Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Parkinson disease (PD) is a rapidly growing neurodegenerative disorder that presents a significant public health challenge in aging societies, particularly in China. Although genetic, clinical, and environmental risk factors have been well-established, the predictive value of multidimensional factors (encompassing socioeconomic determinants, prodromal functional impairments, and lifestyle) remains underexplored in Chinese populations. We used data from the China Health and Retirement Longitudinal Study (n = 13,649; PD cases = 269) to develop and validate a machine learning prediction model for PD. Candidate predictors covered cognition, socioeconomic status, comorbidities, and lifestyle. Feature selection was performed using an ensemble approach (boruta, least absolute shrinkage and selection operator, and minimum redundancy maximum relevance), followed by extreme gradient boosting (XGBoost) model development with Bayesian hyperparameter optimization. Data were randomly split into a training set (70%) and a validation set (30%) for internal validation. Model performance was evaluated using area under the receiver operating characteristic curve, decision curve analysis, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Model interpretability was evaluated using SHapley Additive exPlanations. Key predictors included cognitive impairment (memory and executive deficits), physical inactivity (low metabolic-equivalent scores), socioeconomic indicators (retirement status and financial support), educational attainment, and comorbidities (e.g., liver disease and wrist pain). In the validation set, the optimized XGBoost model achieved an area under the curve of 0.967 (95% confidence interval 0.962–0.972), sensitivity 82.0%, specificity 94.7%, positive predictive value 81.5%, negative predictive value 94.9%, and F1-score 81.7. SHapley Additive exPlanations analyses supported the consistency and relative importance of these predictors. An explainable XGBoost model integrating multidimensional national data showed excellent internal validation performance for identifying individuals with PD in China Health and Retirement Longitudinal Study. This tool may help support PD risk stratification in Chinese populations; external validation and prospective evaluation are warranted before clinical implementation.

Analysis of ground penetrating radar data from the tunnel beneath the Temple of the Feathered Serpent in Teotihuacan, Mexico, using new multi-cross algorithms

Accurate real-time density monitoring is crucial in quality control and quality assurance during the asphalt concrete (AC) pavement construction process. Ground penetrating radar (GPR) technology has shown great potential in the continuous real-time density prediction of AC pavement. However, it is not accepted as a routine method by transportation agencies in the United States due to the lack of validation under field testing conditions. In this study, three field tests were performed using GPR to estimate AC pavement density. The Al-Qadi-Lahouar-Leng model was used to predict the density from GPR signals. The reference scan method was used to remove the effect of surface moisture during construction. The gradient descent-based non-linear optimization method was used to reconstruct the overlapped GPR signals result from the use of thin AC overlay, which has been widely implemented as an AC pavement rehabilitation technique. Digital filtering and other signal processing methods were used to de-noise the signal. GPR results using the proposed methods were compared with field core data and nuclear gauge results. The results show that the proposed methods were effective in estimating in-situ AC pavement density using GPR. Continuous density estimation by installing GPR on the roller is suggested to provide real-time compaction monitoring during the AC pavement construction process.

BackgroundAcute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis.MethodsData of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model.ResultsA total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models.ConclusionThe ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, the efficacy of Random Forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) in predicting the condition rating of the nation’s bridge decks has remained unexplored. This study aims to assess the effectiveness of these algorithms for deck condition rating prediction at the national level. To achieve this, the study collected bridge data, which includes National Bridge Inventory (NBI), traffic, and climate regions gathered using Geospatial Information Science (GIS) and remote sensing techniques. Two datasets were collected: bridge data for a single year of 2020 and historical bridge data covering a five-year period from 2016 to 2020. Three models were trained using 319,404 and 1,246,261 bridge decks in the single-year bridge data and the five-year historical bridge data, respectively. Results show that the use of historical bridge data significantly improves the performance of the models compared to the single-year bridge data. Specifically, the Random Forest model achieved an overall accuracy of 83.4% and an average F1 score of 79.7%. In contrast, the XGBoost model achieved an overall accuracy of 79.4% and an average F1 score of 77.5%, while the ANN model obtained an overall accuracy of 79.7% and an average F1 score of 78.4%. Permutation-based variable importance reveals that NBI, traffic, and climate regions significantly contribute to model development. In conclusion, the Random Forest, XGBoost, and ANN models, trained using updated historical bridge data, provide useful tools for accurately predicting the condition rating of bridge decks in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.

Various techniques have been designed to maximize the use of ground penetrating radar (GPR) as an exploration tool. Improvements in signal processing are expected to further facilitate the accuracy of parameters derived from using GPR in certain geologic environments. Common-offset GPR data were collected at the Marine Corps Air Station (MCAS) in Beaufort, South Carolina, and dielectric constants were calculated following the application of the empirical mode decomposition (EMD) for dewowing GPR traces. Conventional signal processing is applied to the GPR traces to provide hydrogeophysical parameter estimates such as volumetric water content, porosity, and hydraulic conductivity. The results are validated using a coincident vertical radar profile, existing hydraulic data from direct measurements, and comparing EMD derived parameters with those non-EMD derived. The results of the comparison between the EMD and non-EMD methods show improved hydrogeophysical estimations from the EMD processed data. Dielectric constant [Formula: see text] values from the non-EMD method are outside the range of the values for all geologic materials [Formula: see text]. The subsequent parameter estimates using dielectric constants derived from non-EMD processed data yield erroneous results therefore justifying the use of EMD as a method in dewowing GPR data for quantitative analyses.

Optimization of metabolizable energy prediction models for maize in laying hens by incorporating anti-nutritional factors

Optimized machine learning models for predicting crown convergence of plateau mountain tunnels

In recent decades, various software effort estimation (SEE) algorithms have been suggested. Unfortunately, generating high-precision accuracy is still a major challenge in the context of SEE. The use of traditional techniques and parametric approaches is largely inaccurate because they produce biased and subjective accuracy. Meanwhile, none of the machine learning methods performed well. This study applies the AdaBoost ensemble learning method and random forest (RF), on the other hand the Bayesian optimization method is applied to determine the hyperparameters of this model. The PROMISE repository and the ISBSG dataset were used to build the SEE model. The developed model was comprehensively compared with four machine learning methods (classification and regression tree, k-nearest neighbor, multilayer perceptron, and support vector regression) under 3-fold cross validation (CV). It can be seen that the RF method based on AdaBoost ensemble learning and bayesian optimization outperforms this approach. In addition, the AdaBoost-based model assigns a feature importance rating, which makes it a promising tool in software effort prediction.

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.

Size estimation of underground targets from GPR frequency spectra: A deep learning approach

This paper describes a novel approach to detecting shape features of non-metallic anti-personnel land mine using certain attributes of wavelet packets and wavelets in general. Data sources included 2GHz and 10 GHz pulse ground penetrating radar (GPR) data, and 6 GHz stepped-frequency GPR data, from laboratory measurements. Targets are chosen to include rocks and other non-lethal clutter which normally present false alarms to the GPR. The GPR signals are first de-noised with an adaptive wavelet packet de-noising method, then the shape features of the APL are determined form exploiting the regularity properties of wavelets. Results are shown which indicate that the method may be applied to pulse GPR processing, but not for frequency-stepped GPRs. Preprocessing the GPR signal for clutter reduction may not always be necessary, thus simplifying the detection process.

Glass forming ability prediction of bulk metallic glasses based on fused strategy

The frequent occurrence and spread of wildfires pose a serious threat to the ecological environment and urban development. Therefore, assessing regional wildfire susceptibility is crucial for the early prevention of wildfires and formulation of disaster management decisions. However, current research on wildfire susceptibility primarily focuses on improving the accuracy of models, while lacking in-depth study of the causes and mechanisms of wildfires, as well as the impact and losses they cause to the ecological environment and urban development. This situation not only increases the uncertainty of model predictions but also greatly reduces the specificity and practical significance of the models. We propose a comprehensive evaluation framework to analyze the spatial distribution of wildfire susceptibility and the effects of influencing factors, while assessing the risks of wildfire damage to the local ecological environment and urban development. In this study, we used wildfire information from the period 2013–2022 and data from 17 susceptibility factors in the city of Guilin as the basis, and utilized eight machine learning algorithms, namely logistic regression (LR), artificial neural network (ANN), K-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM), and eXtreme gradient boosting (XGBoost), to assess wildfire susceptibility. By evaluating multiple indicators, we obtained the optimal model and used the Shapley Additive Explanations (SHAP) method to explain the effects of the factors and the decision-making mechanism of the model. In addition, we collected and calculated corresponding indicators, with the Remote Sensing Ecological Index (RSEI) representing ecological vulnerability and the Night-Time Lights Index (NTLI) representing urban development vulnerability. The coupling results of the two represent the comprehensive vulnerability of the ecology and city. Finally, by integrating wildfire susceptibility and vulnerability information, we assessed the risk of wildfire disasters in Guilin to reveal the overall distribution characteristics of wildfire disaster risk in Guilin. The results show that the AUC values of the eight models range from 0.809 to 0.927, with accuracy values ranging from 0.735 to 0.863 and RMSE values ranging from 0.327 to 0.423. Taking into account all the performance indicators, the XGBoost model provides the best results, with AUC, accuracy, and RMSE values of 0.927, 0.863, and 0.327, respectively. This indicates that the XGBoost model has the best predictive performance. The high-susceptibility areas are located in the central, northeast, south, and southwest regions of the study area. The factors of temperature, soil type, land use, distance to roads, and slope have the most significant impact on wildfire susceptibility. Based on the results of the ecological vulnerability and urban development vulnerability assessments, potential wildfire risk areas can be identified and assessed comprehensively and reasonably. The research results of this article not only can improve the specificity and practical significance of wildfire prediction models but also provide important reference for the prevention and response of wildfires.

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate the distribution of water resources for crop growth in various regions. However, the spatial variability of soil moisture is dramatic, and its time series data are highly noisy, nonlinear, and nonstationary, and thus hard to predict accurately. In this study, taking Jiangsu Province in China as an example, the data of 70 meteorological and soil moisture automatic observation stations from 2014 to 2022 were used to establish prediction models of 0–10 cm soil relative humidity (RHs10cm) via the extreme gradient boosting (XGBoost) algorithm. Before constructing the model, according to the measured soil physical characteristics, the soil moisture observation data were divided into three categories: sandy soil, loam soil, and clay soil. Based on the impacts of various factors on the soil water budget balance, 14 predictors were chosen for constructing the model, among which atmospheric and soil factors accounted for 10 and 4, respectively. Considering the differences in soil physical characteristics and the lagged effects of environmental impacts, the best influence times of the predictors for different soil types were determined through correlation analysis to improve the rationality of the model construction. To better evaluate the importance of soil factors, two sets of models (Model_soil&atmo and Model_atmo) were designed by taking soil factors as optional predictors put into the XGBoost model. Meanwhile, the contributions of predictors to the prediction results were analyzed with Shapley additive explanation (SHAP). Six prediction effect indicators, as well as a typical drought process that happened in 2022, were analyzed to evaluate the prediction accuracy. The results show that the time with the highest correlations between environmental predictors and RHs10cm varied but was similar between soil types. Among these predictors, the contribution rates of maximum air temperature (Tamax), cumulative precipitation (Psum), and air relative humidity (RHa) in atmospheric factors, which functioned as a critical factor affecting the variation in soil moisture, are relatively high in both models. In addition, adding soil factors could improve the accuracy of soil moisture prediction. To a certain extent, the XGBoost model performed better when compared with artificial neural networks (ANNs), random forests (RFs), and support vector machines (SVMs). The values of the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), Nash–Sutcliffe efficiency coefficient (NSE), and accuracy (ACC) of Model_soil&atmo were 0.69, 11.11, 4.87, 0.12, 0.50, and 88%, respectively. This study verified that the XGBoost model is applicable to the prediction of soil moisture at the provincial level, as it could reasonably predict the development processes of the typical drought event.