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The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH 4 ]:[CO 2 ], in breath from individual animals (the so-called "sniffer technique") and estimated CO 2 production can be used to estimate CH 4 production, provided that CO 2 production can be reliably calculated. This would allow CH 4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH 4 production might become possible and their values could be used for breeding of low CH 4 -emitting animals. Estimates of CO 2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO 2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO 2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO 2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (−0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO 2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO 2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO 2 production from lactating dairy cows. [ABSTRACT FROM AUTHOR]

There has been an explosion of data collected about sports. Because such data is extremely rich and complex, machine learning is increasingly being used to extract actionable insights from it. Typically, machine learning is used to build models and indicators that capture the skills, capabilities, and tendencies of athletes and teams. Such indicators and models are in turn used to inform decision-making at professional clubs. Designing these indicators requires paying careful attention to a number of subtle issues from a methodological and evaluation perspective. In this paper, we highlight these challenges in sports and discuss a variety of approaches for handling them. Methodologically, we highlight that dependencies affect how to perform data partitioning for evaluation as well as the need to consider contextual factors. From an evaluation perspective, we draw a distinction between evaluating the developed indicators themselves versus the underlying models that power them. We argue that both aspects must be considered, but that they require different approaches. We hope that this article helps bridge the gap between traditional sports expertise and modern data analytics by providing a structured framework with practical examples. [ABSTRACT FROM AUTHOR]

Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed. [ABSTRACT FROM AUTHOR]

Geological conditions and rainfall intensity are two primary factors that can induce changes in groundwater level, which are one of the major triggering causes of geological disasters, such as collapse, landslides, and debris flow. In view of this, an improved TANK model is developed based on the influence of rainfall intensity, terrain, and geological conditions on the groundwater level in order to effectively predict the groundwater level evolution of rainfall landslides. A trapezoidal structure is used instead of the traditional rectangular structure to define the nonlinear change in a water level section to accurately estimate the storage of groundwater in rainfall landslides. Furthermore, big data are used to extract effective features from large-scale monitoring data. Here, we build prediction models to accurately predict changes in groundwater levels. Monitoring data of the Taziping landslide are taken as the reference for the study. The simulation results of the traditional TANK model and the improved TANK model are compared with the actual monitoring data, which proves that the improved TANK model can effectively simulate the changing trend in the groundwater level with rainfall. The study can provide a reliable basis for predicting and evaluating the change in the groundwater state in rainfall-type landslides. [ABSTRACT FROM AUTHOR]

Understanding how ecosystem change influences fishery resources through trophic pathways is a key tenet of ecosystem‐based fishery management. System models (SM), which use numerical modeling to describe physical and biological processes, can advance inclusion of ecosystem and prey information in fisheries management; however, incorporating SMs in management requires evaluation against empirical data. The Bering Ecosystem Study Nutrient‐Phytoplankton‐Zooplankton (BESTNPZ) model is an SM (originally created by the Bering Ecosystem Study, which initiated in 2006 and was expanded by Kearney et al.) includes zooplankton biomass hindcasts for the Bering Sea. In the Bering Sea, zooplankton are an important prey item for fishery species, yet the zooplankton component of this SM has not been validated against empirical data. We compared empirical zooplankton data to BESTNPZ hindcast estimates for three zooplankton functional groups and found that the two sources of information are on different absolute scales. We found high correlation between relative seasonal biomass trends estimated by BESTNPZ and empirical data for large off‐shelf copepods (Neocalanus spp.) and low correlations for large on‐shelf copepods and small copepods (Calanus spp. and Pseudocalanus spp., respectively). To address these discrepancies, we constructed hybrid species distribution models (H‐SDM), which predict zooplankton biomass using the BESTNPZ hindcast and environmental covariates. We found that H‐SDMs offered marginal improvements over correlative species distribution models (C‐SDMs) relying solely on empirical data for spatial extrapolation and little improvement for most functional groups when forecasting short‐term temporal zooplankton biomass trends. Overall, we suggest that interpretation of current BESTNPZ hindcasts should be tempered by our understanding of key mismatches in absolute scale, seasonality, and annual indices between BESTNPZ and empirical data. [ABSTRACT FROM AUTHOR]

In fisheries management, accurate stock assessment is pivotal to determine sustainable harvest levels or the scope of conservation measures. When assessment is decentralized and methods differ regionally, adopted approaches must be subjected to rigorous quality-checking, as biased assessments may mislead management decisions. To enable recovery of the critically endangered European eel, EU countries must fulfill a biomass target of potential spawner ("silver eel") escapement, while local eel stock assessment approaches vary widely. We summarize local approaches and results of ground-truthing studies based on direct silver eel monitoring, to evaluate the accuracy of eel stock assessments in retrospect and identify bias sources. A substantial fraction of eel habitat is currently unassessed or assessed by unvalidated approaches. Across assessment models for which validation exists, demographic models frequently overestimated actual escapement, while misestimations of extrapolation ("spatial") models were more balanced, slightly underestimating escapement. Stock size overestimation may lead to overexploitation or insufficient conservation measures, increasing the risk of stock collapse or slow recovery in coordinated frameworks. Underestimations may imply inefficient allocation of conservation efforts or negatively affect socioeconomy. Our work highlights the risks of regionalizing assessment responsibilities along with management decisions, calling for a common assessment toolbox and centralized quality-checking routines for eel. [ABSTRACT FROM AUTHOR]

Background Problem Method Results Conclusion Software defect prediction (SDP) is a topic actively researched in the software engineering community. Within‐project defect prediction (WPDP) involves using labeled modules from previous versions of the same project to train classifiers. Over time, many defect prediction models have been evaluated under the WPDP scenario.Data duplication poses a significant challenge in current WPDP evaluation procedures. Unchanged modules, characterized by identical executable source code, are frequently present in both target and source versions during experimentation. However, it is still unclear how and to what extent the presence of unchanged modules affects the performance assessment of WPDP models and the comparison of multiple WPDP models.In this paper, we provide a method to detect and remove unchanged modules from defect datasets and unveil the impact of data duplication in WPDP on model evaluation.The experiments conducted on 481 target versions from 62 projects provide evidence that data duplication significantly affects the reported performance values of individual learners in WPDP. However, when ranking multiple WPDP models based on prediction performance, the impact of removing unchanged instances is not substantial. Nevertheless, it is important to note that removing unchanged instances does have a slight influence on the selection of models with better generalization.We recommend that future WPDP studies take into consideration the removal of unchanged modules from target versions when evaluating the performance of their models. This practice will enhance the reliability and validity of the results obtained in WPDP research, leading to improved understanding and advancements in defect prediction models. [ABSTRACT FROM AUTHOR]

In the pursuit of ecological validity, current business process simulation methods are calibrated to data from existing processes. This is important for realistic what-if analysis in the context of these processes. However, this is not always the "right tool for the job." To test hypotheses in the area of predictive process monitoring, it can be more helpful to simulate event-log data from a theoretical process, where all aspects can be manipulated. One example is when assessing the influence of process complexity or variability on the performance of a new prediction method. In this case, the ability to include control variables and systematically change process characteristics is a key to fully understanding their influence. Calibrating a simulation model from observed data alone can in these cases be limiting. This paper proposes a simulation framework, Synthetic Business Process Simulation (SynBPS), a Python library for the generation of event-log data from synthetic processes. Aspects such as process complexity, stability, trace distribution, duration distribution, and case arrivals can be fully controlled by the user. The overall architecture is described in detail, and a demonstration of the framework is presented. [ABSTRACT FROM AUTHOR]

Investigating the optimal laser processing parameters for industrial purposes can be time-consuming. Moreover, an exact analytic model for this purpose has not yet been developed due to the complex mechanisms of laser processing. The main goal of this study was the development of a backpropagation neural network (BPNN) with a grey wolf optimization (GWO) algorithm for the quick and accurate prediction of multi-input laser etching parameters (energy, scanning velocity, and number of exposures) and multioutput surface characteristics (depth and width), as well as to assist engineers by reducing the time and energy require for the optimization process. The Keras application programming interface (API) Python library was used to develop a GWO-BPNN model for predictions of laser etching parameters. The experimental data were obtained by adopting a 30 W laser source. The GWO-BPNN model was trained and validated on experimental data including the laser processing parameters and the etching characterization results. The R2 score, mean absolute error (MAE), and mean squared error (MSE) were examined to evaluate the prediction precision of the model. The results showed that the GWO-BPNN model exhibited excellent accuracy in predicting all properties, with an R2 value higher than 0.90. [ABSTRACT FROM AUTHOR]

This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses. Plain Language Summary: This research examines how well a kilometer grid scale global atmospheric model—the Simple Cloud‐Resolving Energy Exascale Earth System Model (SCREAMv0)—performs in simulating clouds and rainfall over the Amazon rainforest region. The model was assessed by comparing to high‐resolution ground‐based observations from the Green Ocean Amazon field campaign supported by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. The model struggles to produce enough middle‐level clouds. When comparing the simulated rainfall to radar observations, SCREAMv0 showed good performance on the diurnal pattern of rain rate, but tends to form too many small rain clusters while failing to create large ones. A possible contributor to these errors could be the inaccurate depiction of how the earth's surface and the atmosphere interact within the model. Overall, this study shows that using detailed DOE ARM data can help improve our understanding of clouds and rainfall in global storm resolving kilometer grid scale models. Key Points: Convective processes in a global storm resolving model (SCREAMv0) are evaluated using ground‐based observations over a tropical rainforestSCREAMv0 captures the morning development of shallow convection and the early afternoon precipitation peak but lacks mid‐level congestusSCREAMv0 struggles to form large precipitation clusters greater than 128 km and produces smaller ones more often than observed [ABSTRACT FROM AUTHOR]

Apparent thermal conductivity of soil (λ) as a function of soil water content (θ), i.e., λ(θ) is needed to determine the heat flow in soil. The function of λ(θ) can be used in heat and water flow models for simplicity. The objective of this study was to develop a sigmoidal model based on logistic equation for entire range of soil water contents and a wide range of soil textures that can be used in simulation of heat and water flow in respected modes. Further, performance of the developed sigmoidal model along with two other models in literature was evaluated. In the proposed sigmoidal model, the constants of this model are estimated based on empirical multivariate equations by using soil sand content and bulk density. The sigmoidal model was validated with good accuracy for a wide range of soil textures, as the relationship between the measured and predicted λ showed slope and intercept values of nearly 1.0 and 0.0, respectively. Comparison of the results obtained by sigmoidal model with those obtained from Johansen and Lu et al. models indicated that, the sigmoidal model was superior to the other two models in prediction of λ for a wide range of soil textures and soil water contents. Furthermore, comparison with a recently proposed model by Xiong et al. indicated that our sigmoidal model is superior. Therefore, our developed sigmoidal model can be used in heat and water flow models to predict the soil temperature and heat flow. [ABSTRACT FROM AUTHOR]

Theories and models are central to Human Factors/Ergonomics (HFE) sciences for producing new knowledge, pushing the boundaries of the field, and providing a basis for designing systems that can improve human performance. Despite the key role, there has been less attention to what constitutes a good theory/model and how to examine the relative worth of different theories/models. This study aims to bridge this gap by (1) proposing a set of criteria for evaluating models in HFE, (2) employing a methodological approach to utilize the proposed criteria, and (3) evaluating the existing models of trust in automation (TiA) according to the proposed criteria. The resulting work provides a reference guide for researchers to examine the existing models' performance and to make meaningful comparisons between TiA models. The results also shed light on the differences among TiA models in satisfying the criteria. While conceptual models offer valuable insights into identifying the causal factors, their limitation in operationalization poses a major challenge in terms of testability and empirical validity. On the other hand, although more readily testable and possessing higher predictive power, computational models are confined to capturing only partial causal factors and have reduced explanatory power capacity. The study concludes with recommendations that in order to advance as a scientific discipline, HFE should adopt modelling approaches that can help us understand the complexities of human performance in dynamic sociotechnical systems. [ABSTRACT FROM AUTHOR]

In this study, a new skill score (SS) is proposed to evaluate the performance of climatological East Asian summer precipitation (EASP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the historical period. By applying the empirical orthogonal function (EOF) to the EASP bias of CMIP6 models, the intermodel spread of EASP bias is revealed to be dominated by the first two modes: the uniform precipitation bias pattern and the north–south dipole precipitation bias pattern. Then the SS is constructed by the weighted‐average model‐observation distances regarding different EOF modes, where the model‐observation distance in a certain EOF mode is defined as the difference between their principal components, and the weight is the corresponding percentage variance. The perfect‐models ensemble based on the SS shows a spatial magnitude close to the observation, indicating that the SS effectively depicts the models' historical performance. However, no robust relationship is found between the model's historical performance and future projection regarding the EASP. This is because they are governed by different physical factors. The historical EASM is determined by the thermal responses to a specific radiative forcing, while the future change in EASP is associated with the warming rate along with the increased radiative forcing. [ABSTRACT FROM AUTHOR]

Modeling of the soil–structure interface has been a critical issue in geotechnical engineering. Numerous studies have simulated complex soil–structure interface behaviors. These models usually are assessed by direct comparisons between the simulations and experiments. However, little work has been done to compare the specific interface behaviors simulated by different interface models. This paper evaluated some frequently recognized interface behaviors for six different interface models. These models either were adopted from the existing literature or modified from the existing soil models, including the exponential model, hyperbolic model, hypoplastic model, MCC model, SANISAND model, and SIMSAND model. Global comparisons and effects of the soil density, normal stiffness, and shearing rate were investigated to evaluate the interface models based on Fontainebleau sand–steel interface experiments and kaolin clay–steel interface experiments. The limitations and advantages of different models under different conditions were discussed. [ABSTRACT FROM AUTHOR]

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Purpose: The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients. Methods: This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. Results: This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855–0.927) and a Brier score of 0.012. Furthermore, the Hosmer–Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856–0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5. Conclusion: A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification. [ABSTRACT FROM AUTHOR]

Using skewed sequential data, the study explores the effectiveness of numerous sequential models designed for binary classification tasks. The dataset under investigation consists of 5,595 testing samples and 13,055 training samples, a structure that presents significant difficulties because of uneven labelling. The researchers carefully go through pretreatment procedures, which include text data encoding and effective methods for handling missing information, in order to address this. The study employs and examines a wide range of algorithms, which reflects the heterogeneous sequential modelling environment. A variety of neural network architectures are included in the arsenal: CNN, CNN-RNN, RCNN. The binary classification job at hand is used to thoroughly assess each architecture, revealing both its advantages and disadvantages. The study's evaluation approach, which presents a wide range of measures indicating consistently excellent performance overall, is its key component. Among these algorithms stand out as the best with an astounding 97% accuracy rate on a variety of evaluation metrics. This strong performance highlights their ability to handle sequential data with unbalanced labels and establishes a standard for further work in related fields. Beyond its empirical results, the study is important because it provides a well-designed assessment approach that may be used as a benchmark by practitioners facing similar problems. Through the clarification of important concepts related to model selection and performance evaluation, the study provides professionals and academics with crucial resources to efficiently traverse the complex terrain of sequential modelling. [ABSTRACT FROM AUTHOR]

ABSTRACT: Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called “sniffer technique”) and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 (“best model”), where all significant traits were included; model 2 (“on-farm model”), where DMI was excluded; and model 3 (“reduced on-farm model”), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (−0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

Arctic warming played a dominant role in recent occurrences of extreme events over the Northern Hemisphere, but climate models cannot accurately simulate the relationship. Here a significant positive correlation (0.33–0.95) between extreme precipitation and Arctic amplification (AA) is found using observations and CMIP5/6 multi-model ensembles. However, CMIP6 models are superior to CMIP5 models in simulating the temporal evolution of extreme precipitation and AA. According to 14 optimal CMIP6 models, the maximum latitude of planetary waves and the strength of Northern Hemisphere annular mode (NAM) will increase with increasing AA, contributing to increased extreme precipitation over the Northern Hemisphere. Under the Shared Socioeconomic Pathway SSP5-8.5, AA is expected to increase by 0.85 °C per decade while the maximum latitude of planetary waves will increase by 2.82° per decade. Additionally, the amplitude of the NAM will increase by 0.21 hPa per decade, contributing to a rise in extreme precipitation of 1.17% per decade for R95pTOT and 0.86% per decade for R99pTOT by 2100.

Abstract Apparent thermal conductivity of soil (λ) as a function of soil water content (θ), i.e., λ(θ) is needed to determine the heat flow in soil. The function of λ(θ) can be used in heat and water flow models for simplicity. The objective of this study was to develop a sigmoidal model based on logistic equation for entire range of soil water contents and a wide range of soil textures that can be used in simulation of heat and water flow in respected modes. Further, performance of the developed sigmoidal model along with two other models in literature was evaluated. In the proposed sigmoidal model, the constants of this model are estimated based on empirical multivariate equations by using soil sand content and bulk density. The sigmoidal model was validated with good accuracy for a wide range of soil textures, as the relationship between the measured and predicted λ showed slope and intercept values of nearly 1.0 and 0.0, respectively. Comparison of the results obtained by sigmoidal model with those obtained from Johansen and Lu et al. models indicated that, the sigmoidal model was superior to the other two models in prediction of λ for a wide range of soil textures and soil water contents. Furthermore, comparison with a recently proposed model by Xiong et al. indicated that our sigmoidal model is superior. Therefore, our developed sigmoidal model can be used in heat and water flow models to predict the soil temperature and heat flow.

Abstract Due to the rapid economic development of globalization and the intensification of economic and trade exchanges, cross-international and regional carbon emissions have become increasingly severe. Governments worldwide establish laws and regulations to protect their countries' environmental impact. Therefore, selecting robustness evaluation models and metrics is an urgent research topic. This article proves the reliability and scientific of the assessment data through literature coupling evaluation, multidisciplinary coupling mathematical model and international engineering case analysis. The innovation of this project's research lies in the comprehensive analysis of the complex coupling effects of various discrete data and uncertainty indicators on the research model across international projects and how to model and evaluate interactive effects accurately. This article provides scientific measurement standards and data support for governments worldwide to formulate carbon tariffs and carbon emission policies. Case analysis data shows that the carbon emission ratio of exporting and importing countries is 0.577:100; the carbon trading quota ratio is 32.50:100.

Background and objectives: Despite being clinically utilized for the treatment of infections, the limited therapeutic range of polymyxin B (PMB), along with considerable interpatient variability in its pharmacokinetics and frequent occurrence of acute kidney injury, has significantly hindered its widespread utilization. Recent research on the population pharmacokinetics of PMB has provided valuable insights. This study aims to review relevant literature to establish a theoretical foundation for individualized clinical management. Methods: Follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, Pop-PK studies of PMB were searched in PubMed and EMBASE database systems from the inception of the database until March 2023. Result: To date, a total of 22 population-based studies have been conducted, encompassing 756 subjects across six different countries. The recruited population in these studies consisted of critically infected individuals with multidrug-resistant bacteria, patients with varying renal functions, those with cystic fibrosis, kidney or lung transplant recipients, patients undergoing extracorporeal membrane oxygenation (ECMO) or continuous renal replacement therapy (CRRT), as well as individuals with obesity or pediatric populations. Among these studies, seven employed a one-compartmental model, with the range of typical clearance (CL) and volume (Vc) being 1.18–2.5L /h and 12.09–47.2 L, respectively. Fifteen studies employed a two-compartmental model, with the ranges of the clearance (CL) and volume of the central compartment (Vc), the volume of the peripheral compartment (Vp), and the intercompartment clearance (Q) were 1.27–8.65 L/h, 5.47–38.6 L, 4.52–174.69 L, and 1.34–24.3 L/h, respectively. Primary covariates identified in these studies included creatinine clearance and body weight, while other covariates considered were CRRT, albumin, age, and SOFA scores. Internal evaluation was conducted in 19 studies, with only one study being externally validated using an independent external dataset. Conclusion: We conclude that small sample sizes, lack of multicentre collaboration, and patient homogeneity are the primary reasons for the discrepancies in the results of the current studies. In addition, most of the studies limited in the internal evaluation, which confined the implementation of model-informed precision dosing strategies. [ABSTRACT FROM AUTHOR]

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds. [ABSTRACT FROM AUTHOR]

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRIESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios. [ABSTRACT FROM AUTHOR]

Evaluation of the performance of flood models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as uncertainty bounds, Nash Sutcliffe efficiency, Kling Gupta efficiency, and the coefficient of determination, which are widely used in the model evaluation, the inherent properties and sampling uncertainty in these metrics are demonstrated. A comprehensive evaluation is conducted using an ensemble of one‐dimensional Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models, which account for the uncertainty associated with the channel roughness and upstream flow input, of six reaches located in Indiana and Texas of the United States. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high‐flow scenarios, and various types of measurement errors in observations on the evaluation metrics are investigated using bootstrapping. Results show that the model performances based on the uniform and normal priors are comparable. The statistical distributions of all the evaluation metrics in this study are significantly different under different high‐flow scenarios, thus suggesting that the metrics should be treated as "random" variables due to both aleatory and epistemic uncertainties and conditioned on the specific flow periods of interest. Additionally, the white‐noise error in observations has the least impact on the metrics. [ABSTRACT FROM AUTHOR]

This study leverages sophisticated machine learning methodologies, particularly XGBoost, to analyze cardiovascular diseases through cardiac datasets. The methodology encompasses meticulous data pre-processing, training of the XGBoost algorithm, and its performance evaluation using metrics such as accuracy, precision, and ROC curves. This technique represents a notable progression in the realm of medical research, potentially leading to enhanced diagnostic precision and a deeper comprehension of cardiovascular ailments, thereby improving patient care and treatment modalities in cardiology. Furthermore, the research delves into the utilization of deep learning methodologies for the automated delineation of cardiac structures in MRI and mammography images, aiming to boost diagnostic precision and patient management. [24][3][5][6] In assessing machine learning algorithms' efficacy in diagnosing cardiovascular diseases, this analysis underscores the pivotal role of such algorithms and their possible data inputs. Additionally, it investigates promising directions for future exploration, such as the application of reinforcement learning. A significant aspect of our investigation is the development and deployment of sophisticated deep learning models for segmenting right ventricular images from cardiac MRI scans, aiming at heightened accuracy and dependability in diagnostics. Through the utilization of advanced techniques like Fourier Convolutional Neural Network (FCNN) and improved versions of Vanilla Convolutional Neural Networks (Vanilla-CNN) and Residual Networks (ResNet), we achieved a substantial improvement in accuracy and reliability. This enhancement allows for more precise and quicker identification and diagnosis of cardiovascular diseases, which is of utmost importance in clinical practice. Evaluation of Machine Learning Algorithms: We conducted a comprehensive evaluation of machine learning algorithms in the context of cardiovascular disease diagnosis. This assessment emphasized the fundamental role of machine learning algorithms and their potential data sources. We also explored promising avenues, such as reinforcement learning, for future research. Factors Affecting Predictive Models: We highlighted the critical factors affecting the effectiveness of machine learning-based predictive models. These factors include data heterogeneity, depth, and breadth, as well as the nature of the modeling task, and the choice of algorithms and feature selection methods. Recognizing and addressing these factors are essential for building reliable models. [ABSTRACT FROM AUTHOR]

Purpose: Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas. Materials and methods: In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data. Results and discussion: Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R2adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R2 = 0.99), and the SVM model was the lowest (R2 = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model. Conclusion: The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy. [ABSTRACT FROM AUTHOR]

We describe and demonstrate a two-step approach for screening global climate models (GCMs) and produce robust annual and seasonal climate projections for Chile. First, we assess climate model simulations through a Past Performance Index (PPI) inspired by the Kling-Gupta Efficiency, which accounts for climatological averages, interannual variability, seasonal cycles, monthly probabilistic distribution, spatial patterns of climatological means, and the capability of the GCMs to reproduce teleconnection responses to El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). The PPI formulation is flexible enough to include additional variables and evaluation metrics and weight them differently. Secondly, we use a recently proposed GCM classification based on model code genealogy to obtain a subset of independent model structures from the top 60% GCMs in terms of PPI values. We use this approach to evaluate 27 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and generate projections in five regions with very different climates across continental Chile. The results show that the GCM evaluation framework is able to identify pools of poor-performing and well-behaved models at each macrozone. Because of its flexibility, the model features that may be improved through bias correction can be excluded from the model evaluation process to avoid culling GCMs that can replicate other climate features and observed teleconnections. More generally, the results presented here can be used as a reference for regional studies and GCM selection for dynamical downscaling, while highlighting the difficulty in constraining precipitation and temperature projections. [ABSTRACT FROM AUTHOR]

Interactions among atmospheric, root‐soil, and vegetation processes drive carbon dioxide fluxes (Fc) from land to atmosphere. Eddy covariance measurements are commonly used to measure Fc at sub‐daily timescales and validate process‐based and data‐driven models. However, these validations do not reveal process interactions, thresholds, and key differences in how models replicate them. We use information theory‐based measures to explore multivariate information flow pathways from forcing data to observed and modeled hourly Fc, using flux tower data sets in the Midwestern U.S. in intensively managed corn‐soybean landscapes. We compare multiple linear regressions, long‐short term memory, and random forests (RF), and evaluate how different model structures use information from combinations of sources to predict Fc. We extend a framework for model predictive and functional performance, which examines a suite of dependencies from all forcing variables to the observed or modeled target. Of the three model types, RF exhibited the highest functional and predictive performance, correctly capturing strong dependencies between radiation and temperature variables with Fc. Regionally trained models demonstrate lower predictive but higher functional performance compared to site‐specific models, suggesting superior reproduction of observed relationships at the expense of predictive accuracy. This study shows that some metrics of predictive performance encapsulate functional behaviors better than others, highlighting the need for multiple metrics of both types. This study improves our understanding of carbon fluxes in an intensively managed landscape, and more generally provides insight into how model structures and forcing variables translate to interactions that are well versus poorly captured in models. Plain Language Summary: In an agricultural landscape, exchanges of carbon dioxide between the land and atmosphere occur due to photosynthesis and respiration and depend on weather, soil, and vegetation conditions. Traditional model performance metrics focus on the relationship between observed and modeled outputs, while functional performance considers the relationships between interacting inputs and outputs. We compare several performance measures for three different machine learning (ML) models that simulate sub‐daily carbon fluxes. We look at how drivers such as solar radiation, soil moisture, temperature, humidity, and rainfall provide information to carbon fluxes, and whether different ML models also capture these interactions. In other words: Air, soil, and plants drive carbon's upward path,Models are detectives, interpreting their math.With information theory, we map data's travel courses,To see how models find or miss carbon's causal sources. Key Points: Information theory measures describe individual and joint causal relationships in observed versus modeled vertical carbon dioxide fluxesThree machine learning models overestimate unique information from sources at the expense of synergistic, or joint informationRegionally trained models have improved functional but not predictive performances, indicating a trade‐off [ABSTRACT FROM AUTHOR]

Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset. [ABSTRACT FROM AUTHOR]

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