Relative Prediction Error Research Articles

Predictive Model for Listeria monocytogenes in RTE Meats Using Exclusive Food Matrix Data.

Post-processing contamination of Listeria monocytogenes has remained a major concern for the safety of ready-to-eat (RTE) meat products that are not reheated before consumption. Mathematical models are rapid and cost-effective tools to predict pathogen behavior, product shelf life, and safety. The objective of this study was to develop and validate a comprehensive model to predict the Listeria growth rate in RTE meat products as a function of temperature, pH, water activity, nitrite, acetic, lactic, and propionic acids. The Listeria growth data in RTE food matrices, including RTE beef, pork, and poultry products (731 data sets), were collected from the literature and databases like ComBase. The growth parameters were estimated using the logistic-with-delay primary model. The good-quality growth rate data (n = 596, R2 > 0.9) were randomly divided into 80% training (n = 480) and 20% testing (n = 116) datasets. The training growth rates were used to develop a secondary gamma model, followed by validation in testing data. The growth model's performance was evaluated by comparing the predicted and observed growth rates. The goodness-of-fit parameter of the secondary model includes R2 of 0.86 and RMSE of 0.06 (μmax) during the development stage. During validation, the gamma model with interaction included an RMSE of 0.074 (μmax), bias, and accuracy factor of 0.95 and 1.50, respectively. Overall, about 81.03% of the relative errors (RE) of the model's predictions were within the acceptable simulation zone (RE ± 0.5 log CFU/h). In lag time model validation, predictions were 7% fail-dangerously biased, and the accuracy factor of 2.23 indicated that the lag time prediction is challenging. The model may be used to quantify the Listeria growth in naturally contaminated RTE meats. This model may be helpful in formulations, shelf-life assessment, and decision-making for the safety of RTE meat products.

Analysis of time-to-positivity data in tuberculosis treatment studies: Identifying a new limit of quantification.

The BACTEC Mycobacteria Growth Indicator Tube (MGIT) machine is the standard globally for detecting viable mycobacteria in patients' sputum. Samples are observed for no longer than 42 days, at which point the sample is declared "negative" for tuberculosis (TB). This time to detection of bacterial growth, referred to as time-to-positivity (TTP), is increasingly of interest not solely as a diagnostic tool, but as a continuous biomarker wherein change in TTP can be used for comparing the bactericidal activity of different TB treatments. However, as a continuous measure, there are oddities in the distribution of TTP values observed, particularly at higher values. We explored whether there is evidence to suggest setting an upper limit of quantification for modeling purposes (ULOQM) lower than the diagnostic limit of detection (LOD) using data from several TB-PACTS randomized clinical trials and PanACEA MAMS-TB. Across all trials, less than 7.1% of weekly samples returned TTP measurements between 25 and 42 days. Further, the relative absolute prediction error (%) was highest in this range. When modeling with ULOQMs of 25 and 30 days, estimator precision improved for 23 of 25 regimen-level slopes compared to models using the LOD. Discrimination between regimens based on Bayesian posteriors also improved. While TTP measurements between 25 days and the diagnostic LOD may be important for diagnostic purposes, TTP values in this range may not contribute meaningfully to its use as a quantitative measure, particularly when assessing treatment response, and may lead to under-powered clinical trials.

Real-Time prediction of pool fire burning rates under complex heat transfer effects influenced by ullage height: A comparative study of BPNN and SVR

This research utilizes machine learning methods to forecast the complex, non-linear thermal phenomena, along with heat transfer mechanisms, that influence the burning rate of pool fires, especially with changes in ullage height. Experiments involving pool fires were systematically designed and carried out, incorporating different diameters and ullage heights. Heptane was used as the representative alkane fuels. A dataset containing more than 70,000 sets of data was created as a training dataset for training the Backpropagation Neural Network (BPNN) and Support Vector Regression (SVR) models. During the optimization of machine learning model parameters, this study is based on Particle Swarm Optimization (PSO) with the principle of intelligent optimization to efficiently and accurately screen and optimize the key parameters of the model. The combustion duration, pool dimensions, and non-dimensional ullage height were input into a machine-learning model to predict the burning rate. By comparing against experimental data, the model was found to be able to predict the dynamic evolution of the burning rate of the pool fire in a real-time manner. The SVR model demonstrates greater predictive accuracy in comparison to the BPNN model, and the relative prediction error remains within ± 20 %, which fully proves its effectiveness and generalization ability in the prediction of pool fire burning rate. The insights gained will offer substantial scientific backing for enhanced fire monitoring systems, while highlighting the capability of advanced machine learning methodologies to predict the intricate, real-time thermal dynamics and heat transfer characteristics of burning liquid fuels.

Water Use Attribution Analysis and Prediction Based on the VIKOR Method and Grey Neural Network Model: A Case Study of Zhangye City

Water consumption forecasting is a critical aspect of the increasingly strained water resources and sustainable water management processes. It is essential to explore the current status of water use patterns and future development directions in Zhangye City. In this study, 17 factors affecting water consumption in Zhangye City were selected to analyze changes in water consumption and to predict values from 2003 to 2022, utilizing the entropy weight–VIKOR model and the grey neural network model. The results indicate that agricultural water consumption and annual rainfall are the factors with the largest weights among the social and natural attribute indicators, respectively, significantly influencing water consumption in Zhangye City. As the proportions of water consumption for forestry, animal husbandry, fishery, livestock, urban public use, and ecological environment increase, while agricultural water consumption continues to decline, the overall water consumption trend in Zhangye City from 2003 to 2022 shows a positive trajectory. Each water consumption factor is tending toward greater balance, and the relationship between water supply and distribution is improving. The multi-year average relative error of the water consumption predictions for Zhangye City from 2003 to 2022 using the grey neural network model was 4.28%. Furthermore, the relative error values for annual predictions ranged from 0.60% to 5.00%, achieving an accuracy rate of 80.00%. This indicates a strong predictive performance. Ultimately, the model was used to predict a water consumption of 20.18 × 108 m3 in Zhangye City in 2027. The model can serve as a theoretical reference for short-term water consumption forecasting and for establishing a basin water resource allocation system in Zhangye City.

Chemometric-assisted spectrophotometric methods for simultaneous drug determination in new Helicobacter pylori treatment regimens - Environmental sustainability assessment

The recent FDA approval of VOQUEZNA™ TRIPLE PAK™ 7-day therapy, which includes vonoprazan (VON), amoxicillin (AMO), and clarithromycin (CLA), marks a significant advancement in the treatment of Helicobacter pylori (H. pylori) infections. Accurate quantification of these active pharmaceutical ingredients (APIs) is critical for ensuring therapeutic efficacy and safety. However, conventional analytical methods often require extensive sample pretreatment and separation, which can be time-consuming and environmentally burdensome. This study presents the first simultaneous quantification of VON, AMO, and CLA using innovative chemometric-assisted spectrophotometric methods aligned with Green Analytical Chemistry (GAC) principles. Our methods eliminate the need for pretreatment or separation, thereby enhancing both analytical efficiency and environmental sustainability. We developed orthogonal partial least squares (OPLS), principal component regression (PCR), and Artificial Neural Network (ANN) models, utilizing the Design of Experiment (DoE) approach to minimize solvent use and waste.Model validation was achieved through Orthogonal Array-based Latin Hypercube Sampling (OALHS), ensuring robust performance evaluation. The models demonstrated high precision, with recovery percentages ranging from 98.00% to 102.00%. The calibration set model fitting was assessed using the determination coefficient (R2), and the cross-validation coefficient (Q2), all model's R2 and Q2 values were close to 1.0, indicating the calibration samples' high capacity for explanation and prediction, while the root mean square error of calibration (RMSEC) values were found to be less than 0.1. The prediction of the validation set was employed by the root mean square error of prediction (RMSEP) and relative root mean square errors of prediction (RRMSEP), the values were found (0.0335–0.0613) and (0.7207–0.5287) for RMSEP and RRMSEP, respectively, while the bias-corrected mean square error of prediction (BCMSEP) was found to be between 0.0014 and 0.0001. To evaluate and enhance the sustainability of the methods, comprehensive tools were utilized: SPIDER Solvent Tool, RGB12 Algorithm, AGREE, and the Need Quality Sustainability (NQS) Index. This work supports the Sustainable Development Goals (SDGs) by demonstrating advancements in environmentally sustainable analytical methods.

Multi-scale multi-task neural network combined with transfer learning for accurate determination of the ash content of industrial coal flotation concentrate

Ash content is a key indicator to evaluate coal flotation concentrate quality and adjust flotation process parameters, which could be determined by analyzing froth images. In this research, a multi-scale multi-task neural network (MSTNet) was developed to realize accurate determination of the ash content of industrial coal flotation concentrate by analyzing froth images. Furthermore, transfer learning is used to further improve model accuracy for low-resolution images. Results obtained using industrial data show that MSTNet achieves a higher prediction accuracy while requiring less computations than previous models. It reaches the maximum R2 of 0.9063 with a processing time of 0.0035 seconds per image, while its competitors only reach the maximum R2 of 0.7231 with a processing time of 0.0038 seconds per image. This suggests that MSTNet surpassing its competitors in both accuracy and speed. Furthermore, MSTNet achieves the minimum MAPE of 0.0300, indicating that MSTNet has a mean relative prediction error of ± 3 %. This proves the high prediction accuracy of MSTNet. These results indicate that the proposed MSTNet holds great promise for practical applications. Its practical application will lead to more efficient and intelligent coal production.

Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks

Focusing on the complex nonlinear problems of strength prediction and the material scheme design of modified red mud for use as a road material in engineering applications, a strength prediction neural network is established and utilized to optimize the material scheme, including the compound-solidifying agent ratio, water content, and curing age, based on experimental data accumulated during years of engineering practice and an artificial neural network. In this study, a backpropagation (BP) neural network is adopted, and 114 sets of experimental data are used to train the parameters of the unconfined compressive strength prediction model. Then, using the BP strength prediction model, the material scheme optimization process is carried out, with the strength and material costs as the objectives. The results show that the BP neural network model has a high prediction accuracy, the relative prediction error is basically within 10%, the root-mean-squared error is less than 0.04, and the correlation coefficient is more than 0.99. According to the strength requirements of modified red mud in different road projects and the constraints of each property, an optimal material scheme with a lower cost and higher 7 d target strength is obtained using a mix of polymer agent–fly-ash–cement–speed-cement in a ratio of 0.02%:1.96%:4.78%:0%, with a 33.93% water content of raw red mud, so that the target strength and material cost are 2.987 MPa and 17.099 CNY/T. This study creates an optimal material scheme, incorporating the compound-solidifying agent ratio, curing age, and water content of the modified red mud road material according to the strength requirements of different projects, thereby promoting the popularization of the utilization of red mud with better engineering practicability and economy.

WSA-SVR prediction model for maximum static response analysis of crawler crane’s lattice boom

A WSA-SVR prediction model using support vector regression (SVR) and water strider algorithm (WSA) is proposed to address the rapid prediction of the maximum static response of a crawler crane's lattice boom under various working cases. Firstly, a parametric model of the lattice boom is created using Ansys parametric design language (APDL), which can obtain maximum static response training samples under different working cases with the consideration of various structural parameters and loads. Then, the key parameters of the SVR are optimized via the WSA based on the static analysis samples of a certain crawler crane’s main boom scenarios. Finally, the WSA-SVR prediction model is established, and the results of test sets show that the prediction accuracy and training efficiency of the WSA-SVR model outperforms those of other models, which verifies the effectiveness of the proposed model. The prediction results under verification working conditions reveal that the proposed WSA-SVR model can rapidly predict the maximum static response values under various working conditions, with absolute relative errors in prediction results all below 8 %. The study dramatically improves the maximum static response analysis efficiency of the crawler crane’s lattice boom, providing an accurate and efficient proxy model for future structural risk assessment and size optimization design.

Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics

Supercritical water gasification (SCWG) is recognized as an efficient technology for biomass conversion, demonstrating substantial potential in the sustainable energy sector. This paper focuses on the H2 production by SCWG of biomass. The objective is to develop an accurate gasification kinetic model that can facilitate the optimization of industrial reactor designs. A series of SCWG experiments is conducted in a batch reactor system under temperature of 500–600 °C and residence time of 1–20 min. Based on the experimental results, a hybrid-driven model for SCWG reaction is established. Firstly, experimental data is utilized to delineate SCWG reaction mechanistic model and forecast gas yields with an acceptable error margin. Subsequently, a Wasserstein Generative Adversarial Network Gradient Boosting Regression Grid Search (WGAN-GBR-GRID) model is applied to acquire knowledge of the predictive errors from the mechanistic model and to develop a hybrid model. The hybrid-driven model significantly enhances the average relative prediction error from 15.56 % to 0.02 % when compared to the mechanistic model. This result underscores the potential of the hybrid model in optimizing SCWG technology and the Shapley Additive exPlanations (SHAP) values in feature analysis shows the possible shortcomings of mechanistic model, thereby providing a new perspective for SCWG reaction dynamics modeling.

Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.

Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation. The goal of our study was to develop and validate an ML-based limited sampling strategy (LSS) approach to determine ganciclovir AUC0-24 after administration of either intravenous ganciclovir or oral valganciclovir in children. Pharmacokinetic parameters from four published population pharmacokinetic models, in addition to the World Health Organization growth curve for children, were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles of children. Different ML algorithms were trained to predict AUC0-24 based on different combinations of two or three samples. Performances were evaluated in a simulated test set and in an external data set of real patients. The best estimation performances in the test set were obtained with the Xgboost algorithm using a 2 and 6 hours post dose LSS for oral valganciclovir (relative mean prediction error [rMPE] = 0.4% and relative root mean square error [rRMSE] = 5.7%) and 0 and 2 hours post dose LSS for intravenous ganciclovir (rMPE = 0.9% and rRMSE = 12.4%). In the external data set, the performance based on these two sample LSS was acceptable: rMPE = 0.2% and rRMSE = 16.5% for valganciclovir and rMPE = -9.7% and rRMSE = 17.2% for intravenous ganciclovir. The Xgboost algorithm developed resulted in a clinically relevant individual estimation using only two blood samples. This will improve the implementation of AUC-targeted ganciclovir therapeutic drug monitoring in children.

Combining capillary electrophoresis and chemometric tools for the straightforward determination of imidazolinone herbicides in plant-based milks.

Highly polar herbicides, such as imidazolinones, are used for weed control to increase agricultural productivity and crop quality. However, their misapplication can lead to residues in ready-to-eat food with a potential health risk for consumers. Hence, the fast determination of these herbicides is necessary for timely action. In this work, an eco-friendly method based on capillary zone electrophoresis combined with chemometrics was used for the determination of imazapyr and imazamox in vegetable-based beverages such as soy and quinoa milk. The analytical strategy consisted of only three steps: (i) protein precipitation prior to sample injection (ii) data pre-processing to reduce the background and make corrections on electrophoretic times shift, and (iii) resolution of fully overlapped capillary electrophoresis (CE) peaks by the well-known partial least square (PLS) algorithm, which extracts quantitative information attributed to the analytes. The method was successfully applied in the concentration range between 1.00 and 100 μg L-1 with coefficient of determination of the calibration (R2 cal) and prediction (R2 pred) > 0.90, residual prediction deviation of calibration (RPDcal) and of prediction (RPDpred) > 3, and relative error of prediction (REP) > 11 in the analyzed sample matrices, in the three built methods (quinoa samples, soy samples, and joint quinoa and soy samples). The proposed methodology offers a simple and quick alternative for determining imidazolinones at trace concentrations in vegetable beverages, such as quinoa and soy milk, without complex sample preparation. The results were consistent with those obtained using more complex techniques, confirming the applicability of this method. © 2024 Society of Chemical Industry.

Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning

Distinguishing gross primary productivity (GPP) into sunlit (GPPsu) and shaded (GPPsh) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPPsu and GPPsh. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models. Here, we propose the TL-CRF model, which uses the random forest technique to integrate various environmental variables, particularly for terrestrial water storage (TWS), into the TL-LUE model. Moreover, we consider seasonal differences in CI at a global scale. Based on 267 global eddy covariance flux sites, we explored the functional response of vegetation photosynthesis to key environmental factors, and trained and evaluated the TL-CRF model. The TL-CRF model was then used to simulate global eight-day GPP, GPPsu, and GPPsh from 2002 to 2020. The results show that the relative prediction error of environmental stress factors on the maximum LUE is reduced by approximately 52 % when these factors are integrated via the RF model. Thus the accuracy of global GPP estimation (R2 = 0.87, RMSE = 0.94 g C m−2 d−1, MAE = 0.61 g C m−2 d−1) in the TL-CRF model is greater than that (R2 = 0.76, RMSE = 2.18 g C m−2 d−1, MAE = 1.50 g C m−2 d−1) in the TL-LUE model, although this accuracy awaits further investigation among the released GPP products. TWS exerts the greatest control over ecosystem photosynthesis intensity, making it a suitable water indicator. Furthermore, the results confirm an optimal minimum air temperature for photosynthesis. Overall, these findings indicate a promising method for producing a new global GPP dataset, advancing our understanding of the dynamics and interactions between photosynthesis and environmental factors.

Investigation on compressive strength and splitting tensile strength of manufactured sand concrete: Machine learning prediction and experimental verification

Due to experimental constraints, traditional strength prediction models for manufactured sand concrete (MSC) exhibit significant limitations and have a narrow range of applicability. To overcome these limitations and enhance prediction accuracy, this paper, based on the widespread application of machine learning in concrete performance research, employed four algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) to develop highly accurate and strongly generalizable models for predicting the compressive strength (CS) and splitting tensile strength (STS) ofMSC. The predictive performance of each model was evaluated, the influence laws of various factors were analyzed, and finally, macroscopic experiments were conducted to validate the models' prediction accuracy and generalization ability. The development of these models and the analysis of the influence laws of various factors on the strength of MSC provide a valuable reference for the mix design. The conclusions are as follows: (1) The XGB model demonstrates the highest prediction accuracy and strongest generalization ability for both the CS and STS of MSC, achieving R2 values of 0.98 and 0.94 on the testing set, respectively. (2) The CS and STS of MSC are primarily influenced by four factors: water-binder ratio (W/B), curing age (AGE), cement (C), and coarse aggregate (RI≥0.08869, RI: relative importance), showing a positive correlation with AGE and C, and a negative correlation with W/B. As the fly ash content, stone powder content, maximum particle size of coarse aggregate, and sand ratio increase, the CS and STS initially increase and then decrease. Appropriately increasing the fineness modulus of manufactured sand is beneficial to both CS and STS, while the addition of slag is beneficial to CS and harmful to STS. (3) The prediction accuracy and generalization ability of the XGB model are verified through three sets of macroscopic experiments: C30, C35, and C40. The relative errors of each set's predictions are less than 8 %. (4) A graphical user interface is constructed based on the XGB model, enhancing its practicality.

A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory

The ionospheric F2 critical frequency (foF2) is one of the most crucial application parameters in high-frequency communication, detection, and electronic warfare. To improve the accuracy of spatial reconstruction of the ionospheric foF2, we propose a high-accuracy surface (HAS) modeling method. This method converts difficult-to-solve differential equations into more manageable algebraic equations using direct difference approximation, significantly reducing algorithm complexity and computational load while exhibiting excellent convergence properties. We used seven stations in Brisbane, Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville, with one station as a validation station and six as training stations (e.g., Brisbane as a validation station and the other stations—Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville—as training stations). The training stations and the HAS method were used to train and reconstruct the validation stations at different solar activity periods, seasons, and local times. The predicted values of the validation stations were compared with the measured values, and the proposed method was analyzed and validated. The reconstruction results show the following. (1) The relative root mean square errors (RRMSEs) of HAS method prediction in different solar activity epochs were 13.67%, 7.74%, and 9.19%, respectively, which are 13.57%, 7.41%, and 6.41% higher than the prediction accuracy of the Kriging method, respectively. (2) In the four seasons, the RRMSEs of the HAS method prediction are 9.27%, 13.1%, 8.81%, and 8.09%, respectively, which are 10.83%, 11.73%, 4.25%, and 12.00% higher than the prediction accuracy of the Kriging method. (c) During the daytime and nighttime, the RRMSEs of HAS method prediction were 9.23% and 11.17%, which were 5.92% and 11.99% higher than the prediction accuracy of the Kriging method, respectively. (d) Under the validation dataset, the average predictive RRMSE of the HAS method was 10.29%, and the average predictive RRMSE of the IRI prediction model was 12.35%, with a 2.06% improvement in the predictive accuracy of the HAS method. In general, the prediction effect of the HAS method was better than that of the Kriging method, thus verifying the effectiveness and reliability of the proposed method. In summary, the proposed reconstruction method is of great significance for improving usable frequency prediction and enhancing communication performance.

Eco-friendly chemometric analysis: Sustainable quantification of five pharmaceutical compounds in bulk, tablets, and spiked human plasma

The development of sustainable chemometric methodologies is crucial in pharmaceutical analysis to ensure accurate chemical quantification with little environmental effect. This study introduces novel chemometric techniques for the simultaneous measurement of Rabeprazole (RAB), Lansoprazole (LAN), Levofloxacin (LEV), Amoxicillin (AMO), and Paracetamol (PAR) in various samples, including lab-prepared mixtures, tablets, and spiked human plasma. Taguchi L25 (5^5) orthogonal array design to construct the calibration and validation sets was employed, the techniques used named Principal Component Regression (PCR), Partial Least Squares (PLS-2), Artificial Neural Networks (ANNs), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). The models were validated for their effectiveness in resolving complex spectra from overlapping components, the models achieving high correlation coefficients (R≥0.9997) and the relative error of prediction (REP) and root mean square error of prediction (RMSEP), were (0.2221 to 0.8022) and (0.0352 to 0.1767), respectively. The bias-corrected mean square error of prediction (BCMSEP) was (−0.00065 to 0.00166). The application of various assessment tools for evaluating greenness, blueness, and whiteness offered valuable insights into the environmental impact and sustainability of the developed methods compared to the reported ones. According to the Need Quality Sustainability (NQS) index, the developed methods align with sustainable analytical practices and support the United Nations Sustainable Development Goals (UN-SDGs).

Control and prediction of bedding-parallel fractures in fine-grained sedimentary rocks: A case from the Permian Lucaogou Formation in Jimusar Sag, Junggar Basin, Western China

The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration, enrichment, and efficient development of oil and gas. However, because of their variety and the complexity of the factors that affect them, their spatial prediction by the industrial community becomes challenging. Based on sample cores, thin sections, and well-logging and seismic data, this study employed a multi-scale data matching approach to quantitatively predict the development of bedding-parallel fractures and investigate their spatial distribution. Bedding-parallel fractures in the Lucaogou Formation in Jimusar Sag frequently occur along preexisting bedding planes and lithological interfaces. Unfilled bedding-parallel fractures inside or near source-rocks exhibit enhanced oil-bearing capacity. They were identified on micro-resistivity scanning images by the presence of regularly continuous black or nearly black sinusoidal curves. Overall, the developmental degree of bedding-parallel fractures was positively related to the brittle mineral and total organic carbon contents and negatively related to single reservoir interval thickness. The maintained porosity of the reservoir matrix contributed to a thorough response to factors affecting the development of bedding-parallel fractures. Here, an effective and objective method was proposed for predicting the development and distribution of bedding-parallel fractures in the fine-grained sedimentary rocks. The method was based on the matched reservoir interval density, reservoir interval density, and matched sweet spot density of bedding-parallel fractures. The prediction method integrated the significant advantages of high vertical resolution from logging curves and strong lateral continuity from seismic data. The average relative prediction error was 8% in the upper sweet spot in the Lucaogou Formation, indicating that the evaluation parameters for bedding-parallel fractures in fine-grained sedimentary rocks were reasonable and reliable and that the proposed prediction method has a stronger adaptability than the previously reported methods. The workflow based on multi-scale matching and stepwise progression can be applied in similar fine-grained sedimentary rocks, providing reliable technological support for the exploration and development of hydrocarbons.

A Spectral Detection Method Based on Integrated and Partition Modeling for Trace Copper in High-Concentration Zinc Solution.

In zinc smelting solution, because the concentration of zinc is too high, the spectral signals of trace copper are masked by the spectral signals of zinc, and their spectral signals overlap, which makes it difficult to detect the concentration of trace copper. To solve this problem, a spectrophotometric method based on integrated and partition modeling is proposed. Firstly, the derivative spectra based on continuous wavelet transform are used to preprocess the spectral signal and highlight the spectral peak of copper. Then, the interval partition modeling is used to select the optimal characteristic interval of copper according to the root mean square error of prediction, and the wavelength points of the absorbance matrix are selected by correlation-coefficient threshold to improve the sensitivity and linearity of copper ions. Finally, the partial least squares integrated modeling based on the Adaboost algorithm is established by using the selected wavelength to realize the concentration detection of trace copper in the zinc liquid. Comparing the proposed method with existing regression methods, the results showed that this method can not only reduce the complexity of wavelength screening, but can also ensure the stability of detection performance. The predicted root mean square error of copper was 0.0307, the correlation coefficient was 0.9978, and the average relative error of prediction was 3.14%, which effectively realized the detection of trace copper under the background of high-concentration zinc liquid.

Residual fatigue life prediction based on a novel improved Manson-Halford model considering loading interaction effect

The classical nonlinear fatigue cumulative damage model Manson-Halford is widely used in fatigue life analysis and prediction, but this model lacks consideration for the effects of load interaction. At present, some studies have proposed new correction models to overcome the shortcomings of classical models. However, the problem with these models is that the parameters are difficult to determine, resulting in a cumbersome application process or the correction process is based only on a single parameter. Insufficient correction leads to large fluctuations in life prediction errors. To overcome the above problems, this article proposes a new correction method and establishes a new improved model. Unlike existing models, the newly improved model is based solely on S-N curve parameters and does not require additional material parameters. It fully considers the relationship between adjacent loads and the difference in fatigue life under different stress states, and dynamically modifies the classical model based on larger influence weights. By conducting multi-level fatigue tests on 300M steel, a new improved model was used to accurately predict its remaining fatigue life. Compared with the classical model, the prediction error was reduced by 11.75 %. Utilized fatigue test data from various other materials to predict the fatigue life of the improved model under different levels of stress loading. The results indicate that in the vast majority of cases, the improved model has the highest prediction accuracy among all compared models, with a minimum relative prediction error of only 3.79 % and small fluctuations in prediction error. Compared with the classical model, the maximum reduction in relative prediction error after model improvement reached 28.73 %, indicating the effectiveness and accuracy of the new improved model.

A novel structural reliability analysis method combining the improved beluga whale optimization and the arctangent function‐based maximum entropy method

AbstractA novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability.

Oracle inequalities and upper bounds for kernel conditional U-statistics estimators on manifolds and more general metric spaces associated with operators

ABSTRACT U-statistics represent a fundamental class of statistics that emerge from modelling quantities of interest defined by multi-subject responses. These statistics generalize the empirical mean of a random variable X to summations encompassing all distinct k-tuples of observations drawn from X . A significant advancement was made by Stute [ConditionalU-statistics. Ann. Probab. 19 (1991), pp. 812–825], who introduced conditional U-statistics as a generalization of the Nadaraya–Watson estimates for regression functions. Stute demonstrated their robust pointwise consistency towards the conditional function r ( k ) ( φ , t ~ ) = E ( φ ( Y 1 , … , Y k ) ∣ ( X 1 , … , X k ) = t ~ ) , for t ~ ∈ R pk , where φ is a measurable function. In this investigation, we develop oracle inequalities and upper bounds for kernel-based estimators of conditional U-statistics of general order, applicable across a wide range of metric spaces associated with operators. Our analysis specifically targets doubling measure metric spaces, incorporating a non-negative self-adjoint operator characterized by Gaussian regularity in its heat kernel. Remarkably, our study achieves an optimal convergence rate in certain cases. To derive these results, we explore the regression function within a general framework, introducing several novel insights. These findings are established under sufficiently broad conditions on the underlying distributions. The theoretical results serve as essential tools for advancing the field of general-valued data, with potential applications including the examination of conditional distribution functions, relative-error prediction, the Kendall rank correlation coefficient, and discrimination problems – areas of significant independent interest.