Predictive Performance Research Articles

Comparison and validation of several scoring systems for non-variceal upper gastrointestinal bleeding: a retrospective study.

Various scoring systems have been developed to predict outcomes in patients with non-variceal upper gastrointestinal bleeding (NVUGIB). However, their accuracy remains unclear. This study aimed to compare and validate the predictive performance of several established scoring systems in patients with NVUGIB: Glasgow-Blatchford score (GBS) and the age, blood tests, and comorbidities (ABC), mental status-anesthesiologist score-pulse-albumin-systolic blood pressure-hemoglobin (MAP(ASH)), Japanese, and Charlson comorbidity index-in-hospital onset-albumin-mental status-Eastern Cooperative Oncology Group performance status-steroids (CHAMPS) scores. We retrospectively reviewed the records of 1,241 patients who presented to the emergency department with NVUGIB and subsequently required hospitalization. Each scoring system was evaluated for its ability to predict in-hospital mortality, rebleeding, and the need for radiological or surgical intervention. The ABC score showed the highest accuracy in predicting in-hospital mortality. The MAP(ASH) score was the most effective predictor of rebleeding and the need for interventions. Different scoring systems have been optimized for various clinical outcomes. The ABC score was the best for predicting mortality, whereas the MAP(ASH) score excelled in identifying rebleeding risks and intervention needs. The selection of an appropriate scoring tool based on specific clinical scenarios can improve patient management and resource allocation in NVUGIB.

Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.

Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four hospitals in Chongqing. Medical records, imaging reports, and laboratory test results from 21,459 ischemic stroke patients were collected and analyzed. Univariable and multivariable statistical analyses identified key predictive factors. The dataset was split into a 70% training set and a 30% testing set. To address the class imbalance, the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors was employed. Nine widely used machine learning algorithms were evaluated using relevant prediction metrics, with SHAP (SHapley Additive exPlanations) used to interpret the model and assess the contributions of different features. Regression analyses revealed that complications such as hydrocephalus, cerebral hernia, and deep vein thrombosis, as well as specific brain regions (frontal, parietal, and temporal lobes), significantly contributed to PSE. Factors such as age, gender, NIH Stroke Scale (NIHSS) scores, and laboratory results like WBC count and D-dimer levels were associated with increased PSE risk. Tree-based methods like Random Forest, XGBoost, and LightGBM showed strong predictive performance, achieving an AUC of 0.99. The model accurately predicts PSE risk, with tree-based models demonstrating superior performance. NIHSS score, WBC count, and D-dimer were identified as the most crucial predictors. The research is funded by Central University basic research young teachers and students research ability promotion sub-projec t(2023CDJYGRH-ZD06), and by Emergency Medicine Chongqing Key Laboratory Talent Innovation and development joint fund project (2024RCCX10).

BCDPi: An Interpretable Multitask Deep Neural Network Model for Predicting Chemical Bioconcentration in Fish.

Predicting the bioconcentration of chemical compounds plays a crucial role in assessing environmental risks and toxicological impacts. This study presents a robust multitask deep learning model for predicting the bioconcentration potential. The model can predict the bioconcentration of compounds in multiple categories, including non-bioconcentrative (non-BC), weakly bioconcentrative (weak-BC), and strongly bioconcentrative (strong-BC). We also employed the SHapley Additive exPlanations (SHAP) technology for the model interpretation. The binary classification models (non-BC vs BC and weak-BC vs strong-BC) showed good predictive performance, which achieved accuracy values over 90% and area under the curve (AUC) values with 0.95. The final ternary classification model provided an overall accuracy with 91.11%. Comparative analysis of molecular physicochemical properties showed that the importance of molecular weight, polar surface area, solubility, and hydrogen bonding are important for chemical bioconcentration. Besides, we identified eight structural alerts responsible for chemical bioconcentration. We made the model available as an online tool named BCdpi-predictor, which is accessible at http://bcdpi.sapredictor.cn/. Users can predict the bioconcentration potential of chemical compounds freely. The model has significant implications for environmental policy and regulatory frameworks, such as REACH, by providing a more accurate and interpretable method for assessing chemical risks. We hope that the results of this study can provide helpful tools and meaningful information for chemical bioconcentration prediction in environmental risk assessment.

Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions.

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Influence of Compressive Strength and Steel-Tube Thickness on Axial Compression Performance of Ultra-High-Performance Concrete-Filled Stainless-Steel Tube Columns Containing Coarse Aggregates

With the increasing use of concrete-filled steel tubular (CFST) structures, exposed steel tubes are highly susceptible to corrosion, posing potential safety hazards. This study innovatively proposes the use of stainless-steel tubes instead of traditional carbon-steel ones and introduces coarse aggregates into ultra-high-performance concrete (UHPC), forming a coarse aggregate-incorporated ultra-high-performance concrete-filled stainless-steel tube (CA-UFSST). The inclusion of coarse aggregates not only compensates for the shortcomings of UHPC but also enhances the overall mechanical performance of the composite structure. Twenty sets of specimens were designed to analyze the influence of four parameters, including the coarse aggregate content, compressive strength, stainless-steel-tube thickness, and stainless-steel type on the axial compression performance of UHPC. The experimental results indicate that the failure mode of UHPC is related to the confinement ratio. As the confinement ratio increases, the failure mode transitions from shear failure to bulging failure. The addition of coarse aggregates enhances the stiffness of the specimens. Furthermore, this paper discusses the applicability of six current codes in predicting the bearing capacity of CA-UFSST and finds that the European code exhibits the best prediction performance. However, as the confinement ratio increases, the prediction accuracy becomes notably insufficient. Therefore, it is necessary to establish a more accurate calculation model for the axial compression bearing capacity.

Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.

Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning

The chloride ion permeability resistance of recycled aggregate concrete (RAC) is influenced by multiple factors, and the prediction model for this resistance based on machine learning is still limited. In the paper, six impact factors (IFs), including the carbonation of recycled coarse aggregates (YN), the replacement ratio of recycled coarse aggregates (r), the bending load level (L), the carbonation time (t) and temperature (T) of RAC, and the replacement ratio of carbonated recycled fine aggregates (f), were considered to conduct a chloride penetration test on RAC. Based on the experimental data, four algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were adopted to establish the machine learning prediction models and study the relationships between the electric flux of RAC and the IFs. The results showed that the predicted values of all four models were in good agreement with the experimental values, and the XGBoost model had the best prediction performance on the testing set. Based on the XGBoost model, the LIME method was adopted to solve the interpretability problem in the prediction process. The importance ranking of IFs on the electric flux was r > t > f > T > L > YN. A graphical user interface (GUI) was developed based on Python 3.8 software to facilitate the use of machine learning models for the chloride ion permeability resistance of RAC. The research results can provide an accurate prediction of the electric flux of RAC.

From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty

Bearings are pivotal components of rotating machines where any defects could propagate and trigger systematic failures. Once faults are detected, accurately predicting remaining useful life (RUL) is essential for optimizing predictive maintenance. Although data-driven methods demonstrate promising performance in direct RUL prediction, their robustness and practicability need further improvement regarding physical interpretation and uncertainty quantification. This work leverages variational neural networks to model bearing degradation behind envelope spectra. A convolutional variational autoencoder for regression (CVAER) is developed to probabilistically predict RUL distributions with confidence measures. Enhanced average envelope spectra (AES) are used as network input for its physical robustness in bearing condition assessment and fault detection. The use of the envelope spectrum ensures that it contains only bearing-related information by removing other rotor-related frequencies, hence it improves the RUL prediction. Unlike traditional variational autoencoders, the probabilistic regressor and latent generator are formulated to quantify uncertainty in RUL estimates and learn meaningful latent representations conditioned on specific RUL. Experimental validations are conducted on vibration data collected using multiple accelerometers whose natural frequencies cover bearing resonance ranges to ensure fault detection reliability. Beyond conventional bearing diagnosis, envelope spectra are extended for statistical RUL prediction integrating physical knowledge of actual defect conditions. Comparative and ablation studies are conducted against benchmark models to demonstrate their effectiveness.

Construction of 2022 Qatar World Cup match result prediction model and analysis of performance indicators

This research investigates the influence of performance metrics on match outcomes and constructs a predictive model using data from the Qatar World Cup. Employing magnitude-based decision and an array of machine learning algorithms, such as Decision Trees, Logistic Regression, Support Vector Machines, AdaBoost, Random Forests, and Artificial Neural Network, we examined data from 59 matches, excluding extra time. Fourteen performance indicators were integrated into the model, with two types of match outcomes—winning and non-winning—serving as the output variables. The ANN model exhibited the highest predictive performance, achieving an accuracy of 75.42%, an AUC of 76.96%, a precision of 72.73%, a recall of 65.31%, a specificity of 77.03%, and an F1 score of 68.82%. SHAP analysis revealed that “On Target”, “Shooting Opportunity”, and “Ball Progressions” were the most influential features. These findings underscore the critical role of shooting accuracy and the creation of scoring opportunities in determining match outcomes. Consequently, this study developed an accurate model for predicting match outcomes and meticulously analyzed the match performance. Coaches should prioritize the sensitive indicators identified in this study during training and structure training sessions accordingly.

Adaptive Forecasting of Nuclear Power Plant Operational Status Under Sensor Concept Drift Using a Bridging Distribution Adaptive Network

A large number of sensors are required to collect information during the operation of nuclear power plants to ensure their absolutely safe operation. However, because of the unique nature of nuclear reactions, the physical environment of nuclear power production is prone to changes, leading to concept drift in the data collected by the sensors. Concept drift describes the phenomenon of sample distribution changing over time, which typically negatively impacts the model’s training and inference processes. We found that nongradual distribution changes could be guided by generating transitional intermediary distributions within the distribution, thereby achieving a gradual change process. Based on this, we designed a bridging distribution adaptive network (BDAN), which consisted of identical-depth TDoA (time difference of arrival) homomorphic backbone neural networks on both sides with a latent adaptive bridging module in the middle. By calculating the distribution differences over multiple timesteps, a series of bridge distributions were generated to guide the gradients in the latent space, updating the parameters of the latent adaptive guiding module in a directional manner and enabling the model to perceive nongradual distribution changes in the time domain. Experimental results showed that the BDAN outperformed the previous state-of-the-art benchmark methods by 5.6% in terms of mean squared error in the nuclear power data prediction task under concept drift, achieving the best fault prediction performance.

Methods for Identifying Epilepsy Surgery Targets Using Invasive EEG: A Systematic Review

Background: The pre-surgical evaluation for drug-resistant epilepsy achieves seizure freedom in only 50–60% of patients. Efforts to identify quantitative intracranial EEG (qEEG) biomarkers of epileptogenicity are needed. This review summarizes and evaluates the design of qEEG studies, discusses barriers to biomarker adoption, and proposes refinements of qEEG study protocols. Methods: We included exploratory and prediction prognostic studies from MEDLINE and Scopus published between 2017 and 2023 that investigated qEEG markers for identifying the epileptogenic network as the surgical target. Cohort parameters, ground truth references, and analytical approaches were extracted. Results: Out of 1789 search results, 128 studies were included. The study designs were highly heterogeneous. Half of the studies included a non-consecutive cohort, with sample sizes ranging from 2 to 166 patients (median of 16). The most common minimum follow-up was one year, and the seizure onset zone was the most common ground truth. Prediction studies were heterogeneous in their analytical approaches, and only 25 studies validated the marker through post-surgical outcome prediction. Outcome prediction performance decreased in larger cohorts. Conversely, longer follow-up periods correlated with higher prediction accuracy, and connectivity-based approaches yielded better predictions. The data and code were available in only 9% of studies. Conclusions: To enhance the validation qEEG markers, we propose standardizing study designs to resemble clinical trials. This includes using a consecutive cohort with long-term follow-up, validating against surgical resection as ground truth, and evaluating markers through post-surgical outcome prediction. These considerations would improve the reliability and clinical adoption of qEEG markers.

Evaluation of Electrical Properties and Uniformity of Single Wall Carbon Nanotube Dip-Coated Conductive Fabrics Using Convolutional Neural Network-Based Image Analysis

This study proposes a convolutional neural network (CNN)-based image analysis method to evaluate the electrical properties and uniformity of conductive fabrics treated with single-walled carbon nanotube (SWCNT) dip-coating. The conductive fabric was produced by dip-coating cotton-blended spandex with SWCNT, and the surface images were scanned and preprocessed to obtain image data, while resistance measurements were conducted to obtain labels and build the dataset. SEM analysis revealed that as the number of dip-coating cycles increased, particle density and path formation improved. The CNN model learned the relationship between surface images and resistance values, achieving a high predictive performance, with an R-squared (R²) value of 0.9422. The model demonstrated prediction accuracies of 99.1792% for the coefficient of variation (CV) of uniformly coated fabrics and 96.8877% for non-uniformly coated fabrics. Additionally, p-value analysis of all fabric samples yielded a result of 0.96044, indicating no statistically significant difference between the predicted and actual values. The proposed CNN-based model can accurately evaluate the electrical uniformity of conductive fabrics, showing potential for contributing to quality control and process optimization in production.

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

AbstractThe spatio-temporal prediction of air pollutant concentrations is vital for assessing regulatory compliance and for producing exposure estimates in epidemiological studies. Numerous approaches have been utilised for making such predictions, including land use regression models, additive models, spatio-temporal smoothing models and machine learning prediction algorithms. However, relatively few studies have compared the predictive performance of these models thoroughly, which is one of the novel contributions of this paper. For the specific challenge of predicting monthly average concentrations of NO$$_{2}$$ 2 , PM$$_{10}$$ 10 and $$\hbox {PM}_{2.5}$$ PM 2.5 in Scotland, we find that random forests typically outperform (or are as good as) more traditional statistical prediction approaches. Additionally, we utilise the best performing model to provide a new data resource, namely, predictions of monthly average concentrations (with uncertainty quantification) of the above pollutants on a regular 1 km$$^{2}$$ 2 grid for all of Scotland between 2016 and 2020.

The Impact of Systemic Inflammation on Recurrence in Patients with Congenital Nasolacrimal Duct Obstruction

Background/Objective: Congenital nasolacrimal duct obstruction (CNLDO) is commonly treated by probing, but recurrence remains a clinical issue. This study investigates the potential role of inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), in predicting recurrence after probing in children with CNLDO. Methods: This retrospective cohort study included 172 patients who underwent initial probing for unilateral CNLDO. The patients were then categorized into two groups: those who experienced the complete resolution of symptoms after primary probing, and those who required Ritleng tube intubation due to recurrence following primary probing. Blood samples for calculating inflammatory biomarkers in all subjects were collected during general anesthesia preparation prior to initial probing. NLR, MLR, and PLR values were compared between the groups using the independent samples t-test. The predictive performance of the inflammatory biomarkers for recurrence was assessed using Receiver Operating Characteristic (ROC) curve analysis. Results: A total of 110 patients were included in the probing group, while 62 patients were in the recurrence group. The mean age at the time of the initial probing procedure was 15 ± 4.06 months in the probing group and 15.83 ± 4.02 months in the recurrence group. There was no difference in the duration of the probing procedure between the groups. The mean age at the time of Ritleng tube intubation in the recurrence group was 37.80 ± 13.34 months. The recurrence group exhibited significantly higher values in all analyzed inflammatory markers compared to the probing group, including the NLR (1.12 ± 0.56 vs. 0.86 ± 0.39, p = 0.002), MLR (0.16 ± 0.06 vs. 0.14 ± 0.06, p = 0.005), and PLR (95.13 ± 24.34 vs. 82.23 ± 22.77, p < 0.001). ROC curve analysis indicated that these inflammatory biomarkers demonstrated moderate performance in predicting recurrence. Conclusions: Recurrence following probing in children with CNLDO was associated with complete blood cell count-derived inflammatory biomarkers. The preoperative assessment of these biomarkers may aid in the individualization of disease management and inform the development of new therapeutic strategies.

Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma

Objectives The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Materials and Methods Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36). Results Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI. Conclusions CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.

Identification of key programmed cell death genes for predicting prognosis and treatment sensitivity in colorectal cancer

Colorectal cancer (CRC) ranks third in global incidence and second in mortality. However, a comprehensive predictive model for CRC prognosis, immunotherapy response, and drug sensitivity is still lacking. Various types of programmed cell death (PCD) are crucial for cancer occurrence, progression, and treatment, indicating their potential as valuable predictors. Fourteen PCD genes were collected and subjected to dimensionality reduction using regression methods to identify key hub genes. Predictive models were constructed and validated based on bulk transcriptomes and single-cell transcriptomes. Furthermore, the tumor microenvironment, immunotherapy response, and drug sensitivity profiles among patients with CRC were explored and stratified by risk. A risk score incorporating the PCD genes FABP4, AQP8, and NAT1 was developed and validated across four independent datasets. Patients with CRC who had a high-risk score exhibited a poorer prognosis. Unsupervised clustering algorithms were used to identify two molecular subtypes of CRC with distinct features. The risk score was combined with the clinical features to create a nomogram model with superior predictive performance. Additionally, patients with high-risk scores exhibited decreased immune cell infiltration, higher stromal scores, and reduced responsiveness to immunotherapy and first-line clinical drugs compared with low-risk patients. Furthermore, the top ten non-clinical first-line drugs for treating CRC were selected based on their predicted IC50 values. Our results indicate the efficacy of the model and its potential value in predicting prognosis, response to immunotherapy, and sensitivity to different drugs in patients with CRC.

Approximate Cross-Validated Mean Estimates for Bayesian Hierarchical Regression Models

We introduce a novel procedure for obtaining cross-validated predictive estimates for Bayesian hierarchical regression models (BHRMs). BHRMs are popular for modeling complex dependence structures (e.g., Gaussian processes and Gaussian Markov random fields) but can be computationally expensive to run. Cross-validation (CV) is, therefore, not a common practice to evaluate the predictive performance of BHRMs. Our method circumvents the need to rerun computationally costly estimation methods for each cross-validation fold and makes CV more feasible for large BHRMs. We shift the CV problem from probability-based sampling to a familiar and straightforward optimization problem by conditioning on the variance-covariance parameters. Our approximation applies to leave-one-out CV and leave-one-cluster-out CV, the latter of which is more appropriate for models with complex dependencies. In many cases, this produces estimates equivalent to full CV. We provide theoretical results, demonstrate the efficacy of our method on publicly available data and in simulations, and compare the model performance with several competing methods for CV approximation. Code and other supplementary materials available online.

Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model

Solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to estimate the solar irradiance of typical irrigation areas. The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as input parameters. In the model testing, the RMSE and MAE of WOA-XGBoost are 2.142 MJ·m−2·d−1 and 1.531 MJ·m−2·d−1, respectively, while those of XGBoost are 2.298 MJ·m−2·d−1 and 1.598 MJ·m−2·d−1. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model. The model developed in this study can be applied to forecast solar irradiance in different regions. By inputting the meteorological parameter data specific to a given area, this model can effectively produce accurate solar irradiance predictions for that region. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

Precision medicine in diabetes prediction: Exploring a subgroup-specific biomarker strategy for risk stratification.

The early detection of high-risk individuals is crucial to delay and reduce the incidence of type 2 diabetes. In this study, we aimed to explore the performance of a novel subgroup-specific biomarker strategy in the prediction of incident diabetes. In the Taiwan Lifestyle Cohort Study, adult subjects without diabetes were included and followed for the incidence of diabetes in 2006-2019. The biomarkers measured included blood secretogranin III (SCG3), vascular adhesion protein-1 (VAP-1), fibrinogen-like protein 1 (FGL1), angiopoietin-like protein 6 (ANGPTL6), and angiopoietin-like protein 4 (ANGPTL4). Among the 1,287 subjects, 12.2% developed diabetes during a 6 year follow-up. Blood VAP-1 was significantly associated with incident diabetes in the overall population (HR = 0.724, P < 0.05), participants under 65 years old (HR = 0.685, P < 0.05), those with a BMI of ≥24 kg/m2 (HR = 0.673, P < 0.05), and females (HR = 0.635, P < 0.05). Blood ANGPTL6 was significantly correlated with incident diabetes in participants aged 65 and older (HR = 0.314, P < 0.05), and blood SCG3 was associated with incident diabetes in those with a BMI of <24 kg/m2 (HR = 1.296, P < 0.05). Two subgroup-specific biomarker strategies were developed. The gender and BMI-specific biomarker strategy, using traditional risk factors and blood SCG3 or VAP-1 in different subgroups, could improve prediction performance, especially the specificity and positive prediction value, compared with the whole-population strategy using only traditional risk factors or traditional risk factors plus blood VAP-1. Gender- and BMI-specific biomarker strategy can improve the prediction of incident diabetes. A subgroup-specific biomarker strategy is a novel approach in the prediction of incident diabetes.