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This article presents an approach for predicting Lockout/Tagout (LOTO) procedure sheets, which are commonly used in the manufacturing industry to prevent premature equipment restart during maintenance. The prediction problem of energetic devices to lock from machine names is regarded as a multi-task classification problem. The dataset was obtained by processing LOTO sheets in Portable Document Format (PDF). The K-Nearest Neighbours (KNN), Random Forest (RF), and Deep Neural Network (DNN) algorithms were compared for this problem. The best prediction performance was achieved with the DNN method, with top-1 accuracies exceeding 63% and top-2 accuracies exceeding 90% for all devices. The sensitivity analysis conducted on the results indicates that the approach is robust and reliable, regardless of the industrial sector considered. In other words, the approach is not significantly affected by variations in the industry or its specific characteristics. These results suggest that the proposed approach can be used to assist workers in drafting LOTO sheets, and offers strong potential for concrete applications in safety management in the era of smart manufacturing. [ABSTRACT FROM AUTHOR]

Processes or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper. [ABSTRACT FROM AUTHOR]

The competition among Food & Beverage companies has substantially increased in today's age of digitization. Sales forecasting is one of their main challenges. Due to space limitations, employee shortages, and rising online demand, retail sales forecasting became extremely important for Food and Beverage companies. This research analyzed the sales data of a multinational Food & Beverage Company. It proposed a framework using Gaussian Mixture Model (GMM) clustering, Hierarchical Agglomerative Clustering (HAC), and Random Forest algorithm for forecasting sales. This model analyzes the impact of the weekends, holidays, promotional activities, customer sentiments, festivals, and socio-economic situations in sales data and is able to forecast sales ranging from one to 15 months. An investigation of the suggested model's performance compared to numerous cutting-edge sales forecasting techniques is carried out to show its efficacy. Here, we demonstrate that the proposed hybrid model surpasses current predicting and computing efficiency methods. The results of this study can help retail managers to allocate resources and manage inventories in well-informed ways. The findings suggest that combining many strategies may produce the most precise forecasts. [ABSTRACT FROM AUTHOR]

As a crucial node in the East Asia–Australasia migratory bird network, the Liaohe River Estuary Wetland (LREW) contributes significantly to coastal ecological balance and biodiversity. Accurate and timely mapping of the LREW is essential for monitoring ecological changes and understanding shifts in the wetland's ecosystem structure and functions. In this study, an innovative approach that integrates radar (Sentinel-1), optical (Sentinel-2), and DEM data on Google Earth Engine (GEE) is proposed to achieve a more optimized and accurate wetland mapping. Recursive Feature Elimination (RFE) is employed to improve feature collection, facilitating the construction of an optimal feature set for Random Forest (RF) classification to extract detailed LREW information. Mapping conducted from 2017 to 2023 using the proposed approach demonstrates its effectiveness and stability in mapping the LREW, achieving overall accuracies ranging from 89.84% to 93.73% and Kappa from 0.85 to 0.91. Compared to single-source methods, the integration of multi-source data substantially enhances mapping accuracy, while RF-RFE effectively eliminates redundant features, further refining classification precision. Texture features (Sum Average, SAVG) and the Water Index 2019 (WI2019) were found to significantly influence wetland mapping. The importance of different feature types is ranked as follows: spectral features > vegetation/water body index > PCA features > SAR texture features > red-edge index > other spectral index > spectral texture features > backscatter coefficient > H-Alpha. During the study period, the wetland area increased from 849.60 km2 to 979.49 km2. This expansion was caused primarily by wetland protection policies that led to the dismantling of coastal aquaculture ponds, resulting in a reduction of 90.28 km2 in waterbodies over 7 years, with many areas transitioning to tidal flats and Suaeda salsa habitats. In conclusion, the proposed method is effective in mapping the LRE wetlands and provides reliable data support for wetland ecological protection and restoration. [ABSTRACT FROM AUTHOR]

Secondary metabolites have high ecological and economic values. Measuring the amounts of these metabolites in plants through laboratory analysis is time-consuming and expensive. On the other hand, the laboratory results cannot be generalized to a wide area of rangelands with diverse climates, topography, and soil. Therefore, the present study aims to evaluate the potential of PRISMA hyperspectral data to estimate the rangeland phenol concentration at landscape scale in western Isfahan province, Iran. Phenol concentration and spectral signatures were extracted from 42 plant individuals, including Astragalus adscendens, Astragalus verus, Daphne mucronata, Anabasis aphylla, and Phlomis olivieri using laboratory methods and spectroscopy in the range of 350 to 2500 nm. Phenol-sensitive bands were identified by establishing a relationship between phenol concentration and all spectral bands by the Partial Least Square Regression (PLSR) method. The map of phenol spatial changes was then produced with the Bayesian Regularized Artificial Neural Network Model (BRNN), Random Forest (RF), Generalized Linear Model (GAM), Generalized Additive Model (GLM), and Gaussian Process Regression (GPR) in rangelands. The results showed that the phenol concentration in different individuals was between 3.65 and 20.13 (mgGAE/gDW), and species spectral absorptions ranged from 1.650 to 1.660. The very high coefficient of determination of phenol concentration with spectral data (R2 = 0.94) confirmed the use of these data in phenol mapping at the landscape scale. The maximum estimated phenol concentration by GLM, GAM, RF, BRNN, and GPR models was 36.40, 26.76, 18.07, 22.74, and 32.92 GAE/g DW, respectively; which RF model had the highest (R2 = 0.955) and GLM had the lowest accuracy (R2 = 0.816). The findings show that hyperspectral data can estimate secondary metabolites at the landscape scale and can supplement laboratory methods in managing rangeland plants. [ABSTRACT FROM AUTHOR]

Conditioning process intensification is necessary for improving the fly ash flotation effect. Understanding the relationship between conditioning parameters and flotation results is crucial for optimizing the conditioning-flotation process. This study developed a machine learning framework for predicting the yield, loss on ignition, and removal rate of unburned carbon using data from conditioning-flotation experiments. Feature variables of conditioning speed, impeller type, stirred tank diameter, collector dosage, and frother dosage were employed to develop five machine learning models. After tuning the hyper-parameters with GridSearchCV technique, it was found that Random Forest Regression model, eXtreme Gradient Boosting Regression model, and Gradient Boosting Regression model demonstrated good agreements between the predicted data and test data for the yield, loss on ignition, and removal rate of unburned carbon. The maximum R2 values were 0.9184, 0.8339, and 0.9434, respectively, while the minimum MSE values were 3.30, 5.82, and 20.59, respectively. The interpretable analysis using SHapley Additive exPlanations (SHAP) method indicated that conditioning speed was the most significant feature variable affecting the yield, loss on ignition, and removal rate of unburned carbon, while impeller type had a secondary impact. This investigation is expected to reveal the important role of conditioning process intensification in optimizing fly ash flotation. [ABSTRACT FROM AUTHOR]

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification. [ABSTRACT FROM AUTHOR]

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utilizes National Poison Data System (NPDS) data. The severity of outcomes was derived from the NPDS Coding Manual. Our database was divided into training (70%) and test (30%) sets. We used a light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) to predict the outcomes of methadone poisoning. A total of 3847 patients with methadone exposures were included. Our results demonstrated that machine learning models conferred high accuracy and reliability in determining the outcomes of methadone poisoning cases. The performance evaluation showed all models had high accuracy, precision, specificity, recall, and F1-score values. All models could reach high specificity (more than 96%) and high precision (80% or more) for predicting major outcomes. The models could also achieve a high sensitivity to predict minor outcomes. Finally, the accuracy of all models was about 75%. However, XGBoost and LGBM models achieved the best performance among all models. This study showcased the accuracy and reliability of machine learning models in the outcome prediction of methadone poisoning. [ABSTRACT FROM AUTHOR]

Crashes involving Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been increasing in recent years. Understanding the characteristics of these crashes can guide the optimization of driving automation systems and the policies improving the safety of mixed traffic. However, due to the limited available data, the crashes of ADS- and ADAS-controlled vehicles are still under-investigated. Thus, utilizing the latest National Highway Traffic Safety Administration crash reports, our study explores the patterns and contributing factors of ADAS- and ADS-involved crashes. The sequences of events leading to crashes were extracted from the reports and then categorized into five clusters. Next, for incomplete records, a non-parametric imputation method was applied based on Random Forest. Finally, logistic regression models were built to explore the factors associated with the crashes. The results show that the automation level, speed limit, and vehicle speed are predictors of crash patterns. At the same time, the crash pattern, combined with incident time, roadway type, roadway surface, and vehicle model year are associated with crash outcomes (i.e. contact area and injury severity). The results indicate that further improvement of the ADS/ADAS control algorithms and driver education may be needed to improve the safety of mixed traffic. [ABSTRACT FROM AUTHOR]

In ordinal classification problems, the class value exhibits a natural order. Usually, these problems are solved as multiclass classification problems while discarding the ordering form of the class. Recently, several research studies have proposed novel methods for ordinal classification problems while aiming to predict a predefined objective value. These methods are based on modification of the splitting criteria of decision tree-based algorithms. These methods consider the ordinal nature of the data and the magnitude of the potential classification error from a predefined predicted objective. This research aims to consolidate these methods and generalize them for any decision tree-based method while constructing decision trees according to any predefined objective value calculated from the training dataset. Furthermore, to evaluate the proposed method, a comprehensive experimental study is performed based on known ordinal datasets. The proposed method is found to significantly outperform its counterpart nonordinal models. This observation confirms that ordering information benefits ordinal methods and improves their performance. Additionally, the ordinal decision tree-based methods achieved competitive performance compared to state-of-the-art ordinal techniques. The results are validated with various performance measures that are commonly used for ordinal and non-ordinal classification problems. [ABSTRACT FROM AUTHOR]

The limitations of Content Based Video Retrieval (CBVR), such as large pools of video data, selection of features, limited processing capacity, and content-related issues, can be overcome by Deep Belief Neural Networks (DBNs). The search engine does the processing, and the results are effectively returned to the users. The deep learning model also has some comparable challenges to be solved at various levels. Typically, the selection of frames is one of the most essential tasks for a face retrieval system which is efficiently accomplished by using deep learning models. Also, the performance of recognition models highly depends on the selection of frames that include both low-level and high-level features from the given video sequence. This paper proposes ALPHA-TO-OMEGA (ALMEGA-VIR) Visual Information Retrieval scheme that analyzes the contents of the video file for a particular face and retrieves the user's relevant videos. VIR aims to solve the challenge of locating relevant pictures and videos based on a query. VIR can be based on text-based search, content-based search, and Mixed based search. VIR here focuses on content-based search approach. The YouTube Celebrities (YTC) dataset, the Youtube Face (YTF) dataset, and Honda / UCSD dataset are used in this work. The used dataset addresses issues such as illumination changes, occlusion, rotation, and scale. We propose an ALMEGA-VIR framework for visual novelty face identification for specific investigation and evaluation. The classification accuracy of the proposed ALMEGA-VIR model shows that 4% is more effective than traditional classifier algorithms such as SVM, Naïve Bayesian classifier, MLP and Random Forest classifier. A comparative study is also implemented with other existing works to check the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The punctuality of vessel arrival at port is a crucial issue in contemporary port operations. Uncertainties in vessel arrival can lead to port handling inefficiency and result in economic losses. Although vessels typically report their estimated time of arrival (ETA) en-route to the destination port, their actual time of arrival (ATA) often differs from the reported ETA due to various factors. To address this issue and enhance terminal operational efficiency, we first quantitatively evaluate vessel arrival uncertainty in different time slides prior to arrival at the port using 2021 vessel arrival data for Hong Kong port (HKP). Our results confirm that the overall vessel arrival uncertainty decreases as vessels approach the HKP. Then, we implement a random forest (RF) approach to predict vessel arrival time. Our model reduces the error in ship ATA data prediction by approximately 40% (from 25.5 h to 15.5 h) using the root mean squared error metric and 20% (from 13.8 h to 11.0 h) using the mean absolute error metric compared with the reported ETA data. The proposed vessel arrival time evaluation and prediction models are applicable to port management and operation, laying the foundation for future research on port daily operations. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement an interpretable ML to visualize the impacts of factors on crash severity using 5 years of freeways data from Iran. Methods including classification and regression trees (CART), K-nearest neighbours (KNNs), random forest (RF), artificial neural network (ANN) and support vector machines (SVM) were applied, with RF demonstrating superior accuracy, recall, F1-score and ROC. The accumulated local effects (ALE) were utilized for interpretation. Findings suggest that light traffic conditions ( volume / capacity < 0.5) with critical values around 0.05 or 0.38, and higher proportion of large trucks and buses, particularly at 10% and 4%, are associated with severe crashes. Additionally, speeds exceeding 90 km/h, drivers younger than 30 years, rollover crashes, collisions with fixed objects and barriers, nighttime driving and driver fatigue elevate the likelihood of severe crashes. [ABSTRACT FROM AUTHOR]

Ensemble learning synthesizes the advantages of different models and has been widely applied in the field of spatial prediction. However, the nonlinear constraints of spatial heterogeneity on the model ensemble process make it difficult to adaptively determine the ensemble weights, greatly limiting the predictive ability of the ensemble learning model. This paper therefore proposes a novel geographical spatial heterogeneous ensemble learning method (GSH-EL). Firstly, the geographically weighted regression model, geographically optimal similarity model, and random forest model are used as three base learners to express local spatial heterogeneity, global feature correlation, and nonlinear relationship of geographic elements, respectively. Then, a spatially weighted ensemble neural network module (SWENN) of GSH-EL is proposed to express spatial heterogeneity by exploring the complex nonlinear relationship between the spatial proximity and ensemble weights. Finally, the outputs of the three base learners are combined with the spatial heterogeneous ensemble weights from SWENN to obtain the spatial prediction results. The proposed method is validated on the PM2.5 air quality and landslide dataset in China, both of which obtain more accurate prediction results than the existing ensemble learning strategies. The results confirm the need to accurately express spatial heterogeneity in the model ensemble process. [ABSTRACT FROM AUTHOR]

In sewer networks, failure prediction plays a significant role in operation and maintenance plans of wastewater utilities. This study aims to determine the effective variables on the failures by using feature selection algorithms (FS) and achieve maximum model accuracy with minimum variables. Also, four scenarios based on the suggested FS algorithms were developed. In these scenarios, the best prediction models were investigated using machine learning classifiers (ML) such as neural network classifier (NNC), gradient boosting machine (GBM), random forest (FR), and hybrid model (HM). The classification performance of ML models was evaluated using accuracy, precision, F1_score, and receiver operating characteristics (ROC) curve. The model accuracies ranging from 0.99 for accuracy to 1 for the ROC curve were achieved through ML algorithms. In conclusion, the ML algorithms suggested in this study may be a decision support tool for wastewater utilities in prioritizing the replacement, maintenance, and inspection of sewer pipes. [ABSTRACT FROM AUTHOR]

With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models. [ABSTRACT FROM AUTHOR]

Modern manufacturing and quality monitoring involve multi-class out-of-control (OOC) information from the training sample. It is essential to use such information during online monitoring of data streams from complex processes. In this paper, a monitoring framework is designed by combining the random forest technique with the exponentially weighted moving average method for monitoring complex processes with multi-class OOC information. To be specific, a process surveillance technique in the form of a control chart is proposed based on the probability that the online data is classified as an in-control (IC) sample, and the control chart triggers an alarm when the probability is lower than the control limit. Our numerical findings based on the Monte–Carlo simulation show that the proposed control chart performs more effectively than its competitors under various distributions and data types, especially for high-dimensional cases when multi-class OOC information is known in advance. Moreover, the proposed method is illustrated with an application using the data related to the hard disk manufacturing processes. [ABSTRACT FROM AUTHOR]

AbstractForestiera includes woody plants distributed from the United States to Ecuador, with a wide distribution in Mexico. However, basic knowledge about its biology and taxonomy is still limited. In the present study, a leaf morphometric analysis of the 14 Mexican Forestiera species, including F. neomexicana, was carried out to determine if leaf characters allow classifying the species and to evaluate if the inclusion of qualitative features in statistical analyses improves the fit of used methods. Four quantitative and four qualitative variables from herbarium specimens, were evaluated. A matrix was generated which was submitted to multivariate analysis, including Linear Discriminant Analysis and Random Forest. Results revealed that the species are correctly classified, with Random Forest being the method with the best classifying capacity. The most important variables to differentiate the species are the shape of the base, width and margin of the leaves. All species were classified as different entities including F. pubescens and F. neomexicana, currently considered synonyms, were classified by Random Forest as different entities. [ABSTRACT FROM AUTHOR]

Background & objectivesMethods and ResultsConclusionsWorry and loss-related secondary stressors appear to be important correlates of problematic grief responses. However, the relative importance of these variables in the context of established correlates of grief responding, ranging from indicators of identity disruption and demographic characteristics of the bereaved to characteristics of the loss of quality of the relationship with the deceased, is unknown. Modeling the relative associations of these factors can be problematic, given the high degree of collinearity between these variables. This study used a machine learning approach to provide accurate estimations of the relative importance of these correlates for post-loss symptom severity.A convenience sample of 428 bereaved people who had lost a parent, spouse, or child in the last 30 to 365 days completed an online survey. Random forest regression modeling examined the effects of worry and secondary stressors on symptom severity in the context of established correlates. Results indicated worry and the number of secondary stressors experienced were among the factors most strongly associated with severity of grief, depression, posttraumatic stress and problems functioning.These results also provide insight into the relative importance of worry and secondary stressors affecting grief severity to guide future research. [ABSTRACT FROM AUTHOR]

The present study involves the development of a new method, a range-dependent multivariate adaptive regression splines–genetic algorithm (RD-MARS-GA), for monthly streamflow prediction. The results of the RD-MARS-GA model are compared with standard MARS, Gaussian process regression, principal component regression, M5 model tree and random forest. The input data utilized in the study combine satellite and ground-based datasets. Monthly data from the Treene, Schaale, and Wandse rivers in Northern Germany were used as case studies. The satellite datasets used were sourced from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG GPM) and Moderate Resolution Imaging Spectroradiometer Evapotranspiration (MODIS). A comparison of the results reveals that the RD-MARS-GA can improve the forecasting accuracy of the MARS model across all watersheds at different temporal scales. The RD-MARS-GA model surpassed the other regression models and improved the root mean square accuracy of the MARS model by 12.8%, 26.7% and 18.5% in predicting one-month-ahead streamflow of the rivers Treene, Schaale, and Wandse, respectively. [ABSTRACT FROM AUTHOR]

AbstractBackgroundMethodResultsConclusionsCurrently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the process of evaluating and collecting medical scores of VCE frames according to the existing KOrea-CanaDA (KODA) scoring system by developing an easy-to-use mobile application called artificial intelligence-KODA (AI-KODA) score, as well as to determine the inter-rater and intra-rater reliability of the KODA score among three readers for prospective AI applications, and check the efficacy of the application.From the 28 patient capsule videos considered, 1539 sequential frames were selected at five-minute intervals, and 634 random frames were selected at random intervals during small bowel transit. The frames were processed and shifted to AI-KODA. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated 2173 frames in duplicate four weeks apart after completing the training module on AI-KODA. The scores were saved automatically in real time. Reliability was assessed for each video using estimate of intra-class correlation coefficients (ICCs). Then, the AI dataset was developed using the frames and their respective scores, and it was subjected to automatic classification of the scores via the random forest and the k-nearest neighbors classifiers.For sequential frames, ICCs for inter-rater variability were ‘excellent’ to ‘good’ among the three readers, while ICCs for intra-rater variability were ‘good’ to ‘moderate’. For random frames, ICCs for inter-rater and intra-rater variability were ‘excellent’ among the three readers. The overall accuracy achieved was up to 61% for the random forest classifier and 62.38% for the k-nearest neighbors classifier.AI-KODA automates the process of scoring VCE frames based on the existing KODA score. It saves time in cleanliness assessment and is user-friendly for research and clinical use. Comprehensive benchmarking of the AI dataset is in process. [ABSTRACT FROM AUTHOR]

Grasslands are one of the most widespread biomes on Earth. The interaction of UAV LiDAR with grasses and herbaceous plants is an infrequently covered area of research, apart from its utilisation for the estimation of above ground biomass. To evaluate the applicability of LiDAR for monitoring grassland vegetation, two model plots in the arctic-alpine tundra (Krkonoše Mountains, Czech Republic) were selected. Throughout the growing season, UAV LiDAR point clouds (800 points/m2) and multispectral imagery (9 cm) were acquired in monthly intervals, along with reference botanical and terrestrial LiDAR data. The study provides insight into the analysis of compact low-lying vegetation at the species level. A set of experiments was conducted focusing on the analysis of LiDAR information loss, vertical strata, and structural metrics computed over the grass species/communities. Random forest was used to determine the importance of metrics by out-of-bag permutation of predictors and to classify vegetation species using UAV LiDAR-based metrics, as well as image-based digital surface models alone and in fusion with multispectral data. The vertical distribution of the UAV LiDAR points varied significantly between species and throughout the growing season. Loss at the canopy bottoms was apparent, with the lowest points corresponding to dry grass matter rather than relief. Grasslands had the highest penetration capability at the start of the growing season. In terms of metrics, maximum canopy height was the most important. The multitemporal LiDAR-derived structural metrics were able to differentiate (F-1 score above 90%) all the shrubs/trees and dominant grass Calamagrostis villosa. Mixed and low-abundance species were indistinguishable. The overall accuracy scores in a 9 (27) cm grid reached 75.1% (78.1) and 63.5% (66.4) for Bílá louka meadow and Úpské rašeliniště bog, respectively. Fusing the LiDAR-derived features with multispectral imagery did not enhance the classification results apart from the delineation of shrubs and trees. [ABSTRACT FROM AUTHOR]

This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the algorithm accuracies increased with RF from 85% to 97% due to the increased temporal resolution. It was also noticed that the RF performed better with an accuracy of 85% than the NN with accuracy 80% when the temporal resolution was smaller, indicating the RF will be efficient when quick-response time is important. As the window size increases, the NN showed higher performance, suggesting that NN will be useful when higher accuracy is required. Future studies should increase the sample size, include more hand gestures, use different feature extraction methods and test different algorithms to improve the accuracy and efficiency of the system. [ABSTRACT FROM AUTHOR]

AbstractPer- and polyfluoroalkyl substances (PFASs), one of the persistent organic pollutants, have immunosuppressive effects. The evaluation of this effect has been the focus of regulatory toxicology. In this investigation, 146 PFASs (immunosuppressive or nonimmunosuppressive) and corresponding concentration gradients were collected from literature, and their structures were characterized by using Dragon descriptors. Feature importance analysis and stepwise feature elimination are used for feature selection. Three machine learning (ML) methods, namely Random Forest (RF), Extreme Gradient Boosting Machine (XGB), and Categorical Boosting Machine (CB), were utilized for model development. The model interpretability was explored by feature importance analysis and correlation analysis. The findings indicated that the three models developed have exhibited excellent performance. Among them, the best-performing RF model has an average AUC score of 0.9720 for the testing set. The results of the feature importance analysis demonstrated that concentration, SpPosA_X, IVDE, R2s, and SIC2 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. In conclusion, this study is the first application of ML models to investigate the immunosuppressive activity of PFASs. The variables used in the models can help understand the mechanism of the immunosuppressive activity of PFASs, allow researchers to more effectively assess the immunosuppressive potential of a large number of PFASs, and thus better guide environmental and health risk assessment efforts. [ABSTRACT FROM AUTHOR]

Machine learning-based prediction of the output characteristics of a negative capacitance (NC) double gate (DG) Tunnel FET (TFET) (NC DG TFET) has been demonstrated in this paper. On current (ION), OFF current (IOFF) and sub-threshold slope (SS) have been predicted for different values of the body thickness (TSi), oxide thickness (TOX) and channel length (LCh) and the work function (WF). Random Forest Regression (RFR) model is used in the ML framework to explore the device characteristics. A total of 5444 simulated data has been generated by using the SILVACO ATLAS tool. After pre-processing the data, RFR-ML has been applied to obtain maximum accuracy. Root means square error (RMSE) (~0.34), R2 score (~0.965) and accuracy (~98.51%) parameter have been used as the figures of merit (FOM) to judge the efficiency of the proposed model with respect to the state-of-the-art literature. Besides, with higher accuracy, the model is capable of predicting the data within a few seconds compared to the few hundred hours using TCAD tools. This fast and accurate design strategy will surely be efficacious for the present VLSI industries. [ABSTRACT FROM AUTHOR]

Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection. [ABSTRACT FROM AUTHOR]

This paper studies the combination of random forest (RF) and classical statistical modeling. We propose two algorithms: RF cluster + ridge and RF regression + ridge, in which the RF reduces overfitting and has a good anti-noise ability. The ridge regression can effectively handle the correlated data by the ridge penalty. To test the performance of the proposed techniques, we model and track the S&P 500 index. Index tracking aims to replicate the returns of an index fund and is very popular in finance. We compare the performance of the proposed methods with those of several existing methods. The portfolio obtained by each technique is quantified using the tracking error. We find that this combination always has higher accuracy. Empirical results show that the proposed methods are qualified for index funds. Our findings shed light on the synthesis of machine learning and statistical modeling techniques and provide an effective practice of this combination. [ABSTRACT FROM AUTHOR]

The historical atlases provide a wealth of information about the evolution of geography over time and space. The alignment of geographical entities across varying time periods is a crucial aspect of extracting meaningful insights into the spatio-temporal dynamics of geography. This paper proposes a geographic entity alignment approach coupling Natural Gradient Boosting (NGBoost) with SHapley Additive exPlanation (SHAP). Taking the historical atlas of China as a case study, a geographic entity alignment model based on NGBoost is constructed considering the different kinds of similarity features of geographic entities, including semantic, distance, shape, size and topology. The contribution of similarity features in the NGBoost model is analyzed using the SHAP framework so as to improve the explanatory capacity of the model. The spatio-temporal evolution relationships of geographic entities are generated by association rules depending on alignment types and represented as quadruples, for constructing geographic knowledge graphs. The proposed NGBoost method was found a superior accuracy by comparing with BP neural networks, random forests, and other alternative methods for aligning geographic entities. The constructed geographic spatio-temporal evolution knowledge graphs offer valuable support for the queries of evolutionary knowledge. [ABSTRACT FROM AUTHOR]

Machine learning can offer an efficient method to identify and map caves, sinkholes, and other cave-like features (i.e. sinkholes, rockshelters, voids) using remotely sensed imagery. While there exists a body of work applying machine learning for sinkhole identification, little work exists for caves. In the densely forested and rugged Maya Lowlands, developing such a methodology can help archaeologists to identify previously unknown caves that may contain important archaeological materials. Here, we introduce a proof-of-concept project that uses random forest and lidar-derived landscape morphometrics to map caves and other cave-like features in northwest Belize. Several undocumented caves and cave-like features were identified in our study area based on model results. Next steps towards making a more robust version of this model include the addition of more training data and integration of a larger number of morphologic parameters. Based on the results described here as well as those in cited works focused on caves, we proposed machine learning as a first step in cave and cave-like feature identification, followed then by fieldwork and ground-truthing. [ABSTRACT FROM AUTHOR]

Poverty threatens human development especially for developing countries, so ending poverty has become one of the most important United Nations Sustainable Development Goals (SDGs). This study aims to explore China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model. The poverty reduction efficiency (PRE) is measured by the difference in the out-of-poverty rates (which measures the probability of being not poor) of 2016 and 2019. The study shows that the probability of poverty in all regions of China has shown an overall decreasing trend (PRE = 0.264), which indicates that the progress in poverty reduction during this period is significant. The Hu Huanyong Line (Hu Line) shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest China. From 2016 to 2019, the centroid of China's out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line, indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to 2019. The results imply that the government's future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor areas. This study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government's poverty reduction policy. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of digital marketing, leveraging data analytics within Enterprise Information Systems (EIS) has become crucial for businesses aiming to understand and engage their customers more effectively. This study presents a comprehensive analysis of data-driven marketing strategies, emphasising the critical role of diverse data sources in developing targeted, EIS-specific campaigns. Central to our investigation is a comparative analysis of various machine learning algorithms, including Random Forest, CART, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, K-Nearest Neighbours, and Naïve Bayes. Our selection and evaluation of these models are grounded in their ability to address the complex dynamics of customer behaviour and marketing data, demonstrating the superior efficacy of Random Forest and CART in this context. [ABSTRACT FROM AUTHOR]

The application of machine learning in salinity tolerance evaluation studies is limited. To address this gap, a pot culture experiment was conducted with six pomegranate cultivars at Punjab Agricultural University Ludhiana, India, in 2020 and 2021. The data were subjected to subordinated function analysis (SFA), random forest regression (RFR), support vector regression (SVR) and general linear modeling (GLM) after 60 and 120 days of first saline water treatment. The SFA identified ‘Bhagwa’ as the most salt tolerant cultivar, followed by ‘Wonderful.’ The RFR highlighted leaf K, catalase activity, and relative growth rate (RGR) as key parameters influencing salinity tolerance of the tested cultivars after 60 days. The SVR demonstrated root S content and net assimilation rate as critical factors. After 120 days, both RFR and SVR identified stomatal conductance and relative growth rate as crucial markers displaying salinity tolerance of cultivars. Both RFR and SVR showed high predictive accuracy, especially after 120 days, compared to GLM. However, GLM provided a broader set of variables for a better understanding of the underlying mechanism across different time intervals. Therefore, this research emphasizes the importance of using both machine learning techniques and traditional statistical approaches to gain a deeper understanding of the subject. [ABSTRACT FROM AUTHOR]

Available water capacity (AWC), field capacity (θFC), and permanent wilting point (θPWP) are regarded as key physical soil health indicators that directly capture the soil's capacity to store plant available water but are expensive components of a comprehensive soil health analysis. To reduce costs, pedotransfer functions for θFC, θPWP, and AWC were developed from a dataset of 7,232 soil samples with texture, soil organic matter (SOM), permanganate-oxidizable carbon, soil respiration, AWC, θFC, θPWP, wet aggregate stability, and extractable potassium, magnesium, iron, and manganese. Three functions were developed for each property: a full random forest (RF) model containing all variables, a reduced RF model and a multiple linear regression model containing texture and SOM. Pedotransfer functions were validated with an independent dataset that contained 1,406 samples. The full RF models for θFC, θPWP, and AWC reduced the root mean square error (RMSE) by 16.3, 13.3, and 12.8%, compared to multiple linear regression models, respectively. Furthermore, the full RF models for θFC, θPWP, and AWC reduced RMSE by 11.6, 6.7, and 12.8%, compared to the reduced RF model, respectively. Permanganate-oxidizable carbon, wet aggregate stability, and extractable magnesium, potassium, and iron were useful novel predictor variables for improving prediction of θFC and AWC. AWC was sensitive in 20/57 long-term experiments, and full RF models were able to replicate 5/20 of those significant results. New RF pedotransfer functions for θFC, θPWP, and AWC can enhance prediction compared to traditional modeling techniques, fits into existing interpretative frameworks, and improves cost-effectiveness of comprehensive assessments of soil health. [ABSTRACT FROM AUTHOR]

Purpose: This study addresses the lack of objective player-based metrics in men’s rugby league by introducing a comprehensive set of novel performance metrics designed to quantify a player’s overall contribution to team success. Methods: Player match performance data were captured by Stats Perform for every National Rugby League season from 2018 until 2022; a total of five seasons. The dataset was divided into offensive and defensive variables and further split according to player position. Five machine learning algorithms (Principal Component Regression, Lasso Regression, Random Forest, Regression Tree, and Extreme Gradient Boost) were considered in the analysis, which ultimately generated Wins Created and Losses Created for offensive and defensive performance, respectively. These two metrics were combined to create a final metric of Net Wins Added. The validity of these player performance metrics against traditional objective and subjective measures of performance in rugby league were evaluated. Results: The metrics correctly predicted the winner of 80.9% of matches, as well as predicting the number of team wins per season with an RMSE of 1.9. The metrics displayed moderate agreement (Gwet AC1 = 0.505) when predicting team of the year award recipients. When predicting State of Origin selection, the metrics displayed moderate agreement for New South Wales (0.450) and substantial agreement for Queensland (0.652). Conclusion: The development and validation of these objective player performance metrics represent significant potential to enhance talent evaluation and player recruitment. [ABSTRACT FROM AUTHOR]

Informative and scalable cartography plays a pivotal role in curbing urban pollution, waste management, and mitigating environmental damage in the development of informal settlements. The contemporary capabilities of cloud computing facilitate streamlined access to comprehensive data repositories, computational infrastructure, and proficient tools that have rapidly advanced the execution of sprawl mapping procedures. This study tests the performance of four machine-learning algorithms, namely: Gradient Boost, K Nearest Neighbor [KNN], Random Forest [RF], and Support Vector Machine [SVM] with data extracted from cloud computing repositories for delineating informal settlements in Gcuwa, Eastern Cape, South Africa, using low-cost datasets. A systematic approach comprising iterative phases, encompassing data acquisition, the development of a training dataset, modeling, and evaluation was employed. The delineation process involved the extraction of both spectral and textural features from Sentinel-2 imagery. The Random Forest algorithm emerged as the top performer, exhibiting the highest levels of accuracy and F1 score, followed by the gradient boosting, support vector machine, and then the K-nearest neighbor algorithms. Consequently, this innovative use of machine learning algorithms with low-cost datasets and the scalable, resilient approach for detecting informal settlements offers a promising avenue for enhancing urban planning and addressing sustainable development challenges. [ABSTRACT FROM AUTHOR]

In small-scale multilingual societies, an individual uses a large number of languages and usually identifies one of them as the primary language. Although empirically observable, primary language has been significantly underexplored, particularly the spatiality of its identification. It is not clear whether and how place is related to why one language might be seen as being more significant than other languages. This study predicts the primary language used by multilingual individuals in an indigenous rural region of Cameroon based on two types of place: the residence of multilingual individuals and the birthplace of their family members. The research goals are supported by a random forest model using spatial-sociolinguistic data of the region. Results show that primary language can be predicted at a high accuracy. The residence of multilingual individuals and the birthplace of their family members capture the spatiality behind primary language identification. This work adds to the current discussion of the space–language connection and is valuable for maintaining the endangered languages in small-scale rural societies. [ABSTRACT FROM AUTHOR]

In this paper, the spoke structure of a non-pneumatic tire is optimized. The finite element model of the spoke structure is created and structural dynamic analysis is made using ABAQUS. The optimization goal is to simultaneously reduce three characteristics of the spoke structure, the mass, vertical displacement and acceleration. Simulations are performed according to the center composite design table, and the objective function is estimated using response surface analysis (RSA), random forest regression (RFR), gradient boosting regression (GBR) and artificial neural network (ANN). The genetic algorithm is used for minimization, and the optimization results of each objective function are analyzed through verification simulation. As a result of the optimization, the error between the predicted value and the actual value is 18.6% for RSA 3.1% for RFR, 1.7% for GBR, and 15.0% for the ANN. The ANN proposes the optimum design variables which derive output values smaller than the current minimum value because the ANN properly represents the nonlinear characteristics of the spoke model generated in this study. Highlights Simulation based optimization of the spoke structure of a non-pneumatic tire Various regression models are used and their accuracy is compared A two-step genetic algorithm is used for optimization [ABSTRACT FROM AUTHOR]

Species extinction and population extirpation are now widespread across aquatic ecosystems with many diadromous species, including Atlantic salmon (Salmo salar), in decline throughout much of the North Atlantic. Declines can occur simultaneously at both large and small regional scales rendering factors driving the decreases more elusive. On the south coast of Newfoundland abundance of Atlantic salmon at Conne River fell by 92% over a period of almost four decades in contrast with most other populations in the region suggesting local factors may be contributing to the decline. Here factors potentially contributing to the long-term decline of salmon are reviewed by (1) examining long-term trends in abundance and survival at different life stages, (2) presenting a synopsis on the presence and absence of factors impacting survival and productivity of this population, (3) using a semi-quantitative two dimensional classification system, based on expert opinion, to rank factors potentially contributing to the decline, and (4) utilizing a quantitative Random Forest analysis to complement the expert opinion approach in identifying factors possibly affecting salmon abundance in this south coast Newfoundland population. Results from both qualitative and quantitative analyses identified factors associated with salmon aquaculture as a possible driver of the decline. Additional factors include the influence of both climate change and predation in freshwater and marine habitats. As various Atlantic salmon populations across the native range approach extirpation, the results further highlight the necessity of river-specific analyses in addition to long-term monitoring and fine-scale demographic and threat information in the prioritization of research necessary for conserving or restoring endangered populations. [ABSTRACT FROM AUTHOR]

Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This article introduces a cross-validation method, Extrapolated Cross-Validation (ECV), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields δ-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests under a computational constraint on the maximum ensemble size. Compared to sample-split and K-fold cross-validation, ECV achieves higher accuracy by avoiding sample splitting. Meanwhile, its computational cost is considerably lower owing to the use of the risk extrapolation technique. for this article are available online. [ABSTRACT FROM AUTHOR]

The nature of the abuse, cultural and religious values, trauma-related cognitions, and recovery actions are considered factors that shape intimate partner violence (IPV) survivors' recovery and pathology. However, less is known about their specific impact on women's psychopathology and wellbeing. Concomitantly, there is scant information about IPV survivors from collectivistic societies such as the Israeli Jewish Ultra-orthodox (JUO) community. The present study was designed to identify predictors of post-traumatic stress (PTSD) symptoms and wellbeing in women from the JUO community who have experienced IPV. Women (N = 261) provided information about their demographics, the nature of the violence, attitudes with respect to cultural and religious norms that normalize violence, trauma-related cognitions, the coping constructs of disengagement, faith, and engaging in help-seeking and recovery actions, and the PTSD symptoms that affect their wellbeing. A Random Forest machine learning (ML) algorithm was used to identify the strongest predictors of psychopathology and wellbeing. Regression trees were developed to identify individuals at greater risk of PTSD symptoms but also of greater wellbeing. Higher self-stigma and the perception of an unsafe world were associated with PTSD symptoms, whereas lower self-stigma, greater faith, and engagement in steps toward recovery were associated with greater wellbeing. These findings highlight the importance of treating women's self-stigma and perceptions of an unsafe world while also encouraging faith and active engagement in recovery to promote survivors' wellbeing and lessen their PTSD symptoms. [ABSTRACT FROM AUTHOR]

Two-vehicle crashes resulting from distracted driving led to a higher number of fatalities and serious injuries over time. This study utilized machine learning and econometric models to investigate two-vehicle-involved distracted driving crashes from the Crash Report Sampling System within the United States. XGBoost and Random Forest were utilized to identify the top variables based on SHAP value, although mixed logit with unobserved heterogeneity was used to model injury severity. The model results indicate that there is a complex interaction of driver characteristics, such as demographics (male drivers), driver actions (careless driving, driving more than the speed limit of more than 15 mph, hitting a stopped vehicle), a driver without violation history, turning violation, drinking, roadway characteristics (non-interstate highways, undivided and divided roadways with positive barrier, curved roadways, dry surface), environmental conditions (rainy weather), vehicle attributes (motorcycle, displacement volume up to 2500 cc, newer vehicle within five years of crash-involvement), temporal characteristics (4–6 PM, July–September, and year 2017). These findings underscore the importance of driving behavior and roadway design. As such, prioritizing efforts to address distracted driving behavior through driver training and law enforcement, as well as considering its implications for roadway design and maintenance, becomes crucial. [ABSTRACT FROM AUTHOR]

Under varying operational conditions, the contact and relative movement of a polymer and metal result in surface wear, accompanied by the emission of noise. The relationship between friction noise and wear is inherently complex and nonlinear. In light of these tribological characteristics, this paper introduces the implementation of a random forest algorithm and generalized regression neural network algorithm to establish a mathematical model for predicting the wear rate based on friction noise. To enhance the accuracy of wear rate regression, this study incorporates L2 norm feature selection and the sparrow search algorithm, which are tailored toward the friction characteristics. These techniques optimize the machine learning-based friction model, ultimately improving the regression accuracy of the wear rate. [ABSTRACT FROM AUTHOR]

The impact of an avatar on real-world behaviors of users is known as the Proteus Effect. Different user avatar bond (UAB) aspects, including identifying, immersing, and compensating via the avatar, influence an individual’s Proteus Effect propensity. This study aimed to use machine learning (ML) classifiers to automate the prediction of those likely to experience Proteus Effect, based on their reports of identifying, immersing, and compensating with their avatar. Participants were 565 gamers (Mage = 29.3 years; SD = 10.6), assessed twice, six months apart, using the User-Avatar-Bond Scale and the Proteus Effect Scale. Tuned and untuned ML classifiers showed ML models could accurately identify individuals with higher Proteus Effect propensity, informed by a gamer’s reported UAB, age, and length of gaming involvement, both concurrently and longitudinally (i.e., six months later). Random forests performed better than other MLs, with avatar identification as the strongest predictor. This suggests higher Proteus Effect propensity for those with a stronger user-avatar bond, informing gamified health applications to introduce adaptive behavioral changes via the avatar. Prevention and practice implications are discussed. [ABSTRACT FROM AUTHOR]

AbstractThe L-asparaginase is commercial enzyme used as chemotherapeutic agent in cancer treatment and food processing agent in backed and fried food industries. In the present research work, the artificial intelligence and machine learning techniques were employed for modeling and optimization of fermentation process conditions for enhanced production of L-asparaginase by submerged fermentation of Aspergillus terreus. The experimental L-asparaginase activity obtained using central composite experiment design was used for optimization. The Random Forest algorithms machine learning techniques was found best based on the analysis of regression coefficient of ANN model and metric score values of machine learning algorithms. The experimental L-asparaginase activity of 41.58 IU/mL was obtained at the Random Forest algorithm predicted fermentation process conditions of temperature 31 °C, initial pH 6.3, inoculum size 2% (v/v), agitation rate 150 rpm and fermentation time 66 h. [ABSTRACT FROM AUTHOR]

There are challenges for cloud detection over bright surfaces, especially for Sentinel-2 image without thermal band. In this paper, we developed a new cloud detection algorithm for Sentinel-2 data designed to tackle the challenges of cloud detection on bright targets. Our algorithm (called RF-NDTeI-SNIC) was built through the integration of Random Forest (RF) model, Normalized Difference Temporal Index (NDTeI) cloud index and Simple Non-Iterative Clustering (SNIC) image segmentation algorithm. First, RF and NDTeI were used to derive the initial and second stage's cloud detection results, respectively. Then, the RF- and NDTeI-based results were fused to remove the bright surfaces and land cover changes. Finally, the fusion results were further refined using SNIC for morphological processing. The proposed RF-NDTeI-SNIC algorithm was evaluated using nine challenging cloud-covered Sentinel-2 scenes with substantial bright surfaces. Results indicated that the average overall accuracy of our algorithm was 95.01%, with average commission rate of 10.19% for cloud. In addition, five commonly used Sentinel-2 cloud mask algorithms including s2cloudless, QA, Fmask, CDI and Tmask were selected for comparative analysis. Results suggested that our algorithm outperformed others with overall accuracies of 89.60–94.17% and cloud commission rate of 9.46–37.50%, and have significant advantages in terms of bright surfaces. In summary, the RF-NDTeI-SNIC algorithm we developed was capable of yielding accurate Sentinel-2 cloud masks, and the novel NDTeI cloud index we proposed gave a new and effective approach for improving the separability of clouds and bright surfaces. [ABSTRACT FROM AUTHOR]

AbstractThe complex dielectric permittivities were determined using a precision LCR meter of the binary mixtures of a polar liquid (methyl ethyl ketone, or MEK) and a non-polar liquid (dimethyl silicone fluid, or DMSF) at 303.15 K temperature. After determining the complex impedance (Z* (ω)) using the complex permittivity portion (ɛ* (ω)) of polar and nonpolar liquids, the complex impedance data (Z* (ω)) was fitted to the Nyquist Plot. Our research described here investigated the impact of ionic impurities in the pure MEK, DMSF and in their mixed state. The established parameters provided information on the impact of concentration changes on the electrical characteristics of the binary mixtures. Our research delved into utilizing machine learning (ML) techniques, such as random forest (RF), LightGBM, and XGBoost regressors, to improve the material design and prediction modeling. The main objective was to assess the efficacy of various regression techniques in evaluating the material characteristics performance. Experiments spanning from 30% to 50% of the data set were carried out, with performance metrics, like, R2 score and MAE, being utilized. Notably, the RF regressor demonstrated outstanding performance in these evaluations with an R2 score of 0.9999. The simulation results indicated that the ML-based techniques offer resource and time savings, serving as effective tools for predicting material performance at intermediate frequencies. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a high-resolution lidar instrument, facilitating landscape monitoring through forest structure and biomass assessments. Previous research has mainly focused on estimating forest attributes such as height and biomass from GEDI data. In this comprehensive study, we empirically investigate how Eucalyptus plantation characteristics and environmental factors collectively influence GEDI waveform metrics. Using a diverse dataset that includes measurements of canopy height, planting density, foliage characteristics, species composition, understorey presence and environmental conditions such as climate and soil properties, we explore the complex relationships between these attributes and GEDI waveform metrics using a two-step approach that includes assessing the linear relationship between each in-situ parameter and GEDI metrics, and also random forest modelling. Key findings include: 1) While canopy height (Ht) plays a significant role in shaping the GEDI waveform, forest wood volume (Volume), which incorporates height and diameter at breast height (DBH) of the trees and also tree stem density (Ntree/ha), exerts an even greater influence on GEDI metrics, in particular on the waveform extent (WE) and relative heights (RH); 2) The influence of factors such as foliage biomass and wood volume on GEDI waveform varies vertically from the forest floor up to forest top, and the model was more precise to predict the top part of the waveform; 3) No single attribute can solely account for the observed variations in the waveform characteristics, but multiple interactions between different forest and environmental features contribute to the complex patterns in the received waveforms. This research contributes to a deeper understanding of the complex relationships between forest plantation characteristics and GEDI waveform metrics and highlights the need to consider a wide range of forest attributes in the analyse of GEDI waveform to produce more accurate canopy height or volume estimates. [ABSTRACT FROM AUTHOR]

Dolphins are social animals that depend on sound to communicate, navigate, and find food. The aim of our research was to describe dolphin whistle characteristics and compare whistles between two isolated populations of bottlenose dolphins in New Zealand. We focused on three aspects of dolphin bioacoustics (1) classification of whistles; (2) comparison of the whistles of two discrete bottlenose dolphin populations; and (3) comparison of whistles of dolphin populations from around the world. At Great Barrier Island, the most common type of whistle was upsweep followed by sine, while at Doubtful Sound, convex and sine whistles were the most common whistle types. Random Forest models discriminated between whistles from the two populations with an estimated accuracy of 90%. The key differences were whistle-type contour, duration, and end frequency. Linear Mixed models show that whistle acoustic parameters were influenced by location but not by group size or the presence of immatures. Whistle parameters of both populations fell within the range of multiple populations from around the world. This studyprovides a benchmark for future research into drivers of geographic variation in dolphin vocal communication. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of constructing a nonparametric predictor when the latent variables are given with incomplete information. The convenient predictor for this task is the random forest algorithm in conjunction to the so-called CART criterion. The proposed technique enables a partial imputation of the missing values in the data set in a way that suits both a consistent estimator of the regression function as well as a partial recovery of the missing values. The imputation is done through iterative assignation of the missing values to the tree's cells, maximising the CART criterion. A proof of the consistency of the random forest estimator is given in the case where each latent variable is missing completely at random (MCAR). [ABSTRACT FROM AUTHOR]

This research focuses on understanding the spatial variation of Soil Organic Matter (SOM) and pH levels in the North of Morocco. The study employs a comprehensive approach to enhance predictive modelling, incorporating the Boruta algorithm for effective environmental covariates selection and optimizing model parameters through hyperparameter optimization. Utilizing a Random Forest (RF) model with remote sensing indices and topographic features, the research predicts SOM and pH to identify key contributors to their spatial variability. SOM prediction saw significant success, with a notable correlation to remote sensing indices such as the RVI, NDVI, and TNDVI. These indices, indicative of vegetation health and productivity, emerged as primary influencers of SOM. In comparison, the influence of topographic features like elevation, slope, and aspect was found to be less significant. Conversely, predicting pH was challenging due to the minimal spatial variability within the dataset. Addressing this limitation could involve dataset expansion or alternative models for low-correlated data handling. Despite the RF model's limited efficacy in pH prediction, an observable correlation between SOM and pH was identified, consistent with prior research. Areas with higher SOM exhibited lower pH values, indicating relative soil acidification from organic matter decomposition. The study's RF model demonstrated potential in SOM prediction using remote sensing indices, but enhancing pH prediction is essential. Future research may explore dataset expansion, diverse sampling, or testing alternative predictive models for better performance with low-correlated datasets. The study offers valuable insights for advanced predictive model development and enriches understanding of soil management practices. [ABSTRACT FROM AUTHOR]