biases in the use of machine learning methods for modeling species occurrence and 2) assess the magnitude of bias and the effectiveness of bias mitigation on modeling prediction, variable importance, and model performance. We illustrate these techniques through the calibration of boosted decision trees for the prediction of annual occurrences of Aedes albopictus, an invasive disease vector, in South-East Pennsylvania between 2001 and 2015. Methods consist of the application of spatial filters and the assignment of sampling reliability weights to observed locations. We tested the performance of spatial bias mitigation by comparing the frequency distribution obtained for predictors before and after filtering with the distribution that would be obtained under an ideal sampling design. We also tested the performance of detection bias mitigation by comparing the importance of variables representing detection bias before and after the assignment of reliability weights. Results show that spatial filtering reduced differences between the frequency distribution obtained with the unfiltered data and the distribution that would be obtained under a reference sampling design. The assignment of sampling reliability weights to observations reduced the relative influence of detection bias on fitted models. The mitigation of spatial bias had a larger effect on modeling prediction and accuracy estimates compared to detection bias mitigation. Spatial sampling bias mitigation largely tended to reduce the number of years of predicted A. albopictus occurrence while detection bias mitigation tended to increase it. Our results highlight the importance of identifying, quantifying and mitigating observation biases as a standard pr

关键词： Privacy leakage   Brain-computer interface   BCI   EEG   BCI games  

摘要： With the digitization of almost every aspect of our lives, privacy leakage in cyber space has become a pressing concern. Brain-Computer Interface (BCI) systems have become more popular in recent years and are now being used for a variety of applications. BCI data represents an individual's brain activity at a given time. Like many other kinds of data, BCI data can be utilized for malicious purposes. Electroencephalography (EEG) is one of the most popular brain activity acquisition methods of BCI applications. More specifically, BCI games, represent one of the main EEG applications. However, a malicious BCI application (e.g. game) could allow an attacker to take advantage of an unsuspecting user happily enjoying a game and record the user's brain activity;by analyzing this data, the attacker can infer private information and characteristics regarding the user, without his/her consent or awareness. This study is the first to demonstrate the ability to predict and infer meaningful personality traits and cognitive abilities by analyzing resting-state EEG (rsEEG) recordings of an individual's brain activity using a variety of machine learning methods. A comprehensive set of raw rsEEG scans, along with the dissociation level and executive function (EF) performance measures, for the 162 subjects were used in our evaluation. The best results we achieved were an accuracy of 73% for dissociation classification and less than 16% mean absolute error in predicting performance for all examined EFs. These encouraging results are better than those presented in prior research, both in terms of accuracy and data-validity and dataset size. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Conditioned Latin hypercube sampling   Soil mapping   Representativeness   Sample randomness  

摘要： Sampling design plays an important role in soil survey and soil mapping. Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil mapping. cLHS samples are randomly selected in each stratum of environmental variables, thus the produced sample sets can vary significantly at different runs with the same sample size. Although variation of mapping accuracies caused by the randomness of cLHS has been realized and qualitatively mentioned in past studies. However, how the randomness of cLHS could quantitatively influence mapping accuracy has rarely been examined. In this study, we conducted experiments to examine how the sample randomness quantitatively influence soil mapping accuracy with different sample sizes, and analyzed the possible reasons from a pedogenesis perspective. The results showed that the largest range of mapping accuracies of 500 repeats was 39.5% at a sample density of 2.59 point/ km(2), while the smallest range was 7.3% at the maximum sample size with a sample density of 32.47 point/km(2). The sample density for satisfactory prediction accuracies in our study area was at least 10.06 Point/km(2). The results showed that both the allocation of sample points to each soil series and the typicality of sample points played important roles in mapping accuracies. But the deep reasons causing the unstable performance of cLHS at small sample sizes were the imbalanced class distribution of soil series and the overlap between soil series in the distribution of environmental covariates. Researchers need to be cautious about the output when applying cLHS with small sampling densities. Some effective approaches to address this issue include increasing the sample size, checking the sample allocations of a cLHS design with the assistance of legacy soil maps, or adding the legacy soil map as a variable during sampling design. When the sampling resources and legacy soil maps are limited for an area, fuzz

关键词： Monte Carlo simulations   Gold   Metal clusters   Molecular structure   Potential energy  

摘要： We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au-38(SR)(24) by using previously published, density functional theory (DFT) based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and optical properties of Au-38(SR)(24). Our ML implementation strategy allows for generalization and accuracy control of distance-based ML models for complex nanostructures having several chemical elements and interactions of varying strength.

关键词： Procrustes analysis   Gamma test   Solar radiation   Input selection   Non-linear systems  

摘要： Selection of optimal model inputs is a challenging issue particularly for non-linear and dynamic systems. In this study, a new input selection method, procrustes analysis (PA), was implemented and compared with gamma test (GT) for estimating daily global solar radiation (Rs). The PA and GT were applied for modeling with the non-linear models of artificial neural networks (ANNs) and support vector machines (SVMs). Goodness-of-fit of the models was evaluated by the coefficient of correlation (CC), root-mean-square error (RMSE), and Nash-Sutcliffe model efficiency coefficient (NS). The uncertainty of the model outputs was determined using 95PPU% (p-factor) and d-factor. In this study, we used maximum wind speed, mean wind speed, maximum temperature, minimum temperature, mean temperature, maximum sea surface pressure, minimum sea surface pressure, mean sea surface pressure, mean vapor pressure, total rainfall, maximum cloudiness, mean cloudiness, maximum humidity, minimum humidity, mean humidity, sunshine hours, evaporation, mean dew point temperature, mean wet point temperature, maximum air pressure, minimum air pressure, mean air pressure, and mean vapor saturation as input variables. Maximum and mean temperature;maximum wind speed;maximum, minimum, and mean sea surface pressure;maximum, minimum, and mean air pressure;mean vapor pressure;mean cloudiness;mean humidity;sunshine hours;mean dew point temperature;mean wet point temperature;and mean vapor saturation pressure were identified as significant input variables by GT in five or more of the eight studied stations. Also, mean air pressure, mean cloudiness, and mean temperature were identified as significant input variables for Rs modeling by the PA method for more than four stations. Results indicated that although ANN-GT and SVM-GT showed better goodness-of-fit metrics, ANN-PA and SVM-PA had lower uncertainties for estimating Rs. According to the obtained results, almost all models showed that the higher the bandwi