Search

This study explores the potential of fluorescence spectroscopy (FS), coupled with principal component analysis (PCA) and partial least square regression (PLSR), to detect meat adulteration rapidly and non‐destructively in cooked minced beef. We aimed at evaluating FS as a simple and efficient tool for identifying cheaper meat species, that is chicken, used as adulterants in beef. Fluorescence spectra were collected at one fixed emission wavelength (410 nm) and three excitation wavelengths (290, 322, and 340 nm) from both pure and adulterated cooked meat samples. Adulteration levels ranging from 10% to 90% were assessed by mixing chicken meat with beef, followed by fluorescence analysis. The results indicated that the PCA model explained 100% of the variance, with 96% accounted for by the first principal component, showing clear discrimination between pure and adulterated samples. PLSR models demonstrated excellent predictive accuracy, with cross‐validated coefficients of determination of 0.95, highlighting FS's capability in distinguishing between pure and adulterated meats even after cooking. The cross‐validated grouping success rate was ~97%, reinforcing the reliability of the technique. This study represents the first investigation using FS to predict adulteration in cooked meat, providing a benchmark for future research. The findings suggest that FS, in combination with mathematical modeling, holds great promise as a rapid, cost‐effective, and nondestructive method for detecting meat adulteration, with significant potential for practical application in food industry quality control. [ABSTRACT FROM AUTHOR]

In this study, we propose a scenario superposition method for real‐time tsunami wave prediction. In the offline phase, prior to actual tsunami occurrence, hypothetical tsunami scenarios are created, and their wave data are decomposed into spatial modes and scenario‐specific coefficients by the singular value decomposition. Then, once an actual tsunami event is observed, the proposed method executes an online phase, which is a novel contribution of this study. Specifically, the predicted waveform is represented by a linear combination of training scenarios consisting of precomputed tsunami simulation results. To make such a prediction, a set of weight parameters that allow for appropriate scenario superposition is identified by the Bayesian update process. At the same time, the probability distribution of the weight parameters is obtained as reference information regarding the reliability of the prediction. Then, the waveforms are predicted by superposition with the estimated weight parameters multiplied by the waveforms of the corresponding scenarios. To validate the performance and benefits of the proposed method, a series of synthetic experiments are performed for the Shikoku coastal region of Japan with the subduction zone of the Nankai Trough. All tsunami data are derived from numerical simulations and divided into a training data set used as scenario superposition components and a test data set for an unknown real event. The predicted waveforms at the synthetic gauges closest to the Shikoku Islands are compared to those obtained using our previous prediction method incorporating sequential Bayesian updating. Plain Language Summary: In this study, we propose a new tsunami waveform prediction method that represents actual tsunami waveforms by superposing scenario waveforms resulting from a series of precomputed tsunami simulations. The prediction scheme uses a Bayesian approach to sequentially estimate the weight parameters assigned to the scenarios using actual real‐time wave height data. At the same time, it identifies the probability distribution of the weight parameters and provides information to understand the forecast error. The actual wave heights of the target event can be predicted by multiplying of the wave heights in the scenario database by the estimated weight parameters. To verify the advantages of the proposed method, a demonstration test is conducted for the Shikoku region of Japan, which is threatened by a large tsunami risk due to the Nankai Trough subduction zone. We show that the proposed method can predict wave heights even when the target event is not present in the scenario database, and we show its superiority over the previously proposed method. Key Points: The proposed scenario superposition method achieves real‐time tsunami waveform prediction via a linear combination of precomputed scenariosA set of parameters weighting each scenario is probabilistically estimated using a Bayesian approachThree‐hour tsunami waveforms can be predicted using wave heights measured at existing gauge locations in less than 10 min [ABSTRACT FROM AUTHOR]

Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction. [ABSTRACT FROM AUTHOR]

This study rigorously examines the impact of various data preprocessing techniques on the accuracy of machine learning models in predicting concrete's compressive strength. It develops ten regression models under nine distinct preprocessing scenarios, including normalization, standardization, principal component analysis (PCA), and polynomial features, utilizing a comprehensive dataset featuring normal and high-strength performances. The results reveal that using polynomial features and kernel PCA significantly enhanced model performance, with R values soaring to 93.27 and 94.65% during training and 88.51 and 88.77% during testing, respectively. This indicates their strong ability to capture the hidden nonlinear relationships within data. Conversely, discretization exhibited the least effectiveness, with the highest normalized root mean square error values of 14.2 (training) and 16.8 (testing) and normalized mean absolute error values of 11.6 (training) and 13.6 (testing), suggesting a potential loss of essential data granularity. Additionally, the study found that machine learning techniques generally surpassed traditional regression models, with higher R values being a consistent trend. These findings offer a nuanced understanding of the importance of preprocessing choice in concrete strength prediction and provide valuable insights for the concrete industry and data scientists, emphasizing the critical role of data preprocessing in achieving optimal model accuracy in materials science. [ABSTRACT FROM AUTHOR]

Slurry electrolysis (SE), as a hydrometallurgical process, has the characteristic of a multitank series connection, which leads to various stirring conditions and a complex solid suspension state. The computational fluid dynamics (CFD), which requires high computing resources, and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE tank. Through scientific selection of calculation samples via orthogonal experiments, a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each factor. Then, a prediction model of the SE tank was constructed using the K-nearest neighbor algorithm. The results show that with the increase in levels of orthogonal experiments, the prediction accuracy of the model improved remarkably. The model established with four factors and nine levels can accurately predict the flow and concentration fields, and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937, respectively. Compared with traditional CFD, the response time of field information prediction in this model was reduced from 75 h to 20 s, which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]

In this paper, a POD reduced-order interpolation model for solving the in situ pyrolysis process of tar-rich coal is employed to predict the flow and heat transfer performance in the porous media region so as to save computational resources and realize fast calculations. Numerical simulation using the finite volume method (FVM) is firstly used to obtain sample data, based on the samples through the primary function and spectral coefficients of the solutions. The physical field information and parameter distribution under different conditions of inlet temperature, inlet velocity and permeability are predicted. The results are compared with those of FVM to verify the accuracy of the calculated results. The relative mean deviation (RME) of the results of the POD prediction of each parameter for each working condition was synthesized to be no more than 5%. The performance of in situ pyrolysis of tar-rich coal is then investigated, and the oil and gas production are predicted. As the inlet velocity increases from 0.3 m/s to 0.9 m/s, the fraction of high-quality oil and gas production reaches 0.47 and then decreases to 0.38. Increasing the inlet temperature and permeability has a negative effect on the fraction of high-quality hydrocarbon production, after which the quality fraction of high-quality oil and gas dropped sharply to about 0.22. Porosity has a positive impact on the oil and gas production. When the porosity reaches 0.3, the quality fraction of high-quality oil and gas can reach 0.27. [ABSTRACT FROM AUTHOR]

Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform–near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky–Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with Rp = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with Rp = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with Rp = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with Rp = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves. [ABSTRACT FROM AUTHOR]

The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk® Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis. [ABSTRACT FROM AUTHOR]

A fast early warning algorithm for wind turbine blade icing based on a RNN model is proposed. Through wind turbine blade history data and labels as model input, the evaluation of raw m-dimension data through mean impact value (MIV) indices eliminates data with an MIV index of less than one; the remaining n-dimension data is reduced to x-dimension by the tSNE method; dimensional data is inputted into the RNN, and the model output is the icing state of the wind turbine blade in a certain future period. Based on the SCADA data from a wind field, the model was verified by an example. Using a certain example case, if the model training data is 104 orders of magnitude, using the MIV-tSNE-RNN algorithm, the prediction accuracy can reach approximately 72 %; compared with the RNN model, the prediction accuracy is improved by approximately 150 % while reducing the algorithm running time by approximately 45 %. If the amount of data exceeds 104 orders of magnitude, using the MIV-tSNE-RNN algorithm, the prediction accuracy is improved by approximately 100 %. This algorithm can provide accurate and rapid prediction results for wind turbine blade icing according to actual needs. [ABSTRACT FROM AUTHOR]

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management. [ABSTRACT FROM AUTHOR]

Many crossing hydraulic structures have been constructed in the Middle Route of the South-to-North Water Transfer Project (MR-SNWTP), which increases the likelihood of sudden water pollution accidents. After an accident, managers need to assess the extent of pollution under conditions of gate control, and it's necessary to make suitable emergency control decision under this assessment. Therefore, we researched the rapid prediction of pollutants behaviours under conditions of complicated gate control in this paper, by presenting three characteristic parameters of pollutant migration and diffusion. According to the simulation results, the influencing reasons and rapid prediction formulas for the characteristic parameters (peak transport distance, pollutant longitudinal length and peak concentration) after a sudden water-soluble pollution accident are proposed. Also, the approval results show that the formulas can accurately predict the location and range of the pollutant after the emergency accident. Finally, the rapid prediction formulas for the characteristic parameters played a fundamental role in the decisions involved in the Emergency Environmental Decision Support System is proved by two application examples. [ABSTRACT FROM AUTHOR]

The predictions of porosity and permeability from well logging data are important in oil and gas field development. Currently, many scholars use machine learning algorithms to predict reservoir properties. However, few scholars have researched the prediction of reservoir porosity and permeability while drilling. This approach requires not only a high prediction accuracy but also short model processing and calculation times as new logging data are incorporated while drilling. In this paper, four machine learning algorithms were evaluated: the one-versus-rest support vector machine (OVR SVM), one-versus-one support vector machine (OVO SVM), random forest (RF) and gradient boosting decision tree (GBDT) algorithms. First, samples of wireline logging data from the Yan969 wellblock of the Yan'an gas field were chosen for model training. To improve the accuracy and reduce the input parameter dimensions and model training time as much as possible, data correlation analysis was performed. Second, we used the grid search method to approximate ranges of reasonable parameter values and then used k-fold cross-validation to optimize the final parameters and avoid overfitting. Third, we used the four classification models to predict porosity and permeability while drilling with data from logging while drilling (LWD) logs. Finally, we indicate the best porosity and permeability prediction models to use while drilling. To ensure that the prediction accuracy is as high as possible and that the model training time is as short as possible, the OVO SVM algorithm was suggested for porosity and permeability prediction. Therefore, appropriate machine learning algorithms can be used to predict porosity and permeability while drilling. [ABSTRACT FROM AUTHOR]

During the prediction of the tool point frequency response function (FRF) based on receptance coupling substructure analysis (RCSA), in allusion to the problems that the existing tool model cannot simulate the contour of the tool precisely and the large amount of calculation for joint parameter identification during the calculation of the rotational FRF of the spindle, the joint model and the optimization algorithm affect the identification accuracy when acquiring the rotational FRF of the spindle. This study proposes a rapid prediction method for the tool point FRF on the basis of RCSA. The method divides the spindle-holder-tool system into two parts: the spindle-holder-partial shank and the remaining tool. On this basis, this study proposes a method of accurately establishing the model of the remaining tool with a 3D scanner and improves the calculation method of the rotational FRF of the spindle-holder-partial shank assembly through the reverse RCSA and finite difference method directly through the modal impact testing on the cutting tool. This study provides the experiments on a vertical machining center and compares the predicted FRF and the measured FRF on the tool point. The result indicates that the predicted FRF curve is consistent with the measured FRF curve with the relative error of natural frequency within 4.0%. This study confirms that the method is feasible, effective, and convenient. [ABSTRACT FROM AUTHOR]

Total volatile basic nitrogen (TVB-N) level rapid evaluation on chicken meat based on gas sensor array (GSA) technique was studied in this paper. GSA responses to chicken meat stored at 4°C were examined for 5 days. TVB-N content was synchronously measured by chemical examination. Principal component analysis (PCA) and non-linear double-layered cascaded serial stochastic resonance (DCSSR) were utilized for measurement data analysis. TVB-N examination results suggested that chicken meat stored for more than 3 days was not fresh. PCA showed poor discrimination abilities, while DCSSR signal-to-noise ratio (SNR) quantitatively characterized the freshness of all samples. Chicken meat TVB-N forecasting model was developed by non-linear fitting between SNR eigenvalues and TVB-N values. The predicting model was constructed. Validation experiment results demonstrated that the forecasting accuracy of the developed model reached 93.3%. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Water Science / Shuikexue Jinzhan is the property of Editorial Board of Advances in Water Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An approach based on the combined use of a 2D shallow water model and analytical 1D long wave run-up theory is proposed which facilitates the forecasting of tsunami run-up heights in a more rapid way, compared with the statistical or empirical run-up ratio method or resorting to complicated coastal inundation models. Its application is advantageous for long-term tsunami predictions based on the modeling of many prognostic tsunami scenarios. The modeling of the Chilean tsunami on February 27, 2010 has been performed, and the estimations of run-up heights are found to be in good agreement with available observations. [ABSTRACT FROM AUTHOR]

Rapid measurement of salmon flesh quality parameters (>400 samples day−1) was demonstrated in the laboratory and remotely at industrial sites. Visible-near infrared spectroscopy ( VNIRS) was applied to predict astaxanthin ( AX) and fat content in farmed Atlantic salmon. Fish were sampled from thirteen batches (1-6 kg whole weight, containing 2.3-16.3% fat and 1.2-12.5 μg g−1 AX), and models validated on small (average ± SD: 1.4 ± 0.4 kg) and large fish (4.2 ± 0.9 kg). Both constituents were well predicted in minced Norwegian Quality Cutlet ( NQC) samples (r2 ≥ 0.86; standard error of prediction ( SEP) ≤0.7% for fat and ≤0.7 μg g−1 for AX). Comparable metrics were observed for AX prediction in whole NQCs (r2 = 0.80-0.88; SEP 0.7 μg g−1). Fat was better predicted in small fish than large fish for whole NQCs (r2 = 0.82, SEP 1.0% cf r2 = 0.59, SEP = 0.59%) and non-destructive scanning through the skin of whole, gutted fish (r2 = 0.77, SEP = 1.2% cf r2 = 0.49, SEP = 1.5%). Models were also developed for screening polyunsaturated fatty acid ( PUFA) concentrations, e.g. in NQCs for docosahexaenoic acid (r2 = 0.73) and n-3:n-6 PUFA (r2 = 0.89). [ABSTRACT FROM AUTHOR]

The open circuit voltage of lithium ion batteries in equilibrium state, as a vital thermodynamic characteristic parameter, is extensively studied for battery state estimation and management. However, the time-consuming relaxation process, usually for several hours or more, seriously hinders the widespread application of open circuit voltage. In this paper, a novel voltage relaxation model is proposed to predict the final open circuit voltage when the lithium ion batteries are in equilibrium state with a small amount of sample data in the first few minutes, based on the concentration polarization theory. The Nernst equation is introduced to describe the evolution of relaxation voltage. The accuracy and effectiveness of the model are verified using experimental data on lithium ion batteries with different kinds of electrodes (LiCoO2/mesocarbon-microbead and LiFePO4/graphite) under different working conditions. The validation results show that the presented model can fit the experimental results very well and the predicted values are quite accurate by taking only 5 min or less. The satisfying results suggest that the introduction of concentration polarization theory might provide researchers an alternative model form to establish voltage relaxation models. [ABSTRACT FROM AUTHOR]