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Machine learning (ML) is effective to establish the complicated trilateral relationship among structures, properties, and photovoltaic performance, which is fundamental issue in developing novel materials for improving power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, we constructed the database of 397 donor–acceptor pairs of OSCs with photovoltaic parameters and descriptor sets, which include donor–acceptor weight ratio within the active layer of the OSCs, root mean square of roughness, and 1024-bit Morgan molecular fingerprint for donor (Fp-D) and acceptor (Fp-A). The ML models random forest (RF), adaptive boosting (AdaBoost), extra trees regression, and gradient boosting regression trees were trained based on the descriptor set. The metrics determination coefficient (R2), Pearson correlation coefficient (r), root mean square error, and mean absolute error were selected to evaluate ML model performances. The results showed that the RF model exhibits the highest accuracy and stability for PCE prediction among these four ML models. Moreover, based on the decomposition of non-fullerene acceptors L8-BO, BTP-ec9, AQx-2, and IEICO, 20 acceptor molecules with symmetric A–D–A and A–π–D–π–A architectures were designed. The photovoltaic parameters of the designed acceptors were predicted using the trained RF model, and the virtual screening of designed acceptors was conducted based on the predicted PCE. The results indicate that six designed acceptors can reach the predicted PCE higher than 12% when P3HT was adopted as a donor. While PM6 was applied as a donor, five designed acceptors can achieve the predicted PCE higher than 16%. [ABSTRACT FROM AUTHOR]

Local morphometric variables (LMVs) are frequently found as weaker predictors than other environmental covariates in digital soil mapping. This study tested and evaluated the performance of a hybrid approach combining gradient boosted regression trees (GBRT) and regularized regression (RR) algorithms in predicting soil textural fractions using a set of LMVs in Khuzestan province, Iran. Here five LMVs (slope gradient, slope aspect, horizontal curvature, vertical curvature, and contour geodesic torsion) were derived from a spheroidal equal-angular DEM as original predictors. The results demonstrated that the hybrid approach improved prediction accuracy for sand, clay, and silt contents by an average of 56% compared to the GBRT models. The importance analysis revealed the significant contribution of tree-based variables obtained from decomposing GBRT models in predicting soil textural fractions. This approach could be recommended for digital soil mapping, particularly in situations of limited environmental covariates or geomorphometric techniques that cannot be easily applied. [ABSTRACT FROM AUTHOR]

• LULC changes of three decades from 1991 to 2021 were examined and predicted. • Multi-spectral remote sensing data and RFT were utilized to generate LULC maps. • ACR were considered to analyze the categorical land use change detection. • Impervious land has expanded 10 times within 30 years and will increase in the future. The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) have measurable impacts on marine ecosystems and reliant fisheries and associated communities. However, how MHWs translate to changes in fishing opportunities and the displacement of fishing fleets remains poorly understood. Using fishing vessel tracking data from the automatic identification system (AIS), we developed vessel distribution models for two pelagic fisheries targeting highly migratory species, the U.S. Atlantic longline and Pacific troll fleets, to understand how MHW properties (intensity, size, and duration) influence core fishing grounds and fleet displacement. For both fleets, MHW size had the largest influence on fishing ground area with northern fishing grounds gaining and southern fishing grounds decreasing in area. However, fleet displacement in response to MHWs varied between coasts, as the Atlantic longline fleet displaced farther in southern regions whereas the most northern and southern regions of the Pacific troll fleet shifted farther. Characterizing fishing fleet responses to these anomalous conditions can help identify regional vulnerabilities under future extreme events and aid in supporting climate‐readiness and resilience in pelagic fisheries. [ABSTRACT FROM AUTHOR]

Interval‐censored data arise when a failure process is under intermittent observation and failure status is only known at assessment times. We consider the development of predictive algorithms when training samples involve interval censoring. Using censoring unbiased transformations and pseudo‐observations, we define observed data loss functions, which are unbiased estimates of the corresponding complete data loss functions. We show that regression trees based on these loss functions can recover the tree structure and yield good predictive accuracy. An application is given to a study involving individuals with psoriatic arthritis where the aim is to identify genetic markers useful for the prediction of axial disease within 10 years of a baseline assessment. [ABSTRACT FROM AUTHOR]

BACKGROUND: Previously, a lexicon and protocol for quantitative descriptive analysis (QDA) was established for the Uganda sweetpotato breeding program. The implication of QDA scores for priority sensory attributes on consumer preference should be determined to interpret results efficiently and make decisions effectively. The present study aimed to develop a gender‐responsive decision tree to obtain an overall sweetpotato eating quality score to facilitate demand‐led targeted breeding selection. It focused on Kamuli and Hoima districts (Uganda) and uses pre‐lease advanced clones ('NKB3', 'NKB105', 'NKB135', 'D11' and 'D20'), released varieties ('NASPOT 8' and 'NAROSPOT 1') and landraces ('Muwulu‐Aduduma', 'Umbrella'). RESULTS: Including boiled sweetpotato sensory characteristics, namely mealy, sweet taste, sweetpotato smell, firm and not fibrous, in breeding design would benefit end‐users, especially women given their role in varietal selection, food preparation and marketing. 'D20', 'NASPOT 8' and 'NAROSPOT 1' were most liked in both districts. 'NKB3' and 'D11' were the least liked in Hoima, whereas 'Muwulu‐Aduduma' was the least liked in Kamuli. There was a positive correlation between color and overall liking (r2 = 0.8) and consumers liked the color (average rating ≥ 6 on a nine‐point hedonic scale) of all genotypes. Threshold values (average rating on 11‐point scales) for consumer acceptability were identified (sweet taste = 6, sweetpotato aroma and flavor = 6, firmness = 3, and mealiness = 4). A regression decision tree tool was created to calculate an eating quality selection index when screening lines in breeding programs using the values. CONCLUSION: Decision trees that include consumer needs and gender considerations would facilitate demand‐led breeding and make varietal selection in sweetpotato breeding programs more effective. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Antimicrobial-resistance genes (ARGs) are spread among bacteria by horizontal gene transfer, however, the effect of environmental factors on the dynamics of the ARG in water environments has not been very well understood. In this systematic review, we employed the regression tree algorithm to identify the environmental factors that facilitate/inhibit the transfer of ARGs via conjugation in planktonic/biofilm-formed bacterial cells based on the results of past relevant research. Escherichia coli strains were the most studied genus for conjugation experiments as donor/recipient in the intra-genera category. Conversely, Pseudomonas spp. Acinetobacter spp. and Salmonella spp. were studied primarily as recipients across inter-genera bacteria. The conjugation efficiency (ce) was found to be highly dependent on the incubation period. Some antibiotics, such as nitrofurantoin (at ≥0.2 µg ml−1) and kanamycin (at ≥9.5 mg l−1) as well as metallic compounds like mercury (II) chloride (HgCl2, ≥3 µmol l−1), and vanadium (III) chloride (VCl3, ≥50 µmol l−1) had enhancing effect on conjugation. The highest ce value (−0.90 log10) was achieved at 15°C–19°C, with linoleic acid concentrations <8 mg l−1, a recognized conjugation inhibitor. Identifying critical environmental factors affecting ARG dissemination in aquatic environments will accelerate strategies to control their proliferation and combat antibiotic resistance. [ABSTRACT FROM AUTHOR]

Induced by climate change associated disturbances and further promoted by changes in forest management, the proportion of European beech, a highly competitive climax species, is increasing in Central Europe. However, its climatic stability is questioned by the recent loss of vitality. In this regard, the evaluation of seed sources used for reforestation receives great attention. Here, we present a multi-trait evaluation of height, basal area and stem quality assessed over 25 years for 85 provenances grown at three sites in northern, western and eastern Germany. Considerable provenance differentiation exists, explaining 21% (proportion of trees with acceptable stem form) to 45% (basal area) of the variance on single sites and 20 to 39% across sites, while provenance by environment interactions are absent. On the landscape level, this results in distinct patterns with spatially adjacent provenances showing a similar trait expression. These patterns are highly similar for height and basal area but divergent for stem form. They could be directly linked to geographic variables with multivariate regression tree analysis that captured 58% of the phenotypic variation, delineating eleven 'ecotypes' shaped by local adaptation. A selection based on two multi-trait indices gives highly concordant results. Particularly, the intermediate elevations of the central highlands in Western Germany host highly suitable provenances. Lower elevation provenances from continental climates in the south-east profit from the transfer to favorable growing conditions. Since the majority of provenances was already exposed to elevated temperatures compared to their origin during the observation period, this study gives a first indication for the potential benefits of assisted migration facing climate change. [ABSTRACT FROM AUTHOR]

This study investigates key performance indicators (KPIs) affecting regional competitiveness in the European Union (EU) and compares its performance across two groups of EU regions identified by their development phase based on the path dependence argument. Utilizing 40 indicators from the Regional Competitiveness Index (RCI) across 268 NUTS2 regions, it identifies five critical KPIs: Knowledge workers, Employment rate, Labor productivity, Potential market size, and not in education, employment, or training. The classification and regression tree decision tree reveals the labor market efficiency pillar as crucial for RCI classification. Offering unique insights into RCI indicators and their impact, this research provides a policy‐oriented perspective and suggests avenues for further comparative research. [ABSTRACT FROM AUTHOR]

This paper examines whether proactive efforts to educate people about disinformation through advertisements can successfully increase skepticism towards false headlines or if such efforts do more harm than good by inadvertently increasing belief in false information. We analyze a survey experiment that employed three different advertisements that directly addressed "fake news." We find that all advertisements were effective at increasing skepticism towards "fake news" headlines. We also find no evidence of backfire effects occurring. However, subsequent analysis using Bayesian additive regression trees (BART) finds significant heterogeneity within these treatment effects. While all advertisements were effective, each ad was effective in different ways despite common themes and content. This suggests a more complicated understanding of the counter-disinformation process and highlights BART's utility in public opinion research. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) can be promising for stratifying patients into homogeneous groups and assessing mortality based on score combination. Using ML, we compared mortality prediction performance for clustered and non-clustered models and tried to develop a simple decision algorithm to predict the patient's cluster membership with classification and regression trees (CART). Methods: Retrospective study involving patients requiring ICU admission (1st January 2011–16th September 2022). Clusters were identified by combining Charlson Comorbidity Index (CCI) plus Simplified Acute Physiology Score II (SAPS II) or Sequential Organ Failure Assessment (SOFA). Intercluster and survival analyses were performed. We analyzed the relationship with mortality with multivariate logistic regressions and receiver operating characteristic curves (ROC) for models with and without clusters. Nested models were compared with Likelihood Ratio Tests (LRT). Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were compared for non-nested models. With the best model, we used CART to build a decision tree for patient's membership. Results: Our sample consisted of 2605 patients (mortality 59.7%). For both score combinations, we identified two clusters (A and B for CCI + SAPS II, α and β for CCI + SOFA). Belonging to cluster B/β was associated with shorter survival times (Peto-Peto p-values < 0.0001) and increased mortality (Odds-ratio 4.65 and 5.44, respectively). According to LRT and ROC analysis, clustered models performed better, and CCI + SOFA showed the lowest AIC and BIC values (AIC = 3021.21, BIC = 3132.65). Using CART (β cluster positive case) the accuracy of the decision tree was 94.8%. Conclusion: Clustered models significantly improved mortality prediction. The CCI + SOFA clustered model showed the best balance between complexity and data fit and should be preferred. Developing a user-friendly decision-making algorithm for cluster membership with CART showed high accuracy. Further validation studies are needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Concrete bridge decks are critical transportation infrastructure components where deterioration can compromise structural integrity and public safety. This study develops machine learning (ML) models using the National Bridge Inventory (NBI) to classify deck conditions and predict deterioration trajectories. Models were tested and trained on inspection records from over 28,786 bridges in Michigan over 23 years, from 1992 to 2015. Eleven approaches were evaluated after hyperparameter optimization, based on 10-fold cross-validation, including logistic regression, gradient boosting, AdaBoost, random forest, extra trees, K-nearest neighbors, naive Bayes, decision tree, LightGBM, CatBoost, and bagging. Model effectiveness was assessed using accuracy, recall, F1-score, and area under the curve. Results indicate the optimized CatBoost classifier achieved 96.66% testing accuracy in rating deck conditions. The incorporation of hyperparameter optimization has significantly enhanced the overall predictive performance of the models, ensuring robust and reliable deterioration forecasting. The research sheds light on crucial factors such as deck age, area, and average daily traffic, contributing to a more comprehensive understanding of the factors influencing bridge deck condition ratings. These insights inform preventative maintenance planning to extend service life. This work pioneers a data-driven framework to forecast concrete deterioration, empowering officials with precise predictions to optimize infrastructure management under budget constraints. The approach provides a promising decision-support tool for sustainable infrastructure. This paper explores the use of machine learning techniques for the deterioration prediction of concrete bridge decks to estimate the remaining service life of bridges. These models will contribute to the safety, efficiency, and sustainability of bridge infrastructure by providing timely information and evidence-based decision making for bridge maintenance and management. Such prediction models have several practical applications such as (1) predicting when maintenance or repairs are likely to be needed; (2) assessing the risk of failure or deterioration of different components of a bridge; (3) effectively managing the bridge life cycle by providing insights into the aging process and helping authorities plan for rehabilitation or replacement strategies; (4) enabling ongoing monitoring of the performance of a bridge under various conditions such as heavy traffic loads, environmental factors, and seismic events; and (5) assisting in effective asset management by allowing for the prioritization of investments, the efficient allocation of budgets, and the planning for the long-term sustainability of the bridge infrastructure. [ABSTRACT FROM AUTHOR]

This paper proposes a new boosting machine based on forward stagewise additive modeling with cost-complexity pruned trees. In the Tweedie case, it deals directly with observed responses, not gradients of the loss function. Trees included in the score progressively reduce to the root-node one, in an adaptive way. The proposed Adaptive Boosting Tree (ABT) machine thus automatically stops at that time, avoiding to resort to the time-consuming cross validation approach. Case studies performed on motor third-party liability insurance claim data demonstrate the performances of the proposed ABT machine for ratemaking, in comparison with regular gradient boosting trees. [ABSTRACT FROM AUTHOR]

This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R2) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R2 values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R2 values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study's implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems. [ABSTRACT FROM AUTHOR]

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree—are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process. [ABSTRACT FROM AUTHOR]

In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a computer. An important question in such a setting is determining the best simulator, or the best combination of simulators, to use for prediction and inference. Bayesian model averaging (BMA) and stacking are two statistical approaches used to account for model uncertainty by aggregating a set of predictions through a simple linear combination or weighted average. Bayesian model mixing (BMM) extends these ideas to capture the localized behavior of each simulator by defining input-dependent weights. One possibility is to define the relationship between inputs and the weight functions using a flexible nonparametric model that learns the local strengths and weaknesses of each simulator. This article proposes a BMM model based on Bayesian Additive Regression Trees (BART). The proposed methodology is applied to combine predictions from Effective Field Theories (EFTs) associated with a motivating nuclear physics application. for this article are available online. Source code is available at . [ABSTRACT FROM AUTHOR]

The selection of electron donors and nonfullerene acceptors (NFAs) in organic solar cells (OSCs) is crucial for improving photovoltaic performance. Machine learning (ML) has brought a breakthrough solution. Herein, 292 donor‐NFA pairs with experimental OSC parameters from the reported articles are collected. The ML descriptors include device processing parameters, molecular properties, and molecular structure. The five ML regression models, random forest (RF), extra tree regression, gradient boosting regression tree, adaptive boosting, and artificial neural network (ANN) are trained. GridSearchCV is used for hyperparameter optimization of ML regression models. The SHapley Additive exPlanation approach is employed to analyze descriptor importance. Among the trained five ML models, the RF model shows superior performance, achieving Pearson's correlation coefficient (r) of 0.81 on the test set. Based on the donors and NFAs in constructed dataset, the 9779 donor–NFA pairs for OSCs are generated by randomly combining donor and acceptor molecules. The trained RF model is utilized to predict the power conversion efficiency (PCE) of new donor–acceptor pairs for OSCs. The results indicate that the OSC composed of PBDB‐TF as donor and L8‐BO as acceptor can achieve the remarkable PCE of 17.9%. [ABSTRACT FROM AUTHOR]

Purpose: The insidious proliferation of online misinformation represents a significant societal problem. With a wealth of research dedicated to the topic, it is still unclear what determines fake news sharing. This paper comparatively examines fake and accurate news sharing in a novel experimental setting that manipulates news about terrorism. Design/methodology/approach: The authors follow an extended version of the uses-and-gratification framework for news sharing, complemented by variables commonly employed in fake news rebuttal studies. Findings: Logistic regression and classification trees revealed worry about the topic, media literacy, information-seeking and conservatism as significant predictors of willingness to share news online. No significant association was found for general analytical thinking, journalism skepticism, conspiracy ideation, uses-and-gratification motives or pass-time coping strategies. Practical implications: The current results broaden and expand the literature examining beliefs in and sharing of misinformation, highlighting the role of media literacy in protecting the public against the spread of fake news. Originality/value: This is, to the authors' knowledge, the first study to integrate a breadth of theoretically and empirically driven predictors of fake news sharing within a single experimental framework. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2022-0693 [ABSTRACT FROM AUTHOR]

Regional competitiveness is considered a key factor of development. In this work, with the aim of analysing the main drivers of the competitiveness, a Regression Tree analysis has been performed for the Eurostat Regional Competitiveness Index (RCI) as response variable by taking the 74 basic indicators used in the 2019 RCI edition as explanatory variables. Being a non-parametric method, suitable for the analysis of large data sets via a recursive partitioning procedure, the Regression Tree allowed to identify (a) the 12 most influential indicators, out of the initial 74, for the overall 2019 RCI, and (b) a classification of the 268 European regions into 15 homogeneous groups. Interestingly, the groups are ranked by their predicted RCI values which correspond to the mean observed RCI values within the groups themselves. The almost perfect correlation between the Eurostat RCI and the predicted RCI within groups confirms the key role of the 12 selected indicators as determinants of the 2019 RCI. These evidences could help policy makers to address their strategies towards focused objectives in line with the specific needs of the territories, characterized by an intrinsic heterogeneity and complexity. [ABSTRACT FROM AUTHOR]

Identification of the baseline chracteristics for children undergoing orthokeratology with relatively fast myopia progression can allow a more accurate determination of the risk/benefit ratio. This study aimed to investigate if baseline corneal biomechanics can classify relatively slow and fast myopia progression in children. Children aged six to 12 years with low myopia (0.50 to 4.00 D) and astigmatism (less than or equal to 1.25 D), were recruited. Participants were randomised to be fitted with orthokeratology contact lenses with a conventional compression factor (0.75 D, n = 29) or an increased compression factor (1.75 D, n = 33). Relatively fast progressors were defined as participants who had axial elongation of 0.34 mm or above per 2 years. A binomial logistic regression analysis and a classification and regression tree model were used in the data analysis. The corneal biomechanics were measured with a bidirectional applanation device. The axial length was measured by a masked examiner. As there were no significant between-group differences in the baseline data (all p > 0.05), data were combined for analysis. The mean ± SD axial elongation for relatively slow (n = 27) and fast (n = 35) progressors were 0.18 ± 0.14 mm and 0.64 ± 0.23 mm per 2 years, respectively. The area under the curve (p2area1) was significantly higher in relatively fast progressors (p = 0.018). The binomial logistic regression and classification and regression tree model analysis showed that baseline age and p2area1 could differentiate between slow and fast progressors over 2 years. Corneal biomechanics could be a potential predictor of axial elongation in orthokeratology contact lens-wearing children. [ABSTRACT FROM AUTHOR]

This study estimates both hourly and daily Downward Surface Solar Radiation (SSR) in Istanbul while determining the importance of variables on SSR using tree-based machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT). The hourly and daily data of climatic factors for the period between January 2016 and December 2020 are gathered from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. In addition to the meteorology data, hourly data of selected aerosols are obtained from the Ministry of Environment, Urbanization and Climate Change. Temperature, cloud coverage, ozone level, precipitation, pressure, and two components of wind speeds, PM10, PM2.5, and SO2 are utilized to train and test the established models. The model performances are determined with the out-of-bag errors by calculating R-squared, MSE, RMSE, and MBE. The GBRT model is found to be the most accurate model with the lowest error rates. Furthermore, this study provides the variable importance in determining the SSR. Although all models provide different values for the variable importance; temperature, ozone level, cloud coverage, and precipitation are found to be the most important variables in estimating daily SSR. For the hourly estimation, the time of day (hour) becomes the most important factor in addition to temperature, ozone level, and cloud coverage. Finally, this study shows that the tree-based machine learning methods used with these variables to estimate hourly and daily SSR results are very accurate when it is not possible to measure the SSR values directly. [ABSTRACT FROM AUTHOR]

Background and Aims Mediterranean ecosystems have a high vascular plant species richness (SR) relative to their surface area. This SR, representing the balance between speciation and extinction, has been attributed to multiple mechanisms that result in both high rates of speciation and/or low rates of extinction. An abiding question is, however, what is special about Mediterranean ecosystems that enables this high SR? Apart from the long-term climatic stability of the region, SR has also been related to resource availability, the many individuals hypothesis, resource spatial heterogeneity, temporal heterogeneity and biotic feedbacks. Methods Spatial patterns of species richness were related to climatic, edaphic and biotic variables and to spatial variability within the Greater Cape Floristic Region (GCFR) of South Africa. Boosted regression tree models were used to explore the strength of relationships between SR and environmental predictors related to each hypothesized mechanism. Key Results Water availability (i.e. precipitation) was a stronger predictor of SR than potential evapotranspiration or temperature. Scarcity of nutrients was also related to SR. There was no indication that SR was related to the density of individuals and only temporal heterogeneity induced by fire was related to SR. Spatial heterogeneities of climatic, edaphic and biotic variables were strongly associated with SR. Biotic interactions remain difficult to assess, although we have some evidence for a putative role in regulating SR. Conclusions While the lack of ecosystem-resetting disturbances (e.g. glaciation) is undoubtedly a key requirement for high species accumulation, predictably, no one explanation holds the key to understanding SR. In the GCFR high SR is the product of a combination of adequate water, nutrient scarcity, spatial and temporal heterogeneity, and possibly biotic feedbacks. [ABSTRACT FROM AUTHOR]

Effective winter road maintenance relies on precise road friction estimation. Machine learning (ML) models have shown significant promise in this; however, their inherent complexity makes understanding their inner workings challenging. This paper addresses this issue by conducting a comparative analysis of road friction estimation models using four ML methods, including regression tree, random forest, eXtreme Gradient Boosting (XGBoost), and support vector regression (SVR). We then employ the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (AI) to enhance model interpretability. Our analysis on an Alberta dataset reveals that the XGBoost model performs best with an accuracy of 91.39%. The SHAP analysis illustrates the logical relationships between predictor features and friction within all three tree-based models, but it also uncovers inconsistencies within the SVR model, potentially attributed to insufficient feature interactions. Thus, this paper not only showcase the role of explainable AI in improving the ML interpretability of models for road friction estimation, but also provides practical insights that could improve winter road maintenance decisions. [ABSTRACT FROM AUTHOR]

• This study aims to develop a rational model of the intention to risky behaviour based on behavioural characteristics, demographic information, and the amount of exposure and comparing aggregate and disaggregate models of input variables. • In this study, the self-reported data of Tehran taxi drivers was collected in the form of a questionnaire based on the theory of planned behaviour, and then the amount of intention to commit violations was predicted and modeled using stepwise regression and decision tree regression. • The variables were compared and refined in two stages of exploratory factor analysis and stepwise regression before being entered into the tree regression model. • The results obtained from different modelling cases showed that the presence or absence of the exposure variable did not have much effect on the models. The inclusion of attitude, subjective norm, perceived behavioural control and descriptive norm increased the explained variance of intention to violate. The need to predict and understand drivers' intention to violate emphasizes the importance of developing a model based on the motives behind the action. This study aims to develop a model to predict a taxi driveŕs intention to speed in an urban area using variables including psychological factors, demographic information, and exposure. Aggregate (factor-based) and disaggregate (item-based) models of input variables will also be compared. In this study, the self-reported data on Tehran taxi drivers was collected in the form of a scenario-based questionnaire which is inspired by the Theory of Planned Behaviour (TPB). The intention to commit violations was predicted and modelled using stepwise regression and decision tree regression models. The results, based on the stepwise regression model showed a higher value of fitting score for an item-based linear model (R2 = 0.695), but the final model using the tree-based regression presented a better fit for the factored model (R2 = 0.746) considering the cross-validated model with the lowest deviation criteria (RMSD = 0.26). Furthermore, in this model attitude made the greatest contribution to the prediction of intention. The results obtained from the two different modelling approaches showed that exposure and age did not have much effect on the models. A main advantage of the current study is that the variables were compared and refined in two stages of exploratory factor analysis and stepwise regression before being entered into the tree regression model. In addition, the results of the factor-based model were compared with the item-based model. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) random mesostructure models have been broadly utilized to study mechanical behavior and damage mechanisms of asphalt concrete due to their high availability and efficiency. The 2D aggregate gradation that characterizes the area fractions of the simulated aggregates of different sizes significantly affects the simulated mesomechanical properties of asphalt concrete. However, there are very few methods for determining the 2D aggregate gradation from the actual three-dimensional (3D) one. This study proposed a 2D aggregate gradation conversion framework integrating a 3D random aggregate method and machine learning models. First, 3D asphalt concrete mesostructures were generated using polyhedral coarse aggregates with random gradations and shapes. Virtual cutting and sieving procedures were then developed to obtain the 2D aggregates at sections with 1 mm intervals in each mesostructure and acquire the area fractions of aggregates of different sizes, respectively. By averaging the results for the sections in each cutting direction, so-called 2D representative area fractions of aggregates within different grading segments in different directions were obtained. The gradation conversion data sets were established by collecting the volume fractions of coarse aggregates in the 3D mesostructures as input variables and the corresponding 2D representative aggregate area fractions as output variables. Based on the data sets, machine learning models, including linear regression, support vector regression, bagging of regression tree, and neural network (NN), were trained using a 5-fold cross-validation approach and then tested. The results showed that the NN model generally provided the most accurate predictions of the 2D representative aggregate area fractions. The effectiveness of the proposed framework was validated by using both the test sets regarding different sizes of random mesostructures and a mesostructure rebuilt from computed tomography (CT) images of actual asphalt concrete. [ABSTRACT FROM AUTHOR]

• Detailed energy and exergy analyses are conducted on the proposed ARC. • Explore the effects of varying refrigerant mass flow rate and ammonia concentration in strong and weak solutions on key performance parameters. • Employing the GBRT machine learning method and FHO approach in the ARC is highly recommended. • The GBRT model proves to be a reliable predictive tool with strong accuracy, offering valuable insights into system performance. • The generator is the main source of exergy destruction rate (50 %). A detailed analysis of energy and exergy is conducted on a single-effect solar ammonia–water (NH 3 –H 2 O) absorption refrigeration cycle (ARC) using TRNSYS and EES software. Considering the physical and chemical exergies, the exergy destruction rate (Ė D) in each component of the system is calculated, highlighting its contribution to the overall Ė D. The study explores the effects of varying refrigerant mass flow rate (ṁ ᵣ) and ammonia concentration in strong and weak solutions (X s and X w) on key performance parameters, including coefficient of performance (COP), exergy efficiency (Ė D), and overall Ė D across a range of generator temperatures (T g). In this study, a gradient boosting regression tree (GBRT) is employed as a supervised machine-learning technique for classification and regression problems, utilizing boosting to enhance conventional decision tree predictions. The Fire Hawk Optimizer (FHO) approach is also utilized to optimize performance parameters, maximizing COP and η η E while minimizing T g and Ė D. The GBRT models are developed using available experimental and simulation data, revealing relationships between variables (ṁ ᵣ, X s , X w , and T g) and outcomes (COP, Ė D , and overall Ė D). The results revealed that the generator exhibits considerable Ė D regardless of operating conditions, underscoring its pivotal role in the ARC. It emerges as the primary Ė D contributor (50 %), followed by the evaporator (17 %) and the absorber (15 %). However, Ė D associated with the recooler, pump, and expansion valves is negligible in comparison. Optimization results reveal that, when minimizing T g and Ė D , the highest COP and Ė D at T g of 373.15 K reach 0.8081 and 0.46, respectively. [ABSTRACT FROM AUTHOR]

Objectives: Determining which sites were important to differentiate polymyalgia rheumatica (PMR) from rheumatoid arthritis (RA) using 18F-fluorodeoxyglucose (FDG) positron emission tomography and computed tomography (PET-CT) is challenging. Methods: Patients with PMR or RA who were undergoing PET-CT were recruited at two mutual-aid hospitals in Japan between 2009 and 2018. Classification and regression tree (CART) analyses were performed to identify FDG uptake patterns that differentiated PMR from RA. Results: We enrolled 35 patients with PMR and 46 patients with RA. Univariate CART analysis showed that FDG uptake in the shoulder joints, spinous processes of the lumbar vertebrae, pubic symphysis, sternoclavicular joints, ischial tuberosities, greater trochanters, and hip joints differentiated PMR from RA. Multivariate CART analysis revealed that FDG uptake by at least one of the ischial tuberosities had the highest diagnostic value for distinguishing PMR from RA (sensitivity, 77.1%; specificity, 82.6%). We performed the same CART analysis to patients who had not undergone treatment (PMR, n = 28; RA, n = 9). Similar results were obtained, and sensitivity and specificity were increased (sensitivity, 89.3%; specificity, 88.8%). Conclusions: In PET-CT, FDG uptake by at least one of the ischial tuberosities best discriminates between PMR and RA. [ABSTRACT FROM AUTHOR]

Aims: To investigate high‐risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine‐learning techniques. Materials and Methods: We performed a cross‐sectional analysis of county‐level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county‐level SEDH factors that were used in a classification and regression trees (CART) model to identify county‐level clusters. The CART model was validated using a 'hold‐out' set of counties and variable importance was evaluated using Random Forest. Results: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non‐Hispanic Black (%). Conclusion: There is significant county‐level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine‐learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo‐specific strategic interventions and optimize resource allocation to help battle the obesity pandemic. [ABSTRACT FROM AUTHOR]

The agricultural extension system of India has various kinds of service providers like state agriculture departments, universities, research institutes, Farm Science Centers and private players. This study was conducted in 2020 and attempted to explore the association between average annual net income earned from citrus cultivation and the source of availing citrus extension services through Correspondence Analysis (CA) method of 300 citrus farmers selected from three districts in Maharashtra, India. The farmers with high income (1808.31 to 2,411.09 USD ha-1) received advisory services of the public research institute ICAR-Central Citrus Research Institute (CCRI) through either personal contact or electronic platforms. Awareness about CCRI services, source of seeking citrus cultivation related information, source of purchasing citrus planting material, using CCRI mobile app and website for citrus advisories, and contacting CCRI scientists for solving citrus farming related issues was found to have significant (P< 0.05) positive relationship with net income from citrus farming through Pearsons' correlation coefficient, while cost of cultivation and orchard age had negative significant relationship. The multi-linear regression analysis, depicted cost of cultivation, awareness about CCRI services, source of seeking citrus advisories, and source of purchasing planting material had significant association with net income. The findings of correlation and regression thus emphasized the positive significant association of CCRI's extension services to income from citrus farming. Identifying homogenous target groups of citrus farmers through Classification and Regression Tree (CART) method can serve as policy implication for extension service providers to deliver customized need-based advisories to target clientele. [ABSTRACT FROM AUTHOR]

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]

Engineering applications including pervious concrete require effective packing of aggregates to optimize strength. Size and shape distribution of aggregates significantly affect the performance. Computational methods numerically represent shape of aggregates, from image analysis, the effectiveness of has not been compared and verified. This study aims to analyse the representability of shape aspects of aggregates by different computational methods. Crushed aggregates were grouped into 5 clusters, and each group was milled in a Los Angeles machine for different degrees (0–2000) to induce morphological changes on the aggregates. Aggregates ranging from 5 to 30mm in diameter were obtained (7191 in total). imageJTM, was used to compute dimensions and shape factors of aggregates from 14 computational methods. Statistical tests, Pearson's Correlations and Principal Components Analysis and machine learning classification tools, Decision-tree, Random-Forest, Naïve-Bayes, Support-Vector-Machines, K-Nearest-Neighbours and Perceptron were employed to assess. In conclusion, no shape factor could be singularly used to numerically represent the morphological changes on aggregate particles but a combination of shape factors is required. Data matrix had three primary dimensions. Combination of Circularity, Kumbrein-Solidity and Barksdale-Shape-Factor yield best representation of aggregate shape. Regression Tree classification method had the highest accuracy (0.9) in classifying milled and unmilled aggregates. [ABSTRACT FROM AUTHOR]

To accurately and rapidly predict seismic responses, including the maximum displacement (MaxD) and maximum acceleration (MaxA), of the isolated structure considering the soil–structure interaction (SSI), five ensemble learning models, i.e. random forest (RF), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and stacking model, are constructed. Firstly, a total of 96 000 nonlinear time history analyses of the isolated structure considering the SSI are conducted with the aid of OpenSees. The generated database is used for training and testing ensemble learning models. The ensemble learning models have 12 input variables in four categories, i.e. ground motion parameters, structural parameter, isolation parameters and soil parameter, and two output variables, i.e. MaxD and MaxA. The study shows that all ensemble learning models have excellent prediction performance for both training and testing datasets. The determination coefficients are larger than 0.96 and root-mean-square errors (RMSEs) are relatively small. Among the five ensemble learning models, the stacking model exhibits the best performance. In addition, the calculation method of feature importance score for the stacking model is provided. According to the feature importance analysis, the ground motion parameters have greater impact on seismic responses than other three categories of inputs. Finally, six ground motions are randomly selected to verify the generalization ability of the proposed ensemble learning models. The results show that the stacking model has a favorable generalization ability with relatively small prediction errors. [ABSTRACT FROM AUTHOR]

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article. [ABSTRACT FROM AUTHOR]

Background: Hand hygiene is a crucial measure for the prevention of healthcare-associated infections (HAIs). The Hand Hygiene Excellence Award (HHEA) is an international programme acknowledging healthcare facilities for their leadership in implementing hand hygiene improvement programmes, including the World Health Organisation's Multimodal Improvement Strategy. This study aimed at summarising the results of the HHEA campaign between 2010 and 2021 and investigating the relationship between different hand hygiene parameters based on data from participating healthcare facilities. Methods: A retrospective analysis was performed on datasets from HHEA forms, including data on hand hygiene compliance, alcohol-based handrub (ABHR) consumption, and Hand Hygiene Self-Assessment Framework (HHSAF) scores. Descriptive statistics were reported for each variable. The correlation between variables was inspected through Kendall's test, while possible non-linear relationships between hand hygiene compliance, ABHR consumption and HHSAF scores were sought through the Locally Estimated Scatterplot Smoothing or logistic regression models. A tree-structured partitioning model was developed to further confirm the obtained findings. Results: Ninety-seven healthcare facilities from 28 countries in three world regions (Asia-Pacific, Europe, Latin America) were awarded the HHEA and thus included in the analysis. HHSAF scores indicated an advanced hand hygiene promotion level (median 445 points, IQR 395–480). System change (100 [95–100] points) and institutional safety climate (85 [70–95] points) showed the highest and lowest score, respectively. In most cases, hand hygiene compliance was above 70%, with heterogeneity between countries. ABHR consumption above 20 millilitres per patient-day (ml/PD) was widely reported, with overall increasing trends. HHSAF scores were positively correlated with hand hygiene compliance (τ = 0.211, p = 0.007). We observed a positive correlation between compliance rates and ABHR consumption (τ = 0.193, p < 0.001), although the average predicted consumption was stable around 55–60 ml/PD for compliance rates above 80–85%. Logistic regression and partitioning tree analyses revealed that higher HHSAF scores were more likely in the high-ABHR consumption group at cut-offs around 57–59 ml/PD. Conclusion: Ten years after its inception, the HHEA proves to be a valuable hand hygiene improvement programme in healthcare facilities worldwide. Consistent results were provided by the different hand hygiene indicators and the HHSAF score represents a valuable proxy measure of hand hygiene compliance. [ABSTRACT FROM AUTHOR]

Animal psychophysics can generate rich behavioral datasets, often comprised of many 1000s of trials for an individual subject. Gradient-boosted models are a promising machine learning approach for analyzing such data, partly due to the tools that allow users to gain insight into how the model makes predictions. We trained ferrets to report a target word's presence, timing, and lateralization within a stream of consecutively presented non-target words. To assess the animals' ability to generalize across pitch, we manipulated the fundamental frequency (F0) of the speech stimuli across trials, and to assess the contribution of pitch to streaming, we roved the F0 from word token-to-token. We then implemented gradient-boosted regression and decision trees on the trial outcome and reaction time data to understand the behavioral factors behind the ferrets' decision-making. We visualized model contributions by implementing SHAPs feature importance and partial dependency plots. While ferrets could accurately perform the task across all pitch-shifted conditions, our models reveal subtle effects of shifting F0 on performance, with within-trial pitch shifting elevating false alarms and extending reaction times. Our models identified a subset of non-target words that animals commonly false alarmed to. Follow-up analysis demonstrated that the spectrotemporal similarity of target and non-target words rather than similarity in duration or amplitude waveform was the strongest predictor of the likelihood of false alarming. Finally, we compared the results with those obtained with traditional mixed effects models, revealing equivalent or better performance for the gradient-boosted models over these approaches. Author summary: The sorts of listening challenges faced by real-world listeners are rarely captured by most laboratory-based auditory paradigms, particularly those testing animal models. However, many labs are attempting to utilize more realistic experiments, and more complicated behavioral paradigms require more sophisticated approaches to analyzing the resulting data. Here, we used a new behavioral paradigm to test the ability of ferret listeners to identify target speech sounds and assess their ability to generalize across changes in pitch. To make sense of the resulting dataset, we used machine learning to understand how trained ferrets perform this task. Gradient-boosted regression and decision trees are well-established machine learning methods that do not require users to predetermine interaction effects and are accompanied by visualization tools that allow insights into how multiple factors ultimately shape behavior. We compare the use of gradient-boosted models to more standard regression approaches and that this machine learning approach is ideal for analyzing behavioral data in animal models. [ABSTRACT FROM AUTHOR]

This ecological study aimed to identify the factors with the greatest power to discriminate the proportion of oral and oropharyngeal cancer (OOC) records with time to treatment initiation (TTI) within 30 days of diagnosis in Brazilian municipalities. A descriptive analysis was performed on the variables grouped into five dimensions related to patient characteristics, access to health services, support for cancer diagnosis, human resources, and socioeconomic characteristics of 3,218 Brazilian municipalities that registered at least one case of OOC in 2019. The Classification and Regression Trees (CART) technique was adopted to identify the explanatory variables with greater discriminatory power for the TTI response variable. There was a higher median percentage of records in the age group of 60 years or older. The median percentage of records with stage III and IV of the disease was 46.97%, and of records with chemotherapy, radiation, or both as the first treatment was 50%. The median percentage of people with private dental and health insurance was low. Up to 75% had no cancer diagnostic support services, and up to 50% of the municipalities had no specialist dentists. Most municipalities (49.4%) started treatment after more than 30 days. In the CART analysis, treatment with chemotherapy, radiotherapy, or both explained the highest TTI in all municipalities, and it was the most relevant for predicting TTI. The final model also included anatomical sites in the oral cavity and oropharynx and the number of computed tomography services per 100,000. There is a need to expand the availability of oncology services and human resources specialized in diagnosing and treating OOC in Brazilian municipalities for a timely TTI of OOC. [ABSTRACT FROM AUTHOR]

Summary: More than 70% of all vascular plants lack conservation status assessments. We aimed to address this shortfall in knowledge of species extinction risk by using the World Checklist of Vascular Plants to generate the first comprehensive set of predictions for a large clade: angiosperms (flowering plants, c. 330 000 species).We used Bayesian Additive Regression Trees (BART) to predict the extinction risk of all angiosperms using predictors relating to range size, human footprint, climate, and evolutionary history and applied a novel approach to estimate uncertainty of individual species‐level predictions.From our model predictions, we estimate 45.1% of angiosperm species are potentially threatened with a lower bound of 44.5% and upper bound of 45.7%.Our species‐level predictions, with associated uncertainty estimates, do not replace full global, or regional Red List assessments, but can be used to prioritise predicted threatened species for full Red List assessment and fast‐track predicted non‐threatened species for Least Concern assessments. Our predictions and uncertainty estimates can also guide fieldwork, inform systematic conservation planning and support global plant conservation efforts and targets. [ABSTRACT FROM AUTHOR]

Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation. [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]

Background: Instrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone. Design: We carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models' performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split. Results: The IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA =.45, R2CLI =.28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed. Interpretation: The results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators—including estimates of uncertainty—become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on. [ABSTRACT FROM AUTHOR]

Background: Apparent diffusion coefficient (ADC) in MRI has been shown to correlate with postoperative House-Brackmann (HB) scores in patients with vestibular schwannoma despite limited methodology. To rectify limitations of single region of interest (ROI) sampling, we hypothesize that whole-tumor ADC histogram analysis will refine the predictive value of this preoperative biomarker related to postoperative facial nerve function. Methods: Of 155 patients who underwent resection of vestibular schwannoma (2014–2020), 125 patients were included with requisite clinical and radiographic data. After volumetric analysis and whole-tumor ADC histogram, regression tree analysis identified ADC cutoff for significant differences in HB grade. Outcomes were extent of resection, facial nerve function, hospital length of stay (LOS), and complications. Results: Regression tree analysis defined three quantitative ADC groups (× 10−6 mm2/s) as high (> 2248.77; HB 1.7), mid (1468.44–2248.77; HB 3.1), and low (< 1468.44; HB 2.3) range (p 0.04). The mid-range ADC group had significantly worse postoperative HB scores and longer hospital LOS. Large tumor volume was independently predictive of lower rates of gross total resection (p <0.0001), higher postoperative HB score (p 0.002), higher rate of complications (p 0.04), and longer LOS (p 0.003). Conclusions: Whole-tumor histogram yielded a robust regression tree analysis that defined three ADC groups with significantly different facial nerve outcomes. This likely reflects tumor heterogeneity better than solid-tumor ROI sampling. Whole-tumor ADC warrants further study as a useful radiographic biomarker in patients with vestibular schwannoma who are considering surgical resection. [ABSTRACT FROM AUTHOR]

The pollution of the topsoil in the city of Baikal'sk (Irkutsk oblast) under the influence of industrial emissions and wastes of the Baikal Pulp and Paper Mill (BPPM) was studied. The contents of 16 individual polycyclic aromatic hydrocarbons (PAHs) in samples of urban and background soils taken during the soil geochemical survey in the summer of 2019 were analyzed. Relatively low contents of PAHs were found in lignin sludge from the BPPM and ash from the combined heat and power station (CHPS). The concentration of total PAHs in CHPS ash reaches 46 mg/kg with a predominance of low molecular weight compounds (the proportion of naphthalene and its homologues is 24% and 34% of the total PAHs, respectively). Among high molecular weight PAHs, 5-nuclear benzo(b)fluoranthene dominates (16%). In lignin sludge, the amount of PAHs is 7.16 mg/kg with a predominance of benzo(b)fluoranthene (83%). In soils of Baikal'sk, the mean total content of PAHs (38.4 mg/kg) is five times higher than the background content. In urban soils, 4–5-nuclear fluoranthene (61.1%) and benzo(b)fluoranthene (29.4%) prevail. This enables us to assign soil pollution to the fluoranthene type. Soils of the motor traffic (total content of PAHs is 105 mg/kg) and industrial (59.5 mg/kg) zones are most strongly polluted and display contrasting geochemical anomalies of PAHs. Land use zones of the city may be arranged in the following sequence with respect to the amount of PAHs: motor traffic > industrial > residential single-story > railway transport > residential multi-story > recreational zone. Several local anomalies with increased contents of PAHs form two large pollution halos in the west and east of the city. The leading factors in the accumulation of high molecular weight PAHs in soils include acid–alkaline conditions and soil organic matter, while the accumulation of low molecular weight polyarenes is mainly controlled by pH. The environmental hazard of pollution of Baikal'sk soils with polyarenes is by 83.5% related to benzo(b)fluoranthene. [ABSTRACT FROM AUTHOR]