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Aerosol remote sensing typically relies on reflected shortwave radiation and thus lacks nighttime retrievals. Here we made an original attempt to retrieve nighttime aerosol optical depth (AOD) by utilizing longwave measurements in the atmospheric window region from the Atmospheric InfraRed Sounder (AIRS) instrument. A machine‐learning based algorithm is developed using AIRS longwave radiance and auxiliary data as the input and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) as well as reanalysis surface temperature as the output. Independent validation indicates good agreement with lunar AOD derived from surface photometers. An overall increase in nighttime AOD compared to daytime is also uncovered, which is further corroborated by surface and space lidar measurements. The theoretical basis of the algorithm is further verified using radiative transfer simulations. Our approach substantially extends the potential of hyperspectral longwave measurements and offers valuable insights into nighttime aerosol properties. Plain Language Summary: Aerosol optical depth (AOD) is an important parameter for understanding the effects of aerosols on the Earth's atmosphere and air quality. However, current satellite‐based methods for estimating AOD mainly rely on reflected sunlight, thus lacking the capability of deriving aerosol properties at night. In this study, we develop a novel machine‐learning based method for estimating nighttime AOD using longwave far infrared measurements from the Atmospheric InfraRed Sounder (AIRS) satellite instrument. Our algorithm shows satisfactory performance with good agreements with surface AOD derived from moonlight, as well as from space lidar. Through radiative transfer simulations, we were able to verify the theoretical basis of our method and establish a relationship between AOD and the longwave measurements. This new approach greatly expands our ability to estimate AOD during nighttime and provides valuable insights into day‐time aerosol variability. Key Points: Nighttime AOD is successfully retrieved from satellite longwave radiance measurementsResults agree well with AERONET lunar AOD and CALIPSO nighttime AODNighttime AOD is found to be overall higher than daytime AOD globally [ABSTRACT FROM AUTHOR]

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes. [ABSTRACT FROM AUTHOR]

Background: Although high tibial osteotomy (HTO) is an established treatment option for medial compartment osteoarthritis, predictive factors for HTO treatment success remain unclear. This study aimed to identify informative variables associated with HTO treatment success and to develop and internally validate machine learning algorithms to predict which patients will achieve HTO treatment success for medial compartmental osteoarthritis. Methods: This study retrospectively reviewed patients who underwent medial opening-wedge HTO (MOWHTO) at our center between March 2010 and December 2015. The primary outcomes were a lack of conversion to total knee arthroplasty (TKA) and achievement of a minimal clinically important difference of improvement in the Knee Injury and Osteoarthritis Outcome Score (KOOS) at a minimum of five years postoperatively. Recursive feature selection was used to identify the combination of variables from an initial pool of 25 features that optimized model performance. Five machine learning algorithms (XGBoost, multilayer perception, support vector machine, elastic-net penalized logistic regression, and random forest) were trained using five-fold cross-validation three times and applied to an independent test set of patients. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC). Results: A total of 231 patients were included, and 200 patients (86.6%) achieved treatment success at the mean of 9 years of follow-up. A combination of seven variables optimized algorithm performance, and the following specific cutoffs increased the likelihood of MOWHTO treatment success: body mass index (BMI) ≤ 26.8 kg/m2, preoperative KOOS for pain ≤ 46.0, preoperative KOOS for quality of life ≤ 33.0, preoperative International Knee Documentation Committee score ≤ 42.0, preoperative Short-Form 36 questionnaire (SF-36) score > 42.25, three-month postoperative hip-knee-ankle angle > 1.0°, and three-month postoperative medial proximal tibial angle (MPTA) > 91.5° and ≤ 94.7°. The random forest model demonstrated the best performance (F1 score: 0.93; AUC: 0.81) and was transformed into an online application as an educational tool to demonstrate the capabilities of machine learning. Conclusions: The random forest machine learning algorithm best predicted MOWHTO treatment success. Patients with a lower BMI, poor clinical status, slight valgus overcorrection, and postoperative MPTA < 94.7 more frequently achieved a greater likelihood of treatment success. Level of evidence: Level III, retrospective cohort study. [ABSTRACT FROM AUTHOR]

The work presents a tool enabling the selection of powder for 3D printing. The project focused on three types of powders, such as steel, nickel- and cobalt-based and aluminum-based. An important aspect during the research was the possibility of obtaining the mechanical parameters. During the work, the possibility of using the selected algorithm based on artificial intelligence like Random Forest, Decision Tree, K-Nearest Neighbors, Fuzzy K-Nearest Neighbors, Gradient Boosting, XGBoost, AdaBoost was also checked. During the work, tests were carried out to check which algorithm would be best for use in the decision support system being developed. Cross-validation was used, as well as hyperparameter tuning using different evaluation sets. In both cases, the best model turned out to be Random Forest, whose F1 metric score is 98.66% for cross-validation and 99.10% after tuning on the test set. This model can be considered the most promising in solving this problem. The first result is a more accurate estimate of how the model will behave for new data, while the second model talks about possible improvement after optimization or possible overtraining to the parameters. [ABSTRACT FROM AUTHOR]

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012–2022), average annual rainfall (2012–2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area's total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county's geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses' strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory. [ABSTRACT FROM AUTHOR]

Background: This investigation sought to cross validate the predictors of tongue pressure recovery in elderly patients' post-treatment for head and neck tumors, leveraging advanced machine learning techniques. Methods: By employing logistic regression, support vector regression, random forest, and extreme gradient boosting, the study analyzed an array of variables including patient demographics, surgery types, dental health status, and age, drawn from comprehensive medical records and direct tongue pressure assessments. Results: Among the models, logistic regression emerged as the most effective, demonstrating an accuracy of 0.630 [95% confidence interval (CI): 0.370–0.778], F1 score of 0.688 [95% confidence interval (CI): 0.435–0.853], precision of 0.611 [95% confidence interval (CI): 0.313–0.801], recall of 0.786 [95% confidence interval (CI): 0.413–0.938] and an area under the receiver operating characteristic curve of 0.626 [95% confidence interval (CI): 0.409–0.806]. This model distinctly highlighted the significance of glossectomy (p = 0.039), the presence of functional teeth (p = 0.043), and the patient's age (p = 0.044) as pivotal factors influencing tongue pressure, setting the threshold for statistical significance at p < 0.05. Conclusions: The analysis underscored the critical role of glossectomy, the presence of functional natural teeth, and age as determinants of tongue pressure in logistics regression, with the presence of natural teeth and the tumor site located in the tongue consistently emerging as the key predictors across all computational models employed in this study. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations. [ABSTRACT FROM AUTHOR]

Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU's Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%. [ABSTRACT FROM AUTHOR]

This study used artificial intelligence techniques to identify clinical cancer biomarkers for recurrent gastric cancer survivors. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random Forest using MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged the synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive exPlanations (SHAPs) for feature importance analysis in this study. Our proposed Random Forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86%, and an F1 of 88.2% on the recurrent category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI (body mass index), and gender; these features significantly affect the prediction model's output and are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well. [ABSTRACT FROM AUTHOR]

Precisely forecasting the levels of PM 2. 5 is crucial for environmental conservation and human health. Thus, it serves as an essential indicator of atmospheric purity. In this paper, a PM 2. 5 concentration prediction model based on random forest and SHAP is proposed using air pollutants and meteorological conditions as the characterizing factors. Initially, pertinent information is gathered and subsequently manipulated, educated, and forecasted through the application of the random forest technique. Then, SHAP is used to explain the degree of influence of each feature in the model and the prediction results. Results of the experiment demonstrate that the random forest-based PM 2. 5 concentration prediction model for the three cities surpass the comparison model in the RMSE, MAE, and R 2 indicators. Examining SHAP values, the essential elements influencing the PM 2. 5 concentration are pinpointed. [ABSTRACT FROM AUTHOR]

Human activity recognition technology has received much academic attention in recent years and plays a vital role in a wide range of applications such as smart healthcare and security surveillance. A fall detection scheme based on Channel State Information (CSI) is designed to overcome the limitations of traditional fall detection systems such as low privacy, high cost and poor cross-domain capability. Based on the data collected by commercial WiFi devices, the scheme analyzes the changes of indoor CSI caused by human activities and realizes the non-contact fall detection. Firstly, Discrete Wavelet Transform (DWT), phase difference analysis and Moving Average Filter (MAF) are combined to reduce the impact of environmental noise on the detection performance. Secondly, the variance of CSI amplitude and phase difference is calculated as an indicator for selecting subcarriers and the moving variance is used to segment the active interval of the selected subcarriers, which can reduce the data dimensionality and extract the time-frequency features. Finally, the Genetic Algorithm (GA) is used to optimize the parameter selection process of the Random Forest (RF) to improve the performance of the classifier model. Through comparison experiments on the DARMS dataset, it is verified that the proposed scheme can effectively improve the accuracy of fall activity recognition and maintain an accuracy of over 95.25 % when the training environment changes. [ABSTRACT FROM AUTHOR]

Airing process has been one of the most important stages in the production of cigar leaves. Also, the appearance quality can be enhanced to indicate the intrinsic quality. The temperature and humidity can be adjusted inside the drying chamber in real time, according to the moisture content of the leaves for the proper browning. However, the leaf moisture content is often determined by the manual experiences at present, resulting in subjectivity and low accuracy. Alternatively, computer vision can be expected to assess the quality of agricultural products in recent years, due to its simplicity and high flexibility. Additionally, the random forest (RF) model can serve as the bagging-based ensemble machine learning. The highdimensional data variables can be efficiently handled with high precision, training and prediction speeds. In this study, the prediction models were established for the moisture content of cigar leaves using RF machine learning. "Yunxue-2" variety of cigar tobacco was taken as the research object. Initially, the images of cigar leaves were collected during the airing process. The crucial apparent feature was extracted to determine the moisture content of cigar leaves. The color threshold and OTSU segmentation were combined to obtain the leaf region of interest (ROI). Subsequently, four-dimensional features were extracted, including color, contour, texture, and location. The correlation coefficient analysis was employed to eliminate the highly correlated features within each feature dimension, in order to prevent "dimension explosion." Then, the out-of-bag (OOB) data was used to determine the average decrease in the coefficient of determination (Decr²). The importance of image features was ranked as well. A comparison was conducted on the prediction accuracy and runtime of the RF model under different feature quantities. The optimal subset of image features was selected as the seven image features that are closely related to the moisture content of cigar tobacco leaf. The original and optimal feature subsets were then used to evaluate the RF, support vector regression (SVR), and back propagation neural network (BPNN) models. Genetic algorithm (GA) was utilized to optimize the hyperparameters of each model. Three models were combined with the two sets of image features. Six modelfeature combination schemes were then established. Five-fold cross validation was employed to compare the prediction accuracy and generalization. Subsequently, the performance of six schemes was verified on a test dataset during drying. The results demonstrated that the combination of color, contour, texture, and location features of cigar tobacco leaf images effectively characterized the changes in the appearance morphology under moisture loss. The combination of SVR and BPNN with the optimal image feature subset outperformed their combinations with the original one after five-fold cross-validation. While RF exhibited better performance on the original image feature set, leading to avoiding the information redundancy with high-dimensional data. The best performance on the test set was achieved in the combination of the GA-SVR model and optimal image feature subset, with r² and MSE values of 0.980 and 0.001, respectively, with the shortest runtime (0.128 s). In summary, the image features of cigar tobacco leaf were utilized to accurately predict the moisture content of different parts in the entire drying. The finding can also provide the theoretical basis for the intelligent drying of cigar tobacco leaves. [ABSTRACT FROM AUTHOR]

Rapid and accurate estimation of rock strength is of great practical significance for the stability of rock engineering. This article is based on the results of triaxial compression tests on crack rocks and comprehensively considers the interrelationships between crack geometric characteristics, confining pressure, and physical and mechanical parameters. A database of rock strength characteristic parameters from an experimental perspective is constructed, and a quantitative prediction model for the strength of crack rock masses under the influence of multiple characteristic parameters is established using the random forest algorithm. The results show that the accuracy of predicting rock strength in categories 2 of 21–30 MPa, 3 of 31–40 MPa, 4 of 41–50 MPa, and 5 of 51–60 MPa is 100%, and the accuracy of predicting rock strength in categories 1 of 11–20 MPa is 82% and in categories 0 of 0–10 MPa is 74%; through the study of parameter correlation and quantitative analysis with strength, the selected parameters are reliable and have a certain degree of correlation, and the influence mechanism of characteristic parameters on rock strength has been explored; the calculation results of parameter classification show that confining pressure and crack length are the key factors affecting rock strength. The importance of other parameters is ranked in the order of longitudinal wave velocity > Poisson’s ratio > saturated water content > porosity > saturated quality > dry quality > saturated density > crack dip angle > crack number > crack penetration. This provides an effective approach to consider the randomness and correlation of rock strength parameters, providing a new idea for rock mechanics research. [ABSTRACT FROM AUTHOR]

Formulating ecological restoration strategies requires accurately quantifying how climate and anthropogenic factors influence net primary production (NPP). A Carnegie-Ames-Stanford approach (CASA) model was applied to estimate China's terrestrial NPP from 2001 to 2020. We adopted a random forest (RF) method to identify the main driving forces for NPP change in China. Total NPP in China increased noticeably with a 24.91 Tg C/yr rate, as shown in our results. The significantly increased NPP was mainly attributed to human activities (64.29 ± 0.17%), chiefly due to human management and ecological projects (afforestation or other) fostered vegetation growth. The primary drivers of NPP variation varied in different geographic regions. Climate dominated the NPP dynamic in north China (52.38 ± 0.91%), where the main factor that restricted the increase of NPP was precipitation. Human activities strongly impacted the NPP variation in the remaining regions. Human management measures increased NPP in northwest and southwest China. In the northeast, east, and south-central China, the NPP change resulted from land use change, primarily grassland, cropland, and forest change. Collectively, our study expands the understanding of the driving forces of NPP change, informing different strategies for achieving ecological restoration and carbon neutrality. [ABSTRACT FROM AUTHOR]

We have maintained the customer grade system that is being implemented to customers with excellent performance through customer segmentation for years. Currently, financial institutions that operate the customer grade system provide similar services based on the score calculation criteria, but the score calculation criteria vary from the financial institution to financial institution. In this study, we create a machine learning prediction model using items and added items that are based on the current customer grade of our bank,- and the purpose is an optimal model that considers the adequacy of existing variables and the validity of additional variables through comparison between models. Using Lasso, Elastic net and Multinomial Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, we propose that the best model be found and gradually applied to customer grade calculation criteria. [ABSTRACT FROM AUTHOR]

Applications for machine learning (ML), deep learning, and other artificial intelligence models have shown great promise in nuclear physics, including not only in classification systems but also in the analysis of numerical data. This study used various ML algorithms to estimate the concentrations of six rare earth elements (Sm, La, Ce, Sc, Eu, and Tb) in both archaeological and marine sediment samples. An interesting aspect of this analysis is that 80% of the 235 data points were used for training data, which included two parameters: specific activity ( A sp ) and concentration (ρ ) by the k0-method for the purpose of model development. The remaining 20% of the dataset was held out for testing the model's accuracy. The fundamental principle of this approach is the use of regression analysis between A sp and ρ to construct a machine learning regression model. This machine learning model was subsequently applied to estimate element concentrations based on A sp values obtained from gamma spectra. The mean absolute error (MAE), root mean square error (RMSE) and the statistical measure R-squared (R2) were employed for evaluating the accuracy of the predicted models. The random forest (RF) algorithm produces smaller MAE and RMSE values and achieves better R2 values compared to other algorithms. In addition, RF shows the lowest relative bias of the concentration values of elements in reference material (NIST 2711a) compared to other prediction models. The work focuses on demonstrating that machine learning models can effectively predict the concentrations of rare earth elements, even though this is a fundamental issue in NAA and one previous study has addressed this issue for one single element. The extension of the current work and potential directions for further development will be presented in the results and discussion section. [ABSTRACT FROM AUTHOR]

Each year, millions of birds migrate nocturnally over the North Sea basin, an area designated for significant offshore wind energy development. Wind turbines can harm aerial wildlife through collisions and barrier effects, especially when birds fly at low altitudes below the wind turbine rotor tip. We aim to quantify seasonal and nightly differences in flight altitudes of nocturnal bird migration over the North Sea and identify how weather influences low‐altitude flight to inform wind turbine curtailment procedures for reducing bird fatalities. We used bird tracking radars at Borssele and Luchterduinen offshore wind parks, 22 and 23 km from the western Dutch coast, to monitor altitude distributions during migration. We show that median flight altitude was higher in spring compared to autumn at Borssele (spring: 285.5 m, autumn: 169.2 m; p <.001, effect size [ES] = 0.0001) and Luchterduinen (spring: 133.8 m, autumn: 126.0 m; p <.001, ES = 0.002) and below wind turbine rotor tip in both seasons. On most nights in both seasons, the majority of migrants flew predominantly at low altitudes, except for intense migration nights in spring in Borssele where, on 87% of these nights, migration mainly occurred at high altitudes. The most important predictors of low‐altitude migration in both seasons were day of year and wind assistance. Birds chose altitudes with the most favorable wind conditions for migration in both seasons. The relationship between day of year and low‐altitude migration fraction suggests that different species migrate at different altitudes. In spring, birds were flying lower at the beginning and the end of the night, reflecting departures and arrivals of birds, while radar location in autumn was a good predictor of low‐altitude flights, indicating that different local migratory axes have distinct altitude distributions. Our findings suggest that mitigation measures offshore may be more effective during autumn than spring, especially on nights with more supportive wind conditions at altitudes below 300 m. [ABSTRACT FROM AUTHOR]

The estimation of disc cutter wear (CW) remains a complex problem in mechanized tunneling using tunnel boring machines (TBM), despite the development of numerous TBM performance models. This research aimed to estimate the cutter life index (CLI) as an index to predict the CW by developing predictive models based on two machine learning algorithms, namely gradient boosting (GB) and random forest (RF), optimized by three optimization techniques: particle swarm optimization (PSO), differential evolution (DE), and simulated annealing (SA). To gain the aim, a dataset consisting of four rock parameters—density (ρ), uniaxial compressive strength, Brazilian tensile strength (BTS), and brittleness index—with 80 mechanized tunnel cases for each parameter has been utilized by obtaining the sample and then relevant tests on them were conducted in the laboratory. First, various parameter selection methods, such as mutual information, have been employed to reduce the dimensionality of the problem, and it has been revealed that ρ and BTS have been the most influential parameters to estimate the CLI. Then, by developing six optimized models, including GB-PSO, GB-DE, GB-SA, RF-PSO, RF-DE, and RF-SA, using the two mentioned parameters, their performance has been assessed via three performance evaluation indices of coefficient of determination (r2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results showed that among six predictive models, the two models of GB-SA (with r2, RMSE, and MAPE of 0.8274, 10.8329, and 0.3957, respectively) and RF-PSO (with r2, RMSE, and MAPE of 0.8213, 11.0249, and 0.4113, respectively) outperformed the other models, with 82.74% accuracy of GB-SA and with 82.13% accuracy of the RF-PSO, and the two can be utilized to estimate disc cutter via CLI for different type of rock in the range of established dataset. [ABSTRACT FROM AUTHOR]

Self-oscillatory systems have great utility in energy harvesting, engines, and actuators due to their ability to convert ambient energy directly into mechanical work. This characteristic makes their design and implementation highly valuable. Due to the complexity of the motion process and the simultaneous influence of multiple parameters, computing self-oscillatory systems proves to be challenging, especially when conducting inverse parameter design. To simplify the computational process, a combined approach o0f Random Forest (RF) and Backpropagation Neural Network (BPNN) algorithms is employed. The example used is a self-rotating skipping rope made of liquid crystal elastomer (LCE) fiber and a mass block under illumination. Numerically solving the governing equations yields precise solutions for the rotation frequency of the LCE skipping rope under various system parameters. A database containing 138,240 sets of parameter conditions and their corresponding rotation frequencies is constructed to train the RF and BPNN models. The training outcomes indicate that RF and BPNN can accurately predict the self-rotating skipping rope frequency under various parameters, demonstrating high stability and computational efficiency. This approach allows us to discover the influences of distinct parameters on the rotation frequency as well. Moreover, it is capable of inverse design, meaning it can derive the corresponding desired parameter combination from a given rotation frequency. Through this study, a deeper understanding of the dynamic behavior of self-oscillatory systems is achieved, offering a new approach and theoretical foundation for their implementation and construction. [ABSTRACT FROM AUTHOR]

Large genotyping datasets, obtained from high‐density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre‐select informative SNPs. To identify different sets of SNPs, these pre‐selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed‐specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose. [ABSTRACT FROM AUTHOR]

Land use and land cover change have significant impacts on climate, the environment, and natural ecosystems. This research analyzes land use change over time using Google Earth Engine (GEE) and provides recommendations for land use planning based on the results. This research aligns with priorities related to SDG issues, specifically the maintenance of degraded terrestrial ecosystems that negatively impact the livelihoods of the organisms that inhabit them. The study utilized Sentinel-2 with Multi Spectral Instrument, Level 2A time series from 2019 - 2023, which were processed using cloud computing and a classification method utilizing SMILE Random Forest. The classification model achieved an accuracy value of 95%. The calculation results indicate a total land use and land cover (LULC) area of 1007.96 hectares. The largest change in land use occurred in fields, decreasing from 10.64% to 4.96%, or 57.18 hectares, while the smallest change occurred in dryland forest, at 2.18 hectares. The total predicted LULC change area was 558.02 hectares. [ABSTRACT FROM AUTHOR]

Neuromarketing uses cognitive science in marketing-related research to study consumer sensorimotor effects, such as cognitive and emotional responses to marketing stimuli using new technologies. This research is a predictive framework for participants' decision-making processes in terms of liking and disliking when viewing and selecting the products of an online store. For this purpose, dispersion entropy (DE) and hierarchical dispersion entropy (HDE) features are extracted from the participant's brain signal (EEG) when displaying different products. Dispersion pattern evaluation is used for the first time to distinguish between two categories of likes and dislikes using EEG signals. In this study, the ensemble learning approach with a random forest classifier is investigated to create a model for predicting neuromarketing to distinguish between two categories of likes and dislikes. From the analysis of the model results, 73.67% accuracy has been obtained. Studies in this area can change and improve marketing strategies to improve production and consumer processes, and ultimately lead to mutual benefit. [ABSTRACT FROM AUTHOR]

Who interacts with whom is a key question in community and network ecology. The concept that these interactions may be driven by a match between the traits of consumer and resource species is known as trait‐matching. If trait‐matching would allow for general predictions of interaction structure based on sufficiently few and easily‐measurable traits, then this approach could replace the laborious description of each individual pairwise interaction. To resolve imprints of trait‐matching in a species‐rich tri‐trophic Salix–galler–parasitoid network, and to identify the most relevant traits, we applied five different methods, each approaching the same phenomenon from a different perspective. As traits, we used, body sizes, gall type (position on plant, structure of gall) and phenology, among others, as well as phylogenetic proxies. When jointly applied, the methods demonstrate distinctly different imprints of traits within the two bipartite network elements (Salix–galler versus galler–parasitoid interactions). Of the galler–parasitoid sub‐network's interactions, approximately half were explainable by the species traits used; of the Salix–galler sub‐network's interactions, traits explained at most two‐fifths. Gall type appeared to be the most important structuring trait in both networks. Phylogeny explained as much, or more than did our tested traits, suggesting that traits may be conserved and phylogeny therefore an effective proxy. Overall, the more specialized structure of the Salix–galler network versus the more nested structure of the galler–parasitoid network meant that different methods were more effective at capturing interactions and interaction structure in the different sub‐networks. Thus, our analysis reveals how structuring impacts may vary even between levels within the same multitrophic network, and calls for comparative analyses of trait matching across a wide set of systems and methods. [ABSTRACT FROM AUTHOR]

The pavement anti-skid performance directly affects road traffic safety, and the evaluation methods based on pavement texture features currently have problems of poor interpretability and low accuracy. Herein, 185 sets of pavement texture data were collected by the portable 3D laser surface analyzer with an accuracy of 0.05 mm. The pavement friction data in the speed range of 0~80 km/h of the corresponding road section were obtained with the dynamic friction coefficient tester. The comprehensive fractal dimension index representing the complexity of the pavement texture space, the cross section, and the depth direction was constructed, and the random forest evaluation model for pavement skid resistance performances at speeds of 10 km/h and 70 km/h. The results show that, the comprehensive fractal dimension has the ability to describe the complexity of texture independently, but there is no linear relationship between it and the pavement dynamic friction coefficient; the prediction accuracy of comprehensive fractal dimensions for dynamic friction coefficients at the 70 km/h speed is 0.78, which can be used to evaluate the skid resistance of pavement under the condition of rapid sliding of tire rubber; the spatial, cross-sectional, surface, shallow, and deep profile fractal features in comprehensive fractal indicators jointly affect the pavement anti-skid performances. In the evaluation of pavement texture morphology, comprehensive analysis of texture features should be conducted from multiple spatial perspectives. [ABSTRACT FROM AUTHOR]

Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a 'Multi classifier ShapRFECV based EOC' (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users' opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers. [ABSTRACT FROM AUTHOR]

In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds. [ABSTRACT FROM AUTHOR]

This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition. [ABSTRACT FROM AUTHOR]

Mobile devices (MDs) are becoming more prevalent and their battery life is optimised by offloading tasks to cloud servers. However, communication costs must be considered when offloading tasks. To make task offloading worthwhile, it is important to measure the energy consumed during communication activities. Thus, a heterogeneous framework is developed to enhance the energy efficiency of smartphones by analysing parameters such as task and non-task offloading, local cloudlets, radio access networks and remote cloud servers. This paper proposes a task offloading framework that uses a novel algorithm, the Hybrid Red Fox Flow Direction-based Ensemble SVM Forest Classifier, to enhance the system parameters and schedule tasks in offloading cloud computing conditions. The multi-objective function aims to improve user satisfaction by maximising resource utilisation and minimising function. The framework was tested in the Cloudsim simulation tool and compared with different techniques, with the results demonstrating its superiority in terms of energy efficiency and system performance. The proposed framework can optimise the energy efficiency of MDs and improve battery life. [ABSTRACT FROM AUTHOR]

This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints' maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency. [ABSTRACT FROM AUTHOR]

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle's position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position. [ABSTRACT FROM AUTHOR]

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments. [ABSTRACT FROM AUTHOR]

Objective: This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs). Background: Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse. Method: 34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers' physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters. Result: Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%. Conclusion: The features investigated and the RF model developed can identify trust-change directions during TTs accurately. Application: Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers' overtrust and undertrust in conditionally AVs. [ABSTRACT FROM AUTHOR]

Tropical forests are the world's most structurally complex ecosystems, providing key functions like biomass accumulation, which is linked to this complexity. Tropical forests are also exposed to chronic, non-severe winds, yet their effect on forest structural complexity is understudied. Here we examine drivers of forest structural complexity in Puerto Rico with a particular focus on chronic wind exposure. We used airborne light detection and ranging data collected in 2016 to quantify canopy height and rugosity (variation in height) in ~ 20,000, 0.28 ha forested sites stratified by forest age. We used random forest models to analyze variation in canopy height and rugosity as a function of chronic wind exposure, forest age, mean annual precipitation, elevation, slope (in degrees), soil type, soil available water storage, and exposure to a previous hurricane. Canopy height was driven by precipitation, forest age, and chronic wind exposure, decreasing by 2.12 m (16%) on average in wind-exposed forests across all forest ages. Canopy height increased by 4.0 m (41%) on average in forests aged 25–66 years, and by 4.0 m between sites with 1000 and 2000 mm y−1 precipitation. Canopy rugosity was driven by canopy height, precipitation, forest age, and elevation, increasing log-linearly with canopy height and precipitation, decreasing with elevation, and was highest in younger forests. Chronic wind exposure did not drive variation in canopy rugosity. Our results suggest that chronic wind exposure plays an integral role in limiting canopy height, potentially reducing aboveground carbon accumulation in older tropical forests. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Precipitation is the basic component of the Earth's water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI. [ABSTRACT FROM AUTHOR]

Climate change and other anthropogenic factors have caused a significant decline in seagrass cover globally. Identifying the specific causes of this decline is paramount if they are to be addressed. Consequently, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in a marine protected area on Jamaica's southern coast. Two random forest regression (RFr) models were built using 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary and 2013 Landsat 7 and 8 reflectance data as the predictors. These were used to generate 24 SAV percentage cover and benthic feature maps (SAV present, absent, and coral reef) for the period 1984–2021 (37 years) from Landsat satellite series reflectance data. These maps and rainfall data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Additionally, rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed. Finally, the most important spatial pattern predictors of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified. Predictors included rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance). SAV area/extent was largely stable, with >70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or 13%) during 2002–2006, and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by >1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change and had a positive overall effect on the spatial pattern of SAV cover percentage change (across the entire bay) and gain (close to the mouth of a river). The most important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Specifically, a hurricane that made landfall in 1988 (Gilbert), resulted in higher SAV percentage cover loss in 1987–1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan's leading edge wind bands) experienced higher SAV losses during 2002–2006. Additionally, exposure to Ivan explained percentage cover loss during 2006–2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013–2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years, SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future. [ABSTRACT FROM AUTHOR]

The production of "Nocciola Romana" hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system. [ABSTRACT FROM AUTHOR]

Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models. [ABSTRACT FROM AUTHOR]

Concerning preventive maintenance plans for heritage timber buildings, computational methods are pioneering knowledge for the implementation of new preservation approaches in heritage structure management. In this context, fuzzy logic and random forest methodologies manage both data obtained from professional experts and data obtained in situ from the buildings themselves. This kind of digital procedure can harmonize the outcomes of building assessments because slight variations in the evaluation of input parameters do not produce a significant dispersion over the model's output. Preventive conservation strategies require cooperation among qualified experts who examine multidisciplinary knowledge related to heritage properties. Thus, new digital protocols and procedures that help decision makers prioritize preventive interventions and avoid corrective actions are paramount in minimizing the irreparable loss of properties. The main aim of this research is a new approach to two computational management systems: fuzzy logic and random forest. The outcomes of this study will be useful to stakeholders who are responsible for the maintenance of heritage buildings, as this methodology reduces the probability of failure and uncertainty during decision-making. The instruments derived will establish mitigation strategies oriented toward proactive future maintenance programs for heritage timber buildings in southern Chile. [ABSTRACT FROM AUTHOR]

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran's I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought. [ABSTRACT FROM AUTHOR]

Garlic (Allium sativum) is an important economic crop in China. In terms of using remote sensing technology to identify it, there is still room for improvement, and the high-precision classification of garlic has become an important issue. However, to the best of our knowledge, few studies have focused on garlic area mapping. Here, we propose a method for identifying garlic crops using samples and a multi-feature dataset under limited conditions. The results indicate the following: (1) In the land-use classification of the Erhai Lake Basin, the importance ranking of the characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. (2) The random forest method based on feature selection demonstrates high accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reached 95.79%, with a Kappa coefficient of 0.95. (3) From 1999 to 2023, the expansion of garlic cultivation in the Erhai Lake Basin showed a trend of initially strengthening from north to south, which was followed by weakening. The vertical development of garlic cultivation reached saturation, showing a slow trend toward horizontal expansion between 2005 and 2018. The planting distributions in various townships in the Erhai Lake Basin gradually shifted from relatively uniform distributions to upstream development. This study utilized the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Moreover, it accurately, rapidly, and efficiently extracted planting information, demonstrating significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm's search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future. [ABSTRACT FROM AUTHOR]

The Random Forest (RF) and K nearest neighbors (KNN) machine learning (ML) algorithms were evaluated for their ability to predict ophiolite occurrences, in the East Vardar Zone (EVZ) of central North Macedonia. A predictive map of the investigated area was created using three data sources: geophysical data (digital elevation model, gravity and geomagnetic), multispectral optical satellite images (Landsat 7 ETM + and their derivatives), and geological data (distance to fault map and ophiolite outcrops map). The research included a comparison and discussion on the statistical and geological findings derived from different training dataset class ratios in relation to a testing dataset characterized by significant class imbalance. The results suggest that the precise selection of a suitable class balance for the training dataset is a critical factor in achieving accurate ophiolite prediction with RF and KNN algorithms. The analysis of feature importance revealed that the Bouguer gravity anomaly map, total intensity of the Earth's magnetic field reduced to the pole map, distance to fault map, band ratio BR3 map obtained from multispectral satellite images, and digital elevation model are the most significant features for predicting ophiolites within the EVZ. KNN showed poorer results compared to RF in terms of both the evaluation metrics and visual analysis of prediction maps. The methods applied in this research can be applied for predictive mapping of complex geo-tectonic units covered by dense vegetation, and may indicate the presence of these units even if they were not previously mapped, particularly when geophysical data are used as features. [ABSTRACT FROM AUTHOR]

This study explores the application of machine learning algorithms for the prediction of pan evaporation (Ep), which is a critical factor in water resource management for the assessment of water demand and usage. Specifically, this research evaluates the effectiveness of two base models: Random Forest (RF) and Multi-Layer Perceptron (MLP) and their optimized counterparts using a Genetic Algorithm (GA), designated as GA-RF and GA-MLP, for modeling Ep at a target station using data from adjacent stations. The datasets were split into a training set (70%) and a testing set (30%). The models' performances were judged using three statistical measures: Correlation Coefficient (CC), Scattered Index (SI), and Willmott's Index of agreement (WI). The enhanced models, particularly GA-MLP-5, showed superior performance with a CC of 0.8704, SI of 0.2539, and WI of 0.9212, indicating the potent ability of GA to refine RF and MLP models for predictive accuracy. Additionally, sensitivity analysis via the GA-RF indicates the varying influence of Ep from neighboring stations on the target station, shedding light on key predictors for effective water management. Conclusively, this study demonstrates that the hybrid models have significant potential in accurate Ep estimation and can be expanded to predict other meteorological variables, offering valuable tools for water resource management strategies. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) is a major measure for disaster prevention and control. This paper proposes an incremental learning random forest model that uses random forests for incremental training of landslides. The 1652 landslides in Ganzhou, China from year 2015 to 2020 were divided into three equal parts (T1, T2 and T3) considering time series. And the data of T1 part is used to construct incremental learning random forest model ILRF_1, T2 and T3 part as the new part of incremental training. Landslide susceptibility maps were plotted using ILRF, batch learning random forests (BLRF), batch learning support vector machines (BLSVM), respectively. And the accuracy is verified using the receiver operating curve (ROC). The results showed that the ILRF was the most promising model(AUC = 0.937) for the LSM, followed by BLRF(AUC = 0.905), BLSVM(AUC = 0.865). ILRF has better predictive ability in the susceptibility evaluation of this region. It is very high-prone area accounts for the smallest proportion, mainly concentrated in the mountain front area in the northeast of Ganzhou City. The multi-year average rainfall, population density, and normalized difference vegetation index are the first three important factors that control the spatial possibility of landslides. [ABSTRACT FROM AUTHOR]

Genomic prediction in Coffee breeding has shown good potential in predictive ability (PA), genetic gains and reduction of the selection cycle time. It is known that the cost of genotyping was prohibitive for many species, and their value is associated with the density markers panel used. The use of optimize marker density panel may reduce the genotyping cost and improve the PA. We aimed to evaluate the trade-off between density marker panels size and the PA for eight agronomic traits in Coffea canephora using machine learning algorithms. These approaches were compared with BLASSO method. The used data consisted of 165 genotypes of C. canephora genotyped with 14,387 SNP markers. The plants were phenotyped for vegetative vigor (Vig), rust (Rus) and cercosporiose incidence (Cer), fruit maturation time (Mat), fruit size (FS), plant height (PH), diameter of the canopy projection (DC) and yield (Y). Twelve different density marker panels were used. The common trend observed in the analysis shows an increase of the PA as the number of markers decreases, having a peak when used between 500 and 1,000 markers. Comparing the best and the worse results (full SNP panel density) for each trait, some had an improvement around of 100% (PH: 0.35–0.77; Cer: 0.40–0.84; DC: 0.39–0.82; Rus: 0.39–0.83, Vig: 0.40–0.77), the other showed an improvement more than 340% (Mat: 0.12–0.60; Y: 0.14–0.61; FS: 0.07–0.60). The results of the current study indicate that the reduction of the number of markers can improve the selection of individuals at a lower cost. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to forecast the manufacturing value added levels of G-20 countries by leveraging the fundamental dimensions extracted from the Global Innovation Index. Another objective is to determine the extent to which innovation indicators contribute to variations in manufacturing value added. The Random Forest algorithm, known for its versatility and precision in dealing with complex datasets, has been employed as a prominent machine learning technique to predict the manufacturing value added levels of G-20 countries during the period 2013-2022. The Manufacturing Value Added (MVA) levels of G-20 countries, obtained using average and standard deviation, were predicted with a 54.14% error rate through the assistance of innovation input and output indicators. The level predicted with the highest accuracy is the one closely aligned with the average. This study's uniqueness lies in its utilization of the Random Forest algorithm to predict value added levels based on innovation inputs and outputs, which constitute the fundamental dimensions of the Global Innovation Index. [ABSTRACT FROM AUTHOR]

Objective: Inclusion body myositis (IBM) is a progressive late‐onset muscle disease characterised by preferential weakness of quadriceps femoris and finger flexors, with elusive causes involving immune, degenerative, genetic and age‐related factors. Overlapping with normal muscle ageing makes diagnosis and prognosis problematic. Methods: We characterised peripheral blood leucocytes in 81 IBM patients and 45 healthy controls using flow cytometry. Using a random forest classifier, we identified immune changes in IBM compared to HC. K‐means clustering and the random forest one‐versus‐rest model classified patients into three immunophenotypic clusters. Functional outcome measures including mTUG, 2MWT, IBM‐FRS, EAT‐10, knee extension and grip strength were assessed across clusters. Results: The random forest model achieved a 94% AUC ROC with 82.76% specificity and 100% sensitivity. Significant differences were found in IBM patients, including increased CD8+ T‐bet+ cells, CD4+ T cells skewed towards a Th1 phenotype and altered γδ T cell repertoire with a reduced proportion of Vγ9+Vδ2+ cells. IBM patients formed three clusters: (i) activated and inflammatory CD8+ and CD4+ T‐cell profile and the highest proportion of anti‐cN1A‐positive patients in cluster 1; (ii) limited inflammation in cluster 2; (iii) highly differentiated, pro‐inflammatory T‐cell profile in cluster 3. Additionally, no significant differences in patients' age and gender were detected between immunophenotype clusters; however, worsening trends were detected with several functional outcomes. Conclusion: These findings unveil distinct immune profiles in IBM, shedding light on underlying pathological mechanisms for potential immunoregulatory therapeutic development. [ABSTRACT FROM AUTHOR]

Simple Summary: Johne's disease is a chronic progressive gastrointestinal disease of ruminants. The diagnosis and control of this disease remains a challenge for cattle producers and herd veterinarians. Machine learning is a broad class of algorithms and statistical analysis belonging to the area of artificial intelligence. These techniques allow for the recognition of patterns in large datasets that are not found by traditional analytical and statistical methods. While artificial intelligence and machine learning have been used in human medicine and in various aspects of animal husbandry, they have only just started to be used in Johne's disease research. By using results from milk component tests and past Johne's individual test results, tree-based models, a type of machine learning, were used to predict future Johne's results. This type of predictive algorithm may help producers and veterinarians more reliably identify those animals most likely to yield a positive result. This will, in turn, help improve control efforts and reduce the testing burden for herds working towards disease control and eradication. Machine learning algorithms have been applied to various animal husbandry and veterinary-related problems; however, its use in Johne's disease diagnosis and control is still in its infancy. The following proof-of-concept study explores the application of tree-based (decision trees and random forest) algorithms to analyze repeat milk testing data from 1197 Canadian dairy cows and the algorithms' ability to predict future Johne's test results. The random forest models using milk component testing results alongside past Johne's results demonstrated a good predictive performance for a future Johne's ELISA result with a dichotomous outcome (positive vs. negative). The final random forest model yielded a kappa of 0.626, a roc AUC of 0.915, a sensitivity of 72%, and a specificity of 98%. The positive predictive and negative predictive values were 0.81 and 0.97, respectively. The decision tree models provided an interpretable alternative to the random forest algorithms with a slight decrease in model sensitivity. The results of this research suggest a promising avenue for future targeted Johne's testing schemes. Further research is needed to validate these techniques in real-world settings and explore their incorporation in prevention and control programs. [ABSTRACT FROM AUTHOR]