Prediction Accuracy Of Model Research Articles

Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data

Targeted contingency measures have proven highly effective at reducing the duration and harm caused by incidents. This study utilized the Classification and Regression Trees (CART) data mining technique to predict and quantify the duration of incidents. To achieve this, multisensor data collected from the Hangzhou freeway in China spanning from 2019 to 2021 was utilized to construct a regression tree with eight levels and 14 leaf nodes. By extracting 14 rules from the tree and establishing contingency measures based on these rules, accurate incident assessment and effective implementation of post-incident emergency plans were achieved. In addition, to more accurately apply the research findings to actual incidents, the CART method was compared with XGBoost, Random Forest (RF), and AFT (accelerated failure time) models. The results indicated that the prediction accuracy of the CART model is better than the other three models. Furthermore, the CART method has strong interpretability. Interactions between explanatory variables, up to seven, are captured in the CART method, rather than merely analyzing the effect of individual variables on the incident duration, aligning more closely with actual incidents. This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data.

EV Charging Prediction in Residential Area Based on SE‐GRU‐MA Model Consider Multi‐Source Data Feature Mining

The number of electric vehicle (EV) in residential areas is growing rapidly, resulting in large‐scale charging of EVs connected to the distribution network. This poses a challenge to the safe and stable operation of the distribution network. In order to cope with this challenge, it is crucial to achieve accurate EV charging load prediction. However, current researches on EV charging load prediction suffer from insufficient data feature mining and lower prediction accuracy. To address this issue, this paper proposes a SE‐GRU‐MA residential EV charging load prediction method that incorporates multi‐source data feature mining. The proposed method employs a multi‐source data feature mining approach based on Pearson correlation analysis, which enhances the training efficiency and prediction accuracy of the prediction model. Additionally, this study develops a prediction model based on SE‐GRU‐MA hybrid network to achieve accurate EV charging load prediction. Simulation cases on actual history data validate that the proposed feature mining method can effectively promote prediction accuracy, and proposed SE‐GRU‐MA prediction model exhibits superior prediction capability in comparison to existing models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Aerodynamic Optimization Method for Propeller Airfoil Based on DBO-BP and NSWOA

To address the issues of tedious optimization processes, insufficient fitting accuracy of surrogate models, and low optimization efficiency in drone propeller airfoil design, this paper proposes an aerodynamic optimization method for propeller airfoils based on DBO-BP (Dum Beetle Optimizer-Back-Propagation) and NSWOA (Non-Dominated Sorting Whale Optimization Algorithm). The NACA4412 airfoil is selected as the research subject, optimizing the original airfoil at three angles of attack (2°, 5° and 10°). The CST (Class Function/Shape Function Transformation) airfoil parametrization method is used to parameterize the original airfoil, and Latin hypercube sampling is employed to perturb the original airfoil within a certain range to generate a sample space. CFD (Computational Fluid Dynamics) software (2024.1) is used to perform aerodynamic analysis on the airfoil shapes within the sample space to construct a sample dataset. Subsequently, the DBO algorithm optimizes the initial weights and thresholds of the BP neural network surrogate model to establish the DBO-BP neural network surrogate model. Finally, the NSWOA algorithm is utilized for multi-objective optimization, and CFD software verifies and analyzes the optimization results. The results show that at the angles of attack of 2°, 5° and 10°, the test accuracy of the lift coefficient is increased by 45.35%, 13.4% and 49.3%, and the test accuracy of the drag coefficient is increased by 12.5%, 39.1% and 13.7%. This significantly enhances the prediction accuracy of the BP neural network surrogate model for aerodynamic analysis results, making the optimization outcomes more reliable. The lift coefficient of the airfoil is increased by 0.04342, 0.01156 and 0.03603, the drag coefficient is reduced by 0.00018, 0.00038 and 0.00027, respectively, and the lift-to-drag ratio is improved by 2.95892, 2.96548 and 2.55199, enhancing the convenience of airfoil aerodynamic optimization and improving the aerodynamic performance of the original airfoil.

Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke

Background and objectivesThis study aimed to employ machine learning techniques to predict the clinical efficacy of acupuncture as an intervention for patients with upper limb motor dysfunction following ischemic stroke, as well as to assess its potential utility in clinical practice.MethodsMedical records and digital subtraction angiography (DSA) imaging data were collected from 735 ischemic stroke patients with upper limb motor dysfunction who were treated with standardized acupuncture at two hospitals. Following the initial screening, 314 patient datasets that met the inclusion criteria were selected. We applied three deep-learning algorithms (YOLOX, FasterRCNN, and TOOD) to develop the object detection model. Object detection results pertaining to the cerebral vessels were integrated into a clinical efficacy prediction model (random forest). This model aimed to classify patient responses to acupuncture treatment. Finally, the accuracies and discriminative capabilities of the prediction models were assessed.ResultsThe object detection model achieved an optimal recognition rate, The mean average precisions of YOLOX, TOOD, and FasterRCNN were 0.61, 0.7, and 0.68, respectively. The prediction accuracy of the clinical efficacy model reached 93.6%, with all three-treatment response classification area under the curves (AUCs) exceeding 0.95. Feature extraction using the prediction model highlighted the significant influence of various cerebral vascular stenosis sites within the internal carotid artery (ICA) on prediction outcomes. Specifically, the initial and C1 segments of the ICA had the highest predictive weights among all stenosis sites. Additionally, stenosis of the middle cerebral, anterior cerebral, and posterior cerebral arteries exerted a notable influence on the predictions. In contrast, the stenosis sites within the vertebral artery exhibited minimal impact on the model's predictive abilities.ConclusionsResults underscore the substantial predictive influence of each cerebral vascular stenosis site within the ICA, with the initial and C1 segments being pivotal predictors.

Enhancing Fluvial Modeling and Flood Prediction Accuracy through the Fusion of 3D Point Clouds, Multispectral, and RGB Data

Enhancing Fluvial Modeling and Flood Prediction Accuracy through the Fusion of 3D Point Clouds, Multispectral, and RGB Data

Sonophotocatalytic degradation of rhodamine B dye using Zr doped ZnO nanoparticles: Kinetic study and toxicity assessment

This study investigates sonophotocatalysis for the oxidation of Rhodamine B (RhB) dye using Zr doped ZnO nanoparticles. The synthesized nanoparticles were characterized by scanning electron microscope, Fourier-transform infrared, X-ray diffraction, Brunauer Emmett-Teller, thermogravimetric analysis, and photoluminescence. The hybrid sonophotocatalysis system achieved 98.73 ± 1.25% degradation of RhB under the conditions: RhB dye concentration = 10 ppm, 4 wt% of Zr doped ZnO = 0.1 g, reaction temperature = 30 °C, time = 60 min, pH = 7. A kinetic model was developed to predict the efficiency of RhB degradation through intrinsic elementary chemical reactions. The high coefficient of determination (R2 = 0.96) indicates the model's prediction accuracy in describing the degradation kinetics of RhB within sonophotocatalysis system. The electrical energy per order (EEO) analysis demonstrated that US + UVC + 4 wt% of Zr doped ZnO significantly reduced EEO (1055 kWh m−3 order−1). The synergy index for this combination was approximately 1.60, demonstrating a significant synergistic effect. Density Functional Theory (DFT) studies were utilized to propose the most probable degradation pathway, identifying reactive sites and potential byproducts. The simulations revealed the central carbon atom (1C site) as highly susceptible to radical attacks, indicating potential decolorization pathways such as N-de-ethylation, chromophore cleavage, and ring opening. Toxicity assessments of both the parent dye and its intermediates were conducted using ECOSAR (Ecological Structure Activity Relationship) to evaluate their ecological impacts. The combination of experimental results and kinetic modeling and simulations offers a deep understanding of RhB degradation complexities, driving advancements in sustainable water treatment technologies.

Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells

This study thoroughly investigates the complex origins of abnormal formation pressure in offshore oil and gas wells, taking the Rio del Rey Basin in Cameroon as a case study. Renowned for its abundant oil and gas resources, the area faces unique challenges in predicting overpressure due to its high-temperature and high-pressure reservoir characteristics. By quantitatively analyzing the main mechanisms such as undercompaction, high-temperature fluid expansion, and mud diapirism, the study addresses the complexities of overpressure prediction. This paper introduces an innovative analytical framework that combines hierarchical clustering algorithms with the LightGBM model. Further refined by the application of Bayesian optimization, the model intelligently adjusts hyperparameters to enhance predictive accuracy. Utilizing well logging data and applying machine learning techniques, the paper classifies and identifies different mechanisms causing abnormal pressures, achieving a model prediction accuracy of 0.942. The research findings highlight the predominant role of the undercompaction mechanism, accounting for approximately 70% of the abnormal high-pressure events in the study area. Fluid expansion and shale diapirism contribute smaller but significant proportions of 10% and 20%, respectively. These quantitative insights into the pressure mechanisms are vital for optimizing drilling operations and reducing risks in oil and gas exploration. The study’s hybrid approach, integrating geophysical analysis with advanced computational techniques, sets a precedent for future research. It provides new avenues for applying machine learning to understand complex geological phenomena in similar geological environments and makes a significant contribution to the strategic planning of hydrocarbon exploration and production activities.

Conductivity Prediction Method of Solid Electrolyte Materials Based on Pearson Coefficient Method and Ensemble Learning

In order to screen solid electrolytic materials with high ionic conductivity more quickly and accurately, a conductivity prediction method for solid electrolyte materials based on logistic regression model and random forest regression model is constructed. The method first selects 20 characteristic descriptors related to ionic conductivity from material library according to four dimensions: structural stability, metal stability, electronic conductivity and oxidative decomposition stability. The enumeration method and feature selection method are used to combine different features. Then, the dimension reduction of the feature combinations is carried out by Pearson coefficient method, so as to screen the optimal feature combinations. Finally, according to the optimal feature combination and data subset, the constructed random forest ensemble learning model is utilized to predict the ionic conductivity of other solid-state electrolyte materials in the material library, in order to find the solid-state electrolyte materials that can meet the high conductivity. The results indicate that as the number of features in the combination increases, the prediction performance of the model shows a trend of first increasing and then decreasing. When the feature descriptor in the combination is 7, the maximum AUC (Accuracy) value is reached, and the optimal feature in this case contains 7 optimal feature subsets corresponding to feature descriptors, namely the standard deviation of the average adjacency number of Li atoms, the standard deviation of Li-X ion bonds, the average electronegativity of the straight path, the average width of the straight path, the average atomic volume, the average Li-Li bond, and the filling fraction of the sublattice. On the basis of the optimal feature screening, the average prediction accuracy of the logistic regression model is 87.13%, which has a high accuracy. Using the random forest regression model for prediction, the average absolute error and root mean square error obtained are only 0.237 and 0.134, and compared with classical classification methods such as KNN, SVM and Adaboost, they are smaller, which can better predict the ionic conductivity of solid electrolyte materials. This proves that the constructed method can quickly and accurately produce high ionic conductivity materials in solid electrolyte materials, which is worthy of further research and promotion.

Rapid identification of fragrant rice using starch flavor compound via NIR spectroscopy coupled with GC–MS and Badh2 genotyping

The identification of fragrant rice varieties using near-infrared reflectance spectroscopy (NIRS) models has attracted extensive attention from regulatory authorities worldwide. In this study, 138 fragrant and 54 nonfragrant rice varieties were planted in the same region and distinguished using sensory evaluation, gas chromatography–mass spectrometry analysis, and betaine aldehyde dehydrogenase 2 (Badh2) genotyping. Then, the 2-acetyl−1-pyrroline (2-AP) content was assessed based on partial least-squares discriminant (PLS-DA) models generated after 2nd individually or combined with SNV/MSC/smoothing preprocessing successfully classified fragrant rice both in the calibration and predictive sets. Moreover, design of experiments (DoE)-based preprocessing selection was employed as an effective strategy to optimize the calibration models compared with the one variable at a time (OVAT) method. Further, Badh2 genotype sample screening assisted with identifying authentic fragrant rice and guaranteed the NIRS model's prediction accuracy in identifying fragrant rice. In conclusion, the high throughput PLS-DA multivariate method coupled with NIRS data was applied to identify fragrant rice varieties in routine monitoring and was effective, accurate, and rapid.

Local two-group bubble size model for adiabatic air–water flow in a large diameter pipe using CFD code

Accurate prediction of bubble behavior in large diameter pipes is crucial for evaluating the performance of safety systems, steam generators, and heat exchangers in nuclear systems. Bubble behavior in large diameter pipes under two-phase flow significantly differs from that in small pipes. With the increasing use of computational fluid dynamics (CFD) codes, predicting interfacial area concentration (IAC) is critical for understanding multi-dimensional bubble behavior. This study developed a two-group local bubble size model for bubbly, slug, and churn flows under adiabatic conditions. The model includes correlations for void fraction and bubble sizes of two groups, which were implemented into CFD codes and validated against experimental data from large diameter pipes with low-pressure air–water flow. Results show the model's prediction accuracy surpasses existing correlations. The developed correlations are applicable across a range of flow conditions covering pipe diameters in the range 0.05–0.152 m, pressures from atmospheric to 300 kPa, superficial liquid velocities from 0.25 m/s to 2.85 m/s, and superficial gas velocities from 0.04 m/s to 5.48 m/s. The model is expected to enhance the prediction capabilities of CFD codes for the adiabatic two-group two-phase flows in the large diameter pipes.

A radius and minimum velocity Jensen model for far wake distribution prediction of tidal stream turbine

The rational layout of tidal stream turbines (TSTs) is beneficial for making full use of tidal stream energy. It is essential to consider the wake radius and velocity distribution for determining the spacing between the TSTs. The wake is primarily affected by the turbulence intensity. The attenuation of turbulence results in the non-linear expansion of the wake. Additionally, the high turbulence in the near wake region inhibits the velocity deficit, which is more evident under high ambient turbulence intensity. Therefore, a radius and minimum velocity Jensen (RMV-Jensen) model is proposed to predict the wake radius and the wake velocity distribution downstream of a TST. The RMV-Jensen model consists of a radius block (R-Block) and a minimum velocity block (MV-Block). The R-Block is a piecewise exponential function based on the turbulence attenuation in the wake region, accurately describing the change of the wake expansion coefficient. The MV-Block is a wake minimum velocity model, and the inhibitory effect of turbulence on velocity deficit is considered for the first time. The RMV-Jensen model is applied to predict the wake distribution in the Zhoushan sea area. The prediction accuracy of the RMV-Jensen model is improved by 10%–20% compared to that of the classical Jensen model, according to the experimental results.

Study on characteristics and prediction of the pressure drag of the swept strut under supersonic wide-range conditions

In this paper, the numerical simulation of the swept strut ramjet combustor was carried out under wide-range conditions (Ma3 = 1.8 ∼ 5.0), and the pressure drag characteristics of the swept strut were discussed. The results show that the pressure drag characteristics of the swept strut are related to the Mach number of the combustor inlet and the swept angle of the strut. The decreased boundary of the strut pressure drag coefficient gradually advances with the decrease of the Mach number of the combustor inlet. When Ma3 = 1.8 ∼ 2.0, the pressure drag reduction boundary is α = 15°. When Ma3 = 2.2 ∼ 2.8, the pressure drag reduction boundary is α = 45°. When Ma3 = 3.0 ∼ 4.0, the pressure drag reduction boundary is α = 60°. When Ma3 = 5.0, the pressure drag reduction boundary is α = 65°. In addition, with the decrease of the Mach number of the combustor inlet, the pressure drag reduction performance benefit brought by increasing the swept angle of the strut will gradually increase. Furthermore, a pressure drag coefficient prediction model suitable for wide-range conditions and multiple configurations of swept struts was proposed based on deep learning. The prediction model consists of two parts in series, which includes the prediction model of the surface pressure coefficient of the swept strut based on multilayer perceptron (MLP) and the prediction model of the pressure drag coefficient of the swept strut based on convolutional neural network (CNN). To improve the prediction accuracy of the MLP model, new training samples were added based on the ensemble-based uncertainty quantification, and the improved MLP model was obtained by retraining. The results show that both the two prediction models have high prediction accuracy under the effect of multiple complex flow characteristics on the strut. The results of this study are helpful to provide a reference for the aerodynamic drag reduction design of the strut in the wide-speed supersonic combustor.

Validation of the Short Form Shoulder Instability Return to Sport After Injury (SIRSI-5) and Its Association With Return to Sports.

Psychosocial readiness is thought to be important in establishing whether athletes are ready to return to sports (RTS). The 5-item Shoulder Instability-Return to Sport After Injury (SIRSI-5) is a short-form version of the 12-item questionnaire (SIRSI-12), which was validated in a postoperative Argentinian population and associated with psychological readiness to RTS. It is unknown if the SIRSI-5 is valid in surgical and nonsurgical populations in other geographic locations. To establish whether the SIRSI-5 is valid in another population and if it is associated with RTS. Cross-sectional study (diagnosis); Level of evidence, 3. A total of 79 participants with anterior shoulder instability managed operatively and nonoperatively were included. The SIRSI-5 and SIRSI-12 total scores were calculated. Intraclass correlation coefficients (ICCs) and Pearson correlations assessed convergent validity between SIRSI-5 and SIRSI-12. Logistic regression was used to assess the discrimination ability (leave-one-out cross-validation [LOOCV]) of a preexisting SIRSI-5 cut point score (≥59/100) to determine participants who had returned to their preinjury level of sport. In addition, the discrimination ability (LOOCV) of multivariable machine learning models including age, sex, time since injury (months), SIRSI-5 (continuous score), surgery, and contact/noncontact sport to predict return to preinjury level sport was also assessed. The Youden index was calculated to assess the models' performance. Of the 79 participants, 62 (78.5%) were male and 27 (34%) had been treated surgically. Only 32% (n = 25) of participants had returned to preinjury level sport. The SIRSI-5 and SIRSI-12 were highly correlated (r > 0.9) with high levels of agreement (ICC, 0.90; 95% CI, 0.76-0.95). The prediction accuracy of the SIRSI-5 cut point score model was 66% (Youden index, 0; sensitivity, 4%; specificity, 94%). The best prediction accuracy obtained through a machine learning model (multilayer perceptron) was 72% (Youden index, 0.4; sensitivity, 64%; specificity, 76%). The SIRSI-5 has excellent convergent validity with the SIRSI-12 to measure psychological readiness to RTS and can be used clinically with less patient burden than the SIRSI-12. However, in a population of surgically and nonsurgically treated contact athletes, the SIRSI-5 with a cut point of ≥59/100 had low levels of prediction accuracy for RTS at preinjury level. The SIRSI-5 should not be utilized in isolation to determine readiness to RTS, and clinicians should consider other factors such as age, type of sport, and time since injury to underpin their clinical reasoning when deciding for RTS.

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R2 = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection.

Prediction of joint roughness coefficient via hybrid machine learning model combined with principal components analysis

Joint roughness coefficient (JRC) is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice. The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information. In this paper, a dataset of eight roughness statistical parameters covering 112 digital joints is established. Then, the principal component analysis method is introduced to extract the significant information, which solves the information overlap problem of roughness characterization. Based on the two principal components of extracted features, the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model, and a new machine learning (ML) prediction model was established. The prediction accuracy of the new model and the other 17 models was measured using statistical metrics. The results show that the prediction result of the new model is more consistent with the real JRC value, with higher recognition accuracy and generalization ability.

Mathematical Modeling of Gas-phase Mass Transfer in Hydrous Materials for a Total Heat Exchange Ventilator

Total heat exchange ventilation systems are effective in achieving energy savings by reducing the ventilation load in buildings, while maintaining a certain amount of fresh outdoor air intake. As the system's elemental materials exchange both latent and sensible heat, hydrophilic chemical compounds may be exchanged simultaneously. Proper control of the exchange of hazardous chemicals and pollutants via these heat exchange elements is an important issue in the development of total heat exchange ventilation systems. In this respect, the development of a numerical model that facilitates repeated sensitivity analysis is important in the development of a new total heat exchanger that has high heat exchange efficiency and suppresses the exchange of polluting chemicals. This study proposes new hygrothermal and chemical compound transfer models for paper-based hydrous materials, which are the main components of total heat exchangers in indoor ventilation systems. Through a series of numerical analyses and experimental measurements, the prediction accuracy of the mathematical model was compared with experimental results for the gas transfer rate in hydrous materials and a building-sized total heat exchanger. The results demonstrated that an increase in water content in hydrous material has a significant impact on the permeability of water-soluble gases, with NH3 and HCHO permeability coefficients increasing by factors of 250 and 20 respectively. Conversely, for low-solubility gases such as CO2, the permeability coefficient only slightly increased at low humidity and remained largely unaffected thereafter. These findings contribute to the advancement of more efficient and safer total heat exchange ventilation systems.

Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields

Rapid and accurate estimation of canopy water content (CWC) is important for agricultural water management and food security. Due to the complexity of dynamic changes in water transport during plant growth, estimation of CWC using a single sensor often leads to high uncertainty in the results. Multi-sensor data fusion is one of the solutions to this problem, but suitable spectral preprocessing methods and data fusion methods still need further research. The objectives of this study were to characterize the performance of two varieties at different growth stages under five water stress conditions and screen hyperspectral sensitive spectral bands by using continuous wavelet transform (CWT) and a successive projection algorithm (SPA). Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. The results showed that canopy temperature parameters and spectral parameters responded significantly to water deficits in winter wheat. Using the CWT-SPA method, a total of 285 hyperspectral feature bands with wavelet decomposition scales ranging from one to eight were selected. The sensitive bands were mainly distributed in the following ranges: 545–561, 746–1348, 1561–1810, and 2122–2430 nm. The GRA algorithm has good multi-source data model fusion capability, and its constructed prediction model based on hyperspectral and thermal image fusion has high accuracy on the canopy water content in winter wheat (R2 = 0.930, RMSE = 5.44%, nRMSE = 7.94%). Compared to the single-feature spectral model (R2 = 0.864, RMSE = 5.92%, nRMSE = 8.63%) and thermal image CWC prediction model (R2 = 0.813, RMSE = 7.22%, nRMSE = 10.49%), the model prediction accuracy based on the GRA algorithm is increased by 7.64% and 13.69%, respectively.

Research on the P‐S‐N Curve Fitting Method of Notched Specimens Considering Small Sample Properties

ABSTRACTAiming at the issue of fatigue test data for large‐scale mechanical components of building steel are very limited, a method for fitting P‐S‐N curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the P‐S‐N curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting P‐S‐N curve is more reliable and accurate.

Estimating Summer Maize Biomass by Integrating UAV Multispectral Imagery with Crop Physiological Parameters.

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446-0.537. The R2 of the PLSR (CIS) model is 0.323-0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles.

Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q2, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and R2. We simulated all EXSCEL trial participants (N = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q2: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q2 facilitated comparisons between subgroups and (unlike R2) was lower for biased predictors. Q2 for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q2, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q2 or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.