Microbial ecological functions are an emergent property of community composition. For some ecological functions this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP). Using data from two independent long-term ecological research sites - Palmer LTER in Antarctica and Station SPOT in California - we found that community composition was a strong predictor of BP. The top performing model achieved an R2 of 0.84 and RMSE of 20.2 pmol L-1 hr-1 on independent validation data, outperforming a model based solely on environmental data (R2 = 0.32, RMSE = 51.4 pmol L-1 hr-1). We then operationalized our top performing model, estimating BP for 346 Antarctic samples from 2015-2020 for which only community composition data were available. Our predictions resolved spatial trends in BP with significance in the Antarctic (P value = 1 x 10-4) and highlighted important taxa for BP across ocean basins. Our results demonstrate a strong link between microbial community composition and microbial ecosystem function and begin to leverage long-term datasets to construct models of BP based on microbial community composition.

In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants (kobs). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy (Egap) and kobs. MLR analysis further underscored Egap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees, feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed Egap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors, establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model\'s stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry.

Renewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV charging effects, two scenarios are explored: coordinated and intelligent charging. A novel optimization method inspired by the Krill Herd Algorithm (KHA) is introduced for the complex problem, along with a self-adaptive modification to tailor solutions to specific situations. Simulation on an IEEE microgrid demonstrates efficiency in both scenarios. The predictive model yields a remarkably low Mean Absolute Percentage Error (MAPE) of 1.02381 for total HEV charging demand. Results also reveal a reduction in microgrid operation cost in the intelligent charging scenario compared to coordinated charging.

In this work, we present a method for optical frequency multiplication utilizing a hybrid deep learning approach that integrates the Residual Network (ResNet) with the Random Forest Regression (RFR) algorithm. Three different frequency multiplication modulation schemes are adopted to illustrate the method, which can obtain suitable parameters for these schemes. Based on the parameters predicted by the algorithm, the 8-tupling, 12-tupling, and 16-tupling mm-wave signals are generated by numerical simulation. The simulation results show that for 8-tupling frequency multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB and an RFSSR (radio frequency spurious suppression ratio) of 80 GHz is 42.29 dB. For 12-tupling frequency multiplication, the OSSR is 30.09 dB, and the RFSSR of the 120 GHz mm wave is 36.21 dB. For generating 16-tupling frequency mm-wave, an OSSR of 29.86 dB and an RFSSR of 34.52 dB are obtained. In addition, the impact of amplitude fluctuation and bias voltage drift on the quality of mm-wave signals is also studied.

Urban parks play a key role in UHI mitigation. However, the role of other prominent types of urban green infrastructure has not been comprehensively studied. Thus, the main objective of this study was to evaluate the role of cemeteries and allotments as cooling islands compared to the well-studied park areas. We assessed the LST of cemeteries, allotments and parks based on Landsat 8 TM images across the five largest German cities during summertime. Random forest regressions explain the LST spatial variability of the different urban green spaces (UGS) with spectral indices (NDVI, NDMI, NDBaI) as well as with tree characteristics (tree type, tree age, trunk circumferences, trunk height or canopy density). As a result, allotments were identified as the hottest UGS with the city means varying between 23.1 and 26.9 °C, since they contain a relatively high proportion of sealed surfaces. The LST spatial variability of allotment gardens was best explained by the NDVI indicating that fields with a higher percentage of flowering shrubs and trees reveal lower LST values than those covered by annual crops. Interestingly, cemeteries were characterized as the coolest UGS, with city means between 20.4 and 24.7 °C. Despite their high proportion of sealed surfaces, they are dominated by old trees resulting in intensive transpiration processes. Parks show heterogeneous LST patterns which could not be systematically explained by spectral indices due to the variability of park functionality and shape. Compared to parks, the tree-covered areas of cemeteries have a higher cooling potential since cemeteries as cultural heritage sites are well-protected allowing old tree growth with intensive transpiration. These findings underline the relevance of cemeteries as cooling islands and deepen the understanding of the role of tree characteristics in the cooling process.

Climate change and human activities have significantly influenced soil loss and the soil conservation service, posed threats to regional ecological sustainability. However, the relationships and underlying driving forces between potential soil loss, actual soil loss, and soil conservation service have not been well understood. Utilizing the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, we evaluated the soil conservation service on the Tibetan plateau from 1990 to 2020. We analyzed the spatial and temporal trends and examined the driving factors using linear regression, Pearson correlation, and random forest regression. The soil conservation service exhibited a complex pattern of increase followed by a decrease, with a turning point around 2010. Soil conservation service and soil loss demonstrated non-trade-off changes. The potential soil loss dominated the spatiotemporal patterns of soil conservation service on the Tibetan Plateau. Climatic factors significantly influenced the spatiotemporal patterns of soil conservation service, with annual precipitation emerging as the dominant driving factor, contributing approximately 20%. However, the impacts of human activities became more pronounced since 2010, and the contribution of vegetation to changes in soil conservation service was increased. The impact of the Normalized Difference Vegetation Index (NDVI) on soil conservation service for the grades I, II, and III increased by 13.19%, 3.08%, and 3.41%, respectively. Conversely, in northern Tibet before 2010 and eastern Three-River-Source after 2010, soil conservation service exhibited an increasing trend driven by both climate factors and human activities. Which indicates that the implementation of ecological restoration measures facilitated vegetation improvement and subsequently reduced actual soil loss. This study provides a scientific basis for resource management, land development strategies, and the formulation of ecological restoration measures on the Tibetan Plateau.

UNASSIGNED: In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems.
UNASSIGNED: Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression.
UNASSIGNED: The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R² = 0.81) and stomatal conductance (R² = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters.

In the rapidly evolving domain of vapor intrusion (VI) assessments, traditional methodologies encompass detailed groundwater and soil vapor sampling coupled with comprehensive laboratory measurements. These models, blending empirical data, theoretical equations, and site-specific parameters, evaluate VI risks by considering a spectrum of influential factors, from volatile organic compounds (VOC) concentrations in groundwater to nuanced soil attributes. However, the challenge of variability, influenced by dynamic ambient conditions and intricate soil properties, remains. Our study presents an advanced on-site gas sensing station geared towards real-time VOC flux monitoring, enriched with an array of ambient sensors, and spearheaded by the reliable PID sensor for VOC detection. Integrating this dynamic system with machine learning, we developed predictive models, notably the random forest regression, which boasts an R-squared value exceeding 79 % and mean relative error near 0.25, affirming its capability to predict trichloroethylene (TCE) concentrations in soil vapor accurately. By synergizing real-time monitoring and predictive insights, our methodology refines VI risk assessments, equipping communities with proactive, informed decision-making tools and bolstering environmental safety. Implementing these predictive models can simplify monitoring for residents, reducing dependence on specialized systems. Once proven effective, there\'s potential to repurpose monitoring stations to other VI-prone regions, expanding their reach and benefit. The developed model can leverage weather forecasting data to predict and provide alerts for future VOC events.

Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective-physiological association.

The objective of this study was to analyse the potential impact of future climate change on grassland cover in Burkina Faso. MODIS NDVI 250 m time series were used to monitor changes in grassland over 2000-2022. The random forest regression (RFR) model was fit by regressing reference data of grassland cover against current climatic and other environmental predictors to predict the current grassland cover map (2022). Projected climate model data of CMIP6 used under SSP 126 and SSP 585 scenarios were integrated into the fit RFR model to predict future change. The results revealed that grassland areas were largely dominated by non-significant productivity change (55%) during 2000-2022. In this period, grassland area knew more increased productivity (35%) than decrease (10%). Burkina Faso is predicted to face more decreased areas of grassland cover than increase by 2061-2080 under SSP 126 and SSP 585 scenarios. The findings of this study can help to set up appropriate adaptation measures to combat climate change in Burkina Faso.