Search Results (3,712)

Surgeons face a significant challenge due to the heat generated during drilling, as excessive temperatures at the bone–tool interface can lead to irreversible damage to the regenerative soft tissue and result in thermal osteonecrosis. While previous studies have explored the use of machine learning to predict the temperature rise during bone drilling, this in vitro study introduces a comprehensive approach by combining the Response Surface Methodology (RSM) with advanced machine learning techniques. The main objective lies in the comprehensive evaluation and comparison of support vector machine (SVM) and random forest (RF) models specifically for the optimization of the bone drilling parameters to prevent thermal bone necrosis. A total of 27 experiments were conducted using a multi-level factorial method, with analysis performed via the Minitab software version 19.1. Performance metrics such as the mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were used to assess model accuracy. The RF model emerged as the most effective, with R² values of 94.2% for testing and 97.3% for training data, significantly outperforming other models in predicting temperature fluctuations. This study demonstrates the superior predictive capabilities of the RF model and offers a robust framework for the optimization of surgical procedures to mitigate the risk of thermal damage. Full article

(1) Background: Urinary tract infection (UTI) is a leading cause of emergency department visits and hospital admissions. Despite many studies identifying UTI-related risk factors for bacteremia or sepsis, a significant gap remains in developing predictive models for in-hospital mortality or the necessity for emergent intensive care unit admission in the emergency department. This study aimed to construct interpretable machine learning models capable of identifying patients at high risk for critical outcomes. (2) Methods: This was a retrospective study of adult patients with urinary tract infection (UTI), extracted from the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database. The critical outcome is defined as either in-hospital mortality or transfer to an intensive care unit within 12 h. ED visits were randomly partitioned into a 70%/30% split for training and validation. The extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms were constructed using variables selected from the stepwise logistic regression model. The XGBoost model was then compared to the traditional model and clinical decision rules (CDRs) on the validation data using the area under the curve (AUC). (3) Results: There were 3622 visits among 3235 unique patients diagnosed with UTI. Of the 2535 patients in the training group, 836 (33%) experienced critical outcomes, and of the 1087 patients in the validation group, 358 (32.9%) did. The AUCs for different machine learning models were as follows: XGBoost, 0.833; RF, 0.814; and SVM, 0.799. The XGBoost model performed better than others. (4) Conclusions: Machine learning models outperformed existing traditional CDRs for predicting critical outcomes of ED patients with UTI. Future research should prospectively evaluate the effectiveness of this approach and integrate it into clinical practice. Full article

The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables. Full article

The accurate and extensive monitoring of soil salinization is essential for sustainable agricultural development. It is difficult for single remote sensing data (satellite, unmanned aerial vehicle) to simultaneously meet the requirements of wide-scale and high-precision soil salinity monitoring. Therefore, this paper adopts the upscaling method to upscale the unmanned aerial vehicle (UAV) data to the same pixel size as the satellite data. Based on the optimally upscaled UAV data, the satellite model was corrected using the numerical regression fitting method to improve the inversion accuracy of the satellite model. The results showed that the accuracy of the original UAV soil salinity inversion model (R² = 0.893, RMSE = 1.448) was higher than that of the original satellite model (R² = 0.630, RMSE = 2.255). The satellite inversion model corrected with UAV data had an accuracy of R² = 0.787, RMSE = 2.043, and R² improved by 0.157. The effect of satellite inversion correction was verified using a UAV inversion salt distribution map, and it was found that the same rate of salt distribution was improved from 75.771% before correction to 90.774% after correction. Therefore, the use of UAV fusion correction of satellite data can realize the requirements from a small range of UAV to a large range of satellite data and from low precision before correction to high precision after correction. It provides an effective technical reference for the precise monitoring of soil salinity and the sustainable development of large-scale agriculture. Full article

One of the most important and widespread corn/maize virus diseases is maize dwarf mosaic (MDM), which can be induced by sugarcane mosaic virus (SCMV). This study explores a machine learning analysis of five-band multispectral imagery collected via an unmanned aerial system (UAS) during the 2021 and 2022 seasons for SCMV disease detection in corn fields. The three primary objectives are to (i) determine the spectral bands and vegetation indices that are most important or correlated with SCMV infection in corn, (ii) compare spectral signatures of mock-inoculated and SCMV-inoculated plants, and (iii) compare the performance of four machine learning algorithms, including ridge regression, support vector machine (SVM), random forest, and XGBoost, in predicting SCMV during early and late stages in corn. On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and red-edge bands and lower reflectance for near-infrared as compared to mock-inoculated samples. Across both years, the XGBoost regression model performed best for predicting disease incidence percentage (R² = 0.29, RMSE = 29.26), and SVM classification performed best for the binary prediction of SCMV-inoculated vs. mock-inoculated samples (72.9% accuracy). Generally, model performances appeared to increase as the season progressed into August and September. According to Shapley additive explanations (SHAP analysis) of the top performing models, the simplified canopy chlorophyll content index (SCCCI) and saturation index (SI) were the vegetation indices that consistently had the strongest impacts on model behavior for SCMV disease regression and classification prediction. The findings of this study demonstrate the potential for the development of UAS image-based tools for farmers, aiming to facilitate the precise identification and mapping of SCMV infection in corn. Full article

Increasing tenure security is essential for promoting safe and inclusive urban development and achieving Sustainable Development Goals. However, assessment of tenure security relies on conventional census and survey statistics, which often fail to capture the dimension of perceived tenure insecurity. This perceived tenure insecurity is crucial as it influences local engagement and the effectiveness of policies. In many regions, particularly in the Global South, these conventional methods lack the necessary data to adequately measure perceived tenure insecurity. This study first used household survey data to derive variations in perceived tenure insecurity and then explored the potential of Very-High Resolution (VHR) satellite imagery and spatial data to assess these variations in urban deprived areas. Focusing on the city of Kigali, Rwanda, the study collected household survey data, which were analysed using Multiple Correspondence Analysis to capture variations of perceived tenure insecurity. In addition, VHR satellite imagery and spatial datasets were analysed to characterize urban deprivation. Finally, a Random Forest regression model was used to assess the relationship between variations of perceived tenure insecurity and the spatial characteristics of urban deprived areas. The findings highlight the potential of geospatial information to estimate variations in perceived tenure insecurity within urban deprived contexts. These insights can inform evidence-based decision-making by municipalities and stakeholders in urban development initiatives. Full article

Understanding the water and carbon cycles within terrestrial ecosystems is crucial for effective monitoring and management of regional water resources and the ecological environment. However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our study, we leveraged multiple variables (vegetation growth, surface moisture, radiative energy, and other relative variables) as inputs for various regression algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN), to estimate water (ET) and carbon fluxes (NEE) in the Haihe River Basin, and compared the estimated results with the observations from six eddy covariance flux towers. We aimed to (1) assess the impacts of different input variables on the accuracy of ET and NEE estimations, (2) compare the accuracy of the three regression methods, including three machine learning algorithms and Multiple Linear Regression, and (3) evaluate the performance of ET and NEE estimation models across various regions. The key findings include: (1) Increasing the number of input variables typically improved the accuracy of ET and NEE estimations. (2) RFR proved to be the most accurate for both ET and NEE estimations among the three regression algorithms. Of these, the four types of variables used together with RFR resulted in the best accuracy for ET (R² of 0.81 and an RMSE of 1.13 mm) and NEE (R² of 0.83 and an RMSE of 2.83 gC/m²) estimations. (3) Vegetation growth variables (i.e., VIs) are the most important inputs for ET and NEE estimation. (4) The proposed ET and NEE estimation models exhibited some variation in accuracy across different validation sites. Despite these variations, the accuracy levels across all six validation sites remained relatively high. Overall, this study lays the groundwork for an efficient approach to agricultural water resources and ecosystem monitoring and management. Full article

This research focuses on investigating the buckling strength of thin-walled composite structures featuring various shapes of holes, laminates, and composite materials. A parametric study is conducted to optimize and identify the most suitable combination of material and structural parameters, ensuring the resilience of structure under both mechanical and thermal loads. Initially, a numerical approach employing the finite element method is used to design the C-section thin-walled composite structure. Later, various structural and material parameters like spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied. Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. Various ML techniques such as linear regression, lasso regression, decision tree, random forest, and gradient boosting are employed to assess their accuracy in comparison with finite element results. As a result, the simulation model showcases the variation in critical buckling load when altering the structural and material properties. Additionally, the machine learning models successfully predict the optimal critical buckling load under mechanical and thermal loading conditions. In summary, this paper delves into the study of the stability of C-section thin-walled composite structures with holes under mechanical and thermal loading conditions using finite element analysis and machine learning studies. Full article

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d⁻¹, 0.54 cm d⁻¹, and 4.54 cm d⁻¹, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson’s correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research. Full article

With the development of unconventional natural gas resources, plunger gas lift technology has gained widespread application. Accurately predicting gas production from unconventional gas reservoirs is a crucial step in evaluating the effectiveness of plunger gas lift technology and optimizing its design. However, most existing prediction methods are mechanism-driven, incorporating numerous assumptions and simplifications that make it challenging to fully capture the complex physical processes involved in plunger gas lift technology, ultimately leading to significant errors in capacity prediction. Furthermore, engineering design factors and production system factors associated with plunger gas lift technology can contribute to substantial deviations in gas production forecasts. This study employs three powerful regression algorithms, XGBoost, Random Forest, and SVR, to predict gas production in plunger gas lift wells. This method comprehensively leverages various types of data, including collected engineering design, production system, and production data, directly extracting the underlying patterns within the data through machine learning algorithms to establish a prediction model for gas production in plunger gas lift wells. Among these, the XGBoost algorithm stands out due to its robustness and numerous advantages, such as high accuracy, ability to effectively handle outliers, and reduced risk of overfitting. The results indicate that the XGBoost algorithm exhibits impressive performance, achieving an R² (coefficient of determination) value of 0.87 for six-fold cross-validation and 0.85 for the test set. Furthermore, to address the “black box” problem (the inability to know the internal working structure and workings of the model and to directly understand the decision-making process), which is commonly associated with conventional machine learning models, the SHAP (Shapley additive explanations) method was utilized to globally and locally interpret the established machine learning model, analyze the main factors (such as starting time of wells, gas–liquid ratio, catcher well inclination angle, etc.) influencing gas production, and enhance the credibility and transparency of the model. Taking plunger gas lift wells in southwest China as an example, the effectiveness and practicality of this method are demonstrated, providing reliable data support for shale gas production prediction, and offering valuable guidance for actual on-site production. Full article

Soil, a non-renewable resource, requires continuous monitoring to prevent degradation and support sustainable agriculture. Visible-near-infrared (Vis-NIR) spectroscopy is a rapid and cost-effective method for predicting soil properties. While traditional machine learning methods are commonly used for modeling Vis-NIR spectral data, large datasets may benefit more from advanced deep learning techniques. In this study, based on the large soil spectral library LUCAS, we aimed to enhance regression model performance in soil property estimation by combining Transformer and convolutional neural network (CNN) techniques to predict 11 soil properties (clay, silt, pH in CaCl₂, pH in H₂O, CEC, OC, CaCO₃, N, P, and K). The Transformer-CNN model accurately predicted most soil properties, outperforming other methods (partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), Long Short-Term Memory (LSTM), ResNet18) with a 10–24 percentage point improvement in the coefficient of determination (R²). The Transformer-CNN model excelled in predicting pH in CaCl₂, pH in H₂O, OC, CaCO₃, and N (R² = 0.94–0.96, RPD > 3) and performed well for clay, sand, CEC, P, and K (R² = 0.77–0.85, 2 < RPD < 3). This study demonstrates the potential of Transformer-CNN in enhancing soil property prediction, although future work should aim to optimize computational efficiency and explore a wider range of applications to ensure its utility in different agricultural settings. Full article

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R² of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates. Full article

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate. Full article

Vertical water jets present significant challenges for hydraulic structures due to their potential to cause erosion and structural damage. This study aimed to predict the dimensionless pressure coefficient (C_p) of vertical water jets by examining the relationships between experimental parameters, such as Froude number, slope, and the ratio of waterfall height over the product of the Froude number and diameter, referred to as α, using machine learning models. Two hundred forty controlled experiments were conducted, with pressure data collected. To address the problem’s non-linearity, six machine learning models were tested: linear regression, K-nearest neighbors, decision tree, support vector regression, random forest, and XGBoost. The XGBoost model outperformed others, achieving an R-squared of 0.953 and a Root Mean Squared Error (RMSE) of 0.191. Residual analysis validated its better performance, demonstrating that it delivered the most accurate predictions with minimal bias. Feature importance analysis revealed the Froude number was the most significant predictor, followed by slope and diameter. This study emphasizes the importance of the Froude number in predicting jet behavior and shows the efficacy of advanced machine learning models in capturing complex fluid dynamics, providing valuable insights for optimizing engineering applications such as water jet cutting and cooling systems. Full article

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm², droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h⁻¹), and spraying norm (250–400 L h⁻¹). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm² (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R² = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R² = 0.437–0.503). Full article