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Grey Fourier model has been successfully applied in seasonal time series forecasting, but its performance in handling nonlinear seasonal time series may still require further improvement. To describe the nonlinear characteristics, a flexible time power grey Fourier model (TPGFM(1,1,N,r)) is proposed by introducing nonlinear time power terms to the grey action of grey Fourier model. The hyperparameters, the truncated Fourier order N and time power r are initially selected by the Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal parameters are determined by the hold-out method. To further improve the prediction accuracy for nonlinear time sequences, combination models based on the proposed grey model, statistical models and artificial intelligence models are designed. The variable weights are assigned by the inverse variance weighting method. Afterward, the results of the designed experiments based on numerical experiment verify the validity of the Fourier order and time power selection, illustrating the superior performances over benchmark models. Finally, the proposed model is applied for monthly PM2.5 forecasting and quarterly wind power generation forecasting, outperforming other benchmark models in prediction, including seasonal grey models, artificial intelligence models and statistical models. Moreover, the combination models, developed based on TPGFM(1,1,N,r) model, have achieved higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop an enhanced model for predicting pork freshness by integrating hyperspectral imaging (HSI) and chemometric analysis Methods: A total of 30 Longissimus thoracis samples from three sows were stored under vacuum conditions at 4°C±2°C for 27 days to acquire data. The freshness prediction model for pork loin employed partial least squares regression (PLSR) with Monte Carlo data augmentation. Total bacterial count (TBC) and volatile basic nitrogen (VBN), which exhibited increases correlating with metabolite changes during storage, were designated as freshness indicators. Metabolic contents of the sample were quantified using nuclear magnetic resonance. Results: A total of 64 metabolites were identified, with 34 and 35 showing high correlations with TBC and VBN, respectively. Lysine and malate for TBC (R² = 0.886) and methionine and niacinamide for VBN (R² = 0.909) were identified as the main metabolites in each indicator by Model 1. Model 2 predicted main metabolites using HSI spectral data. Model 3, which predicted freshness indicators with HSI spectral data, demonstrated high prediction coefficients; TBC R²p = 0.7220 and VBN R²p = 0.8392. Furthermore, the combination model (Model 4), utilizing HSI spectral data and predicted metabolites from Model 2 to predict freshness indicators, improved the prediction coefficients compared to Model 3; TBC R²p = 0.7583 and VBN R²p = 0.8441. Conclusion: Combining HSI spectral data with metabolites correlated to the meat freshness may elucidate why certain HSI spectra indicate meat freshness and prove to be more effective in predicting the freshness state of pork loin compared to using only HSI spectral data. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering 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 intermittency and randomness of photovoltaic power present different characteristics due to seasonal variations, so it is important to consider seasonal characteristics to improve the accuracy of photovoltaic power prediction. Therefore, a short-term photovoltaic power prediction combination model considering seasonal characteristic and data window is proposed in the paper. Firstly, the Pearson correlation coefficient method is adopted to determine suitable meteorological factors with high contribution to photovoltaic power and reduce the input feature dimensions of the prediction model. Secondly, the prediction error of different photovoltaic power models is compared, and the two models with the lowest photovoltaic power prediction error and the lowest correlation are selected to construct the combination model, i.e., gated recurrent unit (GRU) model and extreme gradient boosting (XGboost) model. Thirdly, the effects of different input windows in the historical meteorological data on the prediction accuracy of GRU-XGboost model are analyzed to determine the optimal data window. Finally, on this basis, GRU and XGboost predict the photovoltaic power respectively. The final prediction is obtained by weighted combination of the two predictions. Simulation results show that the proposed model has stronger adaptability and higher prediction accuracy than other models.

Abstract The intermittency and randomness of photovoltaic power present different characteristics due to seasonal variations, which greatly affects the reliability of power supply. To boost the prediction accuracy of photovoltaic power, a short-term prediction combination model named GSK–BiGRU–XGboost is proposed. First, the Pearson correlation coefficient is adopted to determine highly-correlated meteorological factors to photovoltaic power to construct the input features. Second, the prediction errors of different single models are compared, and the two, i.e., Bidirectional Gated Recurrent Unit (BiGRU) and Extreme Gradient Boosting (XGboost) that have the smallest errors and lowest correlation are selected to construct the combination model. Third, to achieve an appropriate weight coefficient of the model, an improved gaining sharing knowledge-based algorithm (GSK) based on parameter adaption is designed to optimize it effectively. Fourth, seasonal models and year-round model based on GSK–BiGRU–XGboost are compared to reveal the effect of seasonal characteristics. Finally, the influence of historical meteorological data window with different steps is investigated. To verify the performance of GSK–BiGRU–XGboost, it is compared with different single and combination models under different weather conditions. GSK–BiGRU–XGboost achieves a high prediction accuracy of 97.85%, which is 9.46% and 12.43% higher than its member models, respectively. Besides, GSK can lead to a 1.71% improvement in the accuracy.

Abstract Background Extubation failure is a crucial issue for acute respiratory distress syndrome (ARDS). Ultrasound of the lung and diaphragm is individually valuable for predicting extubation outcomes. We aimed to determine whether combined lung and diaphragmatic ultrasound could improve the accuracy of predicting the extubation of ARDS patients. Methods This was a prospective cohort study of ARDS patients who were ready for extubation. The lung ultrasound score (LUS), diaphragmatic displacement (DD), diaphragm thickening fraction (DTF), and diaphragmatic-rapid shallow breathing index (D-RSBI) were measured at the end of the spontaneous breathing trial. The primary outcome was extubation success. Logistic regression was used to combine these indicators, and the predictive performance of the single and combined indicators was evaluated through receiver operating characteristic (ROC) curves, the Hosmer–Lemeshow Ĉ‐test, and the Brier score. Multivariate logistic regression was used to determine the association between combined ultrasound indicators and extubation success. Results This study enrolled 132 eligible patients from January 2019 to December 2022. A total of 71% (94/132) of patients were successfully extubated from mechanical ventilation. The combination of LUS and D-RSBI had the largest area under the ROC curves, the lowest Brier score, and the greatest calibration. After formula transformation, LUS + 2.43 × D-RSBI ≤ 14.273 was significantly associated with extubation success in ARDS patients. Conclusions In ARDS patients receiving mechanical ventilation, the combination of LUS and D-RSBI was more accurate than a single parameter alone in predicting extubation outcomes. This combined approach could help refine extubation protocols in critical care. Clinical trial registration This study is registered online with the Chinese Clinical Trial Registry (ChiCTR), http://www.chictr.org.cn , ChiCTR1800019340 (Registration time: 2018/11/06).

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office 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 intermittency and randomness of photovoltaic power present different characteristics due to seasonal variations, which greatly affects the reliability of power supply. To boost the prediction accuracy of photovoltaic power, a short-term prediction combination model named GSK–BiGRU–XGboost is proposed. First, the Pearson correlation coefficient is adopted to determine highly-correlated meteorological factors to photovoltaic power to construct the input features. Second, the prediction errors of different single models are compared, and the two, i.e., Bidirectional Gated Recurrent Unit (BiGRU) and Extreme Gradient Boosting (XGboost) that have the smallest errors and lowest correlation are selected to construct the combination model. Third, to achieve an appropriate weight coefficient of the model, an improved gaining sharing knowledge-based algorithm (GSK) based on parameter adaption is designed to optimize it effectively. Fourth, seasonal models and year-round model based on GSK–BiGRU–XGboost are compared to reveal the effect of seasonal characteristics. Finally, the influence of historical meteorological data window with different steps is investigated. To verify the performance of GSK–BiGRU–XGboost, it is compared with different single and combination models under different weather conditions. GSK–BiGRU–XGboost achieves a high prediction accuracy of 97.85%, which is 9.46% and 12.43% higher than its member models, respectively. Besides, GSK can lead to a 1.71% improvement in the accuracy. [ABSTRACT FROM AUTHOR]

Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers' checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers. [ABSTRACT FROM AUTHOR]

Background: Extubation failure is a crucial issue for acute respiratory distress syndrome (ARDS). Ultrasound of the lung and diaphragm is individually valuable for predicting extubation outcomes. We aimed to determine whether combined lung and diaphragmatic ultrasound could improve the accuracy of predicting the extubation of ARDS patients. Methods: This was a prospective cohort study of ARDS patients who were ready for extubation. The lung ultrasound score (LUS), diaphragmatic displacement (DD), diaphragm thickening fraction (DTF), and diaphragmatic-rapid shallow breathing index (D-RSBI) were measured at the end of the spontaneous breathing trial. The primary outcome was extubation success. Logistic regression was used to combine these indicators, and the predictive performance of the single and combined indicators was evaluated through receiver operating characteristic (ROC) curves, the Hosmer–Lemeshow Ĉ‐test, and the Brier score. Multivariate logistic regression was used to determine the association between combined ultrasound indicators and extubation success. Results: This study enrolled 132 eligible patients from January 2019 to December 2022. A total of 71% (94/132) of patients were successfully extubated from mechanical ventilation. The combination of LUS and D-RSBI had the largest area under the ROC curves, the lowest Brier score, and the greatest calibration. After formula transformation, LUS + 2.43 × D-RSBI ≤ 14.273 was significantly associated with extubation success in ARDS patients. Conclusions: In ARDS patients receiving mechanical ventilation, the combination of LUS and D-RSBI was more accurate than a single parameter alone in predicting extubation outcomes. This combined approach could help refine extubation protocols in critical care. Clinical trial registration This study is registered online with the Chinese Clinical Trial Registry (ChiCTR), http://www.chictr.org.cn, ChiCTR1800019340 (Registration time: 2018/11/06). [ABSTRACT FROM AUTHOR]

The continuous improvement of bridge construction technology has resulted in an ongoing expansion of bridge spans, which has concomitantly increased the difficulty of controlling the alignment of long-span bridges during construction. In order to address the issue of the grey prediction model exhibiting a significant discrepancy in its alignment predictions for long-span continuous girder bridges, a pre-camber prediction method for bridges based on a combination of the grey model (GM) and BP neural network (GM-BP) is proposed. Firstly, the parameters are identified according to their influence on the pre-camber, and the appropriate parameters are selected as the original data to improve the efficiency of prediction. Subsequently, the original data are preliminarily fitted by the GM(1,1) model, and the predicted values are used as inputs for training the neural network. Finally, the new predicted values are output using the nonlinear fitting ability of the BP neural network. To assess the efficacy of the model, it is applied to the prediction of the pre-camber of the girder segments of a bridge under cantilever construction. The pre-camber prediction for 11#–13# girder sections was based on 10 sets of monitoring data from constructed girder sections. The results demonstrated that the average relative error of the GM-BP combined prediction model was 3.01%, which was 5.68% less than that of the GM(1,1) model, and the overall prediction exhibited a closer alignment with the original data. The GM-BP combined prediction model is an effective method for ensuring the alignment control of bridge construction and is able to achieve high accuracy and stability in its predictions in the case of limited and irregular data. [ABSTRACT FROM AUTHOR]

In recent years, the prediction of stocks has mainly focused on improving and combining stock prediction algorithms, or analyzing news sentiment tendencies to simulate subjective investor consciousness. However, both methods have shortcomings in practicality and comprehensiveness. Therefore, based on the use of stock data, the sentiment propensity of vocabulary in the article was processed, and a new algorithm model was obtained by combining the differential integration moving average autoregressive model and backpropagation feedforward neural network model. Finally, sentiment propensity was integrated into the combination model to obtain an algorithm model that integrates sentiment analysis. After optimizing the sentiment vocabulary of news. The algorithm has improved its ability to recognize emotional tendency words, while traditional algorithms have been improved to improve the accuracy of stock prediction, further verifying the relationship curve between emotional tendency and stock prediction fluctuations. The experimental results show that the combined model of sentiment analysis is close to the true value in predicting stock results, with an error of less than 1.5%. The accuracy and stability of the model's prediction results are significantly better than the uncombined model and traditional prediction models. The new combination model provides better judgment basis for investors through experimental prediction results, creating conditions for investors to avoid stock market risks and improve investment value. [ABSTRACT FROM AUTHOR]

The current research aims to investigate the power of financial distress prediction models while presenting a combination model, comparing the extracted model with the Merton model and the binary logistic regression model in predicting financial distress. In order to achieve the purpose of the research, the information of 168 distressed companies selected based on the specific criteria of distress and 168 healthy companies admitted to the Tehran Stock Exchange between 2006 and 2019 have been used. After reviewing past studies, 25 variables affecting financial distress, including 17 accounting variables, 4 market variables, and 4 macroeconomic variables, were identified, and by emphasizing the frequency and successful performance of these ratios in past studies and performing statistical tests, the final indicators were selected. To determine the dependent variable, Merton's model was used, and finally, by applying the logit model and determining the relationship between the independent variables and the dependent variable, a composite model was extracted. The research results showed that adding economic and stock market variables to financial variables does not increase the ability to predict financial distress and the combined model has better explanatory power than the Merton model and binary logistic regression. In the present research, to predict financial distress, all three categories of accounting, economic and stock market variables are considered together, and the emphasis is not only on accounting variables, and the combined model is compared with the accounting and market model. [ABSTRACT FROM AUTHOR]

Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance its development has become increasingly necessary to enable efficient detection and response to a wide range of movements. Current recognition methods rely on calculating changes in joint distance to classify activity patterns. Therefore, a different approach is required to identify the direction of movement to distinguish activities exhibiting similar joint distance changes but differing motion directions, such as sitting and standing. The research conducted in this study focused on determining the direction of movement using an innovative joint angle shift approach. By analyzing the joint angle shift value between specific joints and reference points in the sequence of activity frames, the research enabled the detection of variations in activity direction. The joint angle shift method was combined with a Deep Convolutional Neural Network (DCNN) model to classify 3D datasets encompassing spatial-temporal information from RGB-D video image data. Model performance was evaluated using the confusion matrix. The results show that the model successfully classified nine activities in the Florence 3D Actions dataset, including sitting and standing, obtaining an accuracy of (96.72 ± 0.83)%. In addition, to evaluate its robustness, this model was tested on the UTKinect Action3D dataset, obtaining an accuracy of 97.44%, proving that state-of-the-art performance has been achieved.

Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers’ checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers.

In recent years, under the influence of changes in natural conditions and human social activities, the issue of global warming has become increasingly prominent. So it is crucial to effectively predict the future trend of temperature changes. In this regard, from the perspective of statistical models, this paper studies a new combination model, namely the new GM-ARIMA model, based on linear combination of weight calculation. Furthermore, it also analyzes the prediction effect through comparative experiments and uses multiple performance evaluation indicators, so as to prove the scientificity and effectiveness of the proposed combination model in this paper. Finally, according to the experimental results, it can be clearly found that among the four methods for calculating the weights of linear combination, namely the equal weight method, the variance reciprocal method, the residual reciprocal method and the standard deviation method, the combination model using the standard deviation method for calculation has the highest prediction accuracy, so it is finally decided to use this method to build the combination model (namely S-GM-ARIMA). In addition, the experimental results show that the S-GM-ARIMA model achieves the best prediction results compared to other existing prediction models. Among them, the MAE of S-GM-ARIMA decreases by 10.38% and 16.22% compared to the GM(1,1) model and ARIMA model, respectively. The RMSE of S-GM-ARIMA decreases by 4.52% and 10.03% compared to the GM(1,1) model and ARIMA model, respectively. And the MAPE of S-GM-ARIMA decreases by 10.34% and 16.17% compared to the GM(1,1) model and ARIMA model, respectively. Therefore, the new GM-ARIMA combination model studied in this paper has relatively higher prediction accuracy when making predictions, and can be used to make more effective and accurate predictions of global average temperature. This study can also provide reference for countries in making decisions to address global warming issues. [ABSTRACT FROM AUTHOR]

A groundbreaking method is proposed to mitigate the impact of unpredictable fluctuations in wind velocity on wind power generation. This innovative approach integrates the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) and XGBoost models in a harmonious manner. Initially, the raw wind speed data is subjected to wavelet threshold denoising to reduce noise and volatility. For short-term wind speed prediction, a PSO-LSSVM-XGBoost model is introduced. After the initial wind speed sequence undergoes wavelet threshold denoising, the enhanced sequence is forecasted using the LSSVM model, with its hyperparameters optimized through the PSO algorithm. The errors, obtained by subtracting the predicted values from the original data, are compensated using XGBoost. The final forecast results combine the rectified error data with the initial projected results. Experimental findings demonstrate the model's remarkable capability to enhance prediction performance and accuracy. [ABSTRACT FROM AUTHOR]

Transportation is not only a significant force in promoting economic and social development but also one of the primary industries that consume energy and emit greenhouse gas emissions. In order to achieve China's overall goal of reaching the carbon peak by 2030, this paper selects six influencing factors, such as population, GDP and urbanization rate, and proposes a combined prediction model based on ARIMA-LSTM-BP, which predicts transportation carbon emissions in China from 2022 to 2050 under three scenarios of low carbon, benchmark and high carbon. The results show that the peak emissions of transportation in low-carbon, benchmark and high-carbon scenarios are 1624.7732 million tons, 1478.1694 million tons and 1367.5417 million tons, respectively, reaching the peak in 2031, 2034 and 2039. It can be seen that in China, the transportation industry alone cannot achieve the goal of reaching the peak by 2030, and more measures need to be taken to achieve the carbon peak of the transportation industry as soon as possible. [ABSTRACT FROM AUTHOR]

Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance its development has become increasingly necessary to enable efficient detection and response to a wide range of movements. Current recognition methods rely on calculating changes in joint distance to classify activity patterns. Therefore, a different approach is required to identify the direction of movement to distinguish activities exhibiting similar joint distance changes but differing motion directions, such as sitting and standing. The research conducted in this study focused on determining the direction of movement using an innovative joint angle shift approach. By analyzing the joint angle shift value between specific joints and reference points in the sequence of activity frames, the research enabled the detection of variations in activity direction. The joint angle shift method was combined with a Deep Convolutional Neural Network (DCNN) model to classify 3D datasets encompassing spatial-temporal information from RGB-D video image data. Model performance was evaluated using the confusion matrix. The results show that the model successfully classified nine activities in the Florence 3D Actions dataset, including sitting and standing, obtaining an accuracy of (96.72 ± 0.83)%. In addition, to evaluate its robustness, this model was tested on the UTKinect Action3D dataset, obtaining an accuracy of 97.44%, proving that state-of-the-art performance has been achieved. [ABSTRACT FROM AUTHOR]

The continuous improvement of bridge construction technology has resulted in an ongoing expansion of bridge spans, which has concomitantly increased the difficulty of controlling the alignment of long-span bridges during construction. In order to address the issue of the grey prediction model exhibiting a significant discrepancy in its alignment predictions for long-span continuous girder bridges, a pre-camber prediction method for bridges based on a combination of the grey model (GM) and BP neural network (GM-BP) is proposed. Firstly, the parameters are identified according to their influence on the pre-camber, and the appropriate parameters are selected as the original data to improve the efficiency of prediction. Subsequently, the original data are preliminarily fitted by the GM(1,1) model, and the predicted values are used as inputs for training the neural network. Finally, the new predicted values are output using the nonlinear fitting ability of the BP neural network. To assess the efficacy of the model, it is applied to the prediction of the pre-camber of the girder segments of a bridge under cantilever construction. The pre-camber prediction for 11#–13# girder sections was based on 10 sets of monitoring data from constructed girder sections. The results demonstrated that the average relative error of the GM-BP combined prediction model was 3.01%, which was 5.68% less than that of the GM(1,1) model, and the overall prediction exhibited a closer alignment with the original data. The GM-BP combined prediction model is an effective method for ensuring the alignment control of bridge construction and is able to achieve high accuracy and stability in its predictions in the case of limited and irregular data.

The frequent fluctuation of pork prices has seriously affected the sustainable development of the pork industry. The accurate prediction of pork prices can not only help pork practitioners make scientific decisions but also help them to avoid market risks, which is the only way to promote the healthy development of the pork industry. Therefore, to improve the prediction accuracy of pork prices, this paper first combines the Sparrow Search Algorithm (SSA) and traditional machine learning model, Classification and Regression Trees (CART), to establish an SSA-CART optimization model for predicting pork prices. Secondly, based on the Sichuan pork price data during the 12th Five-Year Plan period, the linear correlation between piglet, corn, fattening pig feed, and pork price was measured using the Pearson correlation coefficient. Thirdly, the MAE fitness value was calculated by combining the validation set and training set, and the hyperparameter "MinLeafSize" was optimized via the SSA. Finally, a comparative analysis of the prediction performance of the White Shark Optimizer (WSO)-CART model, CART model, and Simulated Annealing (SA)-CART model demonstrated that the SSA-CART model has the best prediction of pork price (compared with a single decision tree, R2 increased by 9.236%), which is conducive to providing support for pork price prediction. The accurate prediction of pork prices with an optimized machine learning model is of great practical significance for stabilizing pig production, ensuring the sustainable growth of farmers' income, and promoting sound economic development. [ABSTRACT FROM AUTHOR]

Abstract Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability, and the development of the artificial intelligence industry. To provide investors with a more reliable reference in terms of artificial intelligence index investment, this paper selects the NASDAQ CTA Artificial Intelligence and Robotics (AIRO) Index as the research target, and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics. Specifically, this paper uses the ensemble empirical mode decomposition (EEMD) method and the modified iterative cumulative sum of squares (ICSS) algorithm to decompose the index returns and identify the structural breakpoints. Furthermore, it combines the least-square support vector machine approach with the particle swarm optimization method (PSO-LSSVM) and the generalized autoregressive conditional heteroskedasticity (GARCH) type models to construct innovative hybrid forecasting methods. On the one hand, the empirical results indicate that the AIRO index returns have complex structural characteristics, and present time-varying and nonlinear characteristics with high complexity and mutability; on the other hand, the newly proposed hybrid forecasting method (i.e., the EEMD-PSO-LSSVM-ICSS-GARCH models) which considers these complex structural characteristics, can yield the optimal forecasting performance for the AIRO index returns.

Pingshan County, Hebei was affected by topography, geological structure, ecological environment and other factors, geological disasters such as landslides occurred frequently. Nine evaluation factors including topographic relief, slope, aspect, river network density, fault zone density, stratigraphic lithology, NDVI, land use type and geological disaster point density were selected. The weights of each evaluation factor were calculated by AHP and catastrophe theory, and the combination model of AHP and catastrophe theory was established and applied according to the minimum information entropy weight method. The results of geological disaster risk assessment in Pingshan County based on three methods were compared. The results show that the evaluation results of the combined model have higher accuracy and are in line with the development characteristics of geological disasters in this area. Combined model method combines subjective and objective, considering the influence of factors, the evaluation results are reliable. This study provides a new attempt and method for geological disaster risk assessment in Pingshan County and similar areas.

The characteristics of hydrological data include nonconsistency and nonlinearity. The prediction accuracy can be improved through the combination of both the decomposition algorithm and the runoff model. Previous studies have typically focused on the combination of a single decomposition algorithm and model. These studies have compared the prediction accuracy before and after decomposition, ignoring the role of multiple decomposition algorithms and models. Considering the limitations of previous single combinations of decomposition algorithms and models, this study will explore the unique features of hydrological data by using a combination of five algorithms, including Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), TIME series decomposition (TIME), Variational Mode Decomposition (VMD), and Singular Spectrum Analysis (SSA). The study constructed models for Prophet, Long Short-Term Memory (LSTM), Multiple Regression (MLR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR). Thirty combined prediction models were then developed and used to forecast medium and long-term runoff at Xianyang Station. To comprehensively evaluate the forecasted runoff results, multiple evaluation metrics were used. The prediction accuracy improved after using EMD and TIME decomposition, but the difference was insignificant, and TIME decomposition was the least effective. VMD, EEMD, and SSA, on the other hand, yielded higher data quality. The combined model achieved an NSE above 0.70, demonstrating good prediction results. Of the thirty combined models, the SSA-SVR and SSA-LSTM models were most accurate, with a verification NSE of 0.90. This study developed a comprehensive, reliable, and accurate combination prediction model by employing multiple decomposition algorithms and models. These findings provide a framework for characteristics-driven watershed runoff prediction and water resources scheduling.

In intensive aquaculture systems, accurate water temperature prediction is crucial for aquaculture efficiency. Traditional prediction models often have limitations in dealing with strongly coupled, nonlinear, and time-varying water temperature data. A novel hybrid model for temperature prediction is proposed to improve prediction generalization ability and robustness. The model integrates advanced data processing and prediction techniques. Firstly, the VMD method is utilized to achieve effective data decomposition and noise reduction. Secondly, the CNN algorithm is applied to achieve feature extraction of the data. Finally, the bi-directional LSTM and self-concerned combination are used to obtain the final prediction results. The experimental results show that the MAE, RMSE, MSE, MAPE, and R2 of the VMD-CNN-BILSTM-SA combination prediction model proposed in this paper are 0.016, 0.143, 0.020, 0.035, and 0.978, respectively. Compared with other deep learning models, the BiLSTM model presented in this paper improves the R2 by 13.2% compared with LSTM and 13.7% over the GRU model. This study can be applied in fishery farming, which can reduce the risk of farming and promote the modernization of fishery.

In order to improve the prediction accuracy and generalization ability of railway freight volume, considering the linear and nonlinear characteristics of railway freight volume time series, a railway freight volume prediction method based on ARIMA-LSTM-XGBoost combined model was proposed. Firstly, autoregressive integrated moving average(ARIMA) model was used to preliminarily predict China's railway freight volume, and then long short-term memory(LSTM) network was used to correct the residuals. Secondly, by combining with extreme gradient boosting(XGBoost) model, a weighted combination model with the weight determined by error reciprocal method was constructed. Finally, the combination model was compared with ARIMA, ARIMA-LSTM, LSTM and XGBoost models, and the prediction accuracy of the above models were analyzed by means of mean square error(MSE), root mean square error (RMSE), mean absolute error(MAE) and mean absolute percentage error(MAPE). Based on the experiment of the monthly data of China's railway freight volume from 2007 to 2021, the results show that the MSE, RMSE, MAE and MAPE of the combined model are 0. 011 9, 0. 109 4, 0. 068 3 and 1. 775 2% respectively. The prediction error is lower than the above comparison models, and the prediction accuracy and generalization ability of the model are improved. [ABSTRACT FROM AUTHOR]

As the basis for maintaining the operation and analysis of the power grid system, short-term power load forecasting provides judgment basis and information for the economic dispatch and safety analysis of the power grid system, and plays an important role in maintaining the normal operation of the power grid system. In this study, the FA(Firefly Algorithm) is used to optimize the penalty factor c, nuclear parameter g of SVR(Support Vector Regression) model and the number of neurons m and learning rate lr of LSTM(Long Short-Term Memory) model. The FA-SVR-LSTM combined prediction model is established using the optimal parameters, and the sample data are predicted. Taking the historical data of power load of Florida as an example, four reference models of LSTM, SVR, FA-SVR and FA-LSTM are established to predict the power load of 360 h in 15 days, and the results are compared with those of FA-SVR-LSTM. The experimental results show that compared with LSTM and SVR model, the prediction accuracy of FA-SVR-LSTM model is improved by 33.184 9% and 30.326 5%, respectively. The evaluation values of MAPE and RMSE are significantly lower than those of the other four models. These results indicate that the prediction effect of FA-SVR-LSTM combined model is significantly improved when compared with other models. [ABSTRACT FROM AUTHOR]

With the development of economy, the urbanization process is accelerated and the infrastructure construction is increased, which leads to the widespread occurrence of landslides in mountain areas all over the world. However, due to the complex geological environment or some other reasons, the lack of landslide-related data in some mountainous areas makes it more difficult to predict landslides. At the same time, the existing models have different prediction effects in different regions, and it is difficult for a single model to objectively and accurately evaluate landslide hazard. The purpose of this research is to complete the landslide hazard assessment (LHA) in data-deficient areas by proposed a combination model with help of remote sensing (RS) and geographic information system (GIS) technology. Firstly, 146 landslides and 10 LHA conditioning factors in Tumen City were obtained by using RS, GIS and field investigation. To increase the amount of model training data, 386 landslides (including 146 landslides in Tumen City) in some areas of Yanbian Korean Autonomous Prefecture with similar landslide conditions to Tumen City were obtained. Secondly, three combination models for LHA are proposed, which make full use of the effective information provided by logistic regression (LR), artificial neural network (ANN) and support vector machine (SVM), and the evaluation effect and applicability of the three combination models are discussed. Finally, the three combination models and three single models of logistic regression (LR), artificial neural network (ANN), support vector machine (SVM) are analyzed and compared through the overall accuracy (OA), confusion matrix and landslide density. The results show that it can effectively complete the landslide hazard assessment in data-deficient areas with help of RS and GIS, and the three combination models proposed in this research are superior to the other three single models, and the evaluation effect of the LA-SVM combination model is the best. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office 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.)

Deep excavation engineering is a comprehensive discipline that involves multiple fields such as engineering geology, hydrogeology, and foundation engineering. With the improvement of the utilization rate of underground space, the demand for the construction of large-scale underground structural engineering is growing, making the excavation of underground soil become increasingly frequent, which also brings about the safety problems of deep foundation pit engineering and the surrounding environment. Prediction of foundation pit deformation is an important research direction with diverse historical developments, but it is also facing a series of difficulties and challenges. In order to solve these problems, this article proposes an improvement plan, establishes a prediction model based on the combination model of grey BP (back propagation) neural network, and verifies its effectiveness through experiments. The results show that the average error of the new model's prediction of horizontal deformation is about 0.31, which is about 32% lower than the traditional model's prediction error. The difference between the vertical deformation prediction and actual monitoring results is also controlled. The vertical deformation predicted by wavelet transform is 7% to 9% larger than the actual monitoring results, meeting the prediction requirements. Finally, this article explores the research on the prediction of foundation pit deformation in deep excavation engineering, An improved grey BP neural network combination model was proposed and its effectiveness was verified through experiments. This article has important reference value for the study of deformation prediction in deep excavation engineering. [ABSTRACT FROM AUTHOR]

Accurate implementation of short-term wind speed prediction can not only improve the efficiency of wind power generation, but also relieve the pressure on the power system and improve the stability of the grid. As is known to all, the existing wind speed prediction systems can improve the performance of the prediction in some sense, but at the same time they have some inherent shortcomings, just like forecasting accuracy is not high or indicators are difficult to obtain. In this paper, based on 10-min wind speed data from a wind farm, a new combination model is developed, which consists of three parts: data noise reduction techniques, five artificial single-model prediction algorithms, and multi-objective optimization algorithms. Through detailed and complete experiments and tests, the results demonstrate that the combination model has better performance than other models, solving the problem of instability of traditional forecasting models and filling the gap of low-prediction short-term wind speed forecasting. [ABSTRACT FROM AUTHOR]

The traditional combination model based on multinomial logit is incapable of reflecting the nested structure of urban tourism destinations. Therefore, this paper established a variational inequality model of the combination of tourism demand distribution and transportation assignment based on nested logit. Through the first-order optimization conditions, it proved that the volume of travel distribution and transport assignment could meet the combination equilibrium conditions. Based on the MSA algorithm, it designed the solution algorithm of the model and verified the feasibility of the model and algorithm in a simplified tourism passenger transport network in Huangshan City. The calculation results show that the variational inequality model proposed in this paper can obtain the volume of travel distribution and transportation assignment at the same time, meanwhile compared with the multinomial logit, the nested logit structure fully considers the attraction measure of hotel and scenic spots, which is more in line with the choice behavior of tourists when choosing transportation route. In addition, the time value type of tourists has a great impact on the comprehensive tourism impedance which means that in the practical application, it is necessary to determine the time value type of tourists in each transportation routinereasonably.

The accurate prediction of the trend of natural gas production changes plays an important role in the formulation of development planning plans. The conventional gas exploration and development in Sichuan Basin has a long history. Based on the development of conventional natural gas production, the article uses the Hubbert model, Gauss model, and GM (1, N) model to predict conventional natural gas production, and then the Shapley value method is used to allocate the weight values of the three models, and a combination model for conventional gas production prediction is established. Finally, residual analysis and precision test are carried out on the prediction results. The results show that: 1) The combination model established using the Shapley value method can effectively combine the advantages of various models and improve the accuracy of prediction. And the standardized residual of the combined model is the lowest, the prediction is closest to the actual value, and the accuracy test is the best, indicating that the combined model has the highest accuracy. 2) After using a combination model for prediction, conventional gas production will peak in 2046, with a peak production of 412 × 108 m3, with a stable production period of (2038–2054) years, a stable production period of 17 years, and a stable production period of 389 × 108 m3, the predicted results of the combined model have a longer stable production period, and the trend of production changes is more stable. The use of combination model provides a reference for the field of natural gas prediction, while improving the accuracy of prediction results and providing better guidance for production planning.

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of 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 accurate forecasting of short-term subway passenger flow is beneficial for promoting operational efficiency and passenger satisfaction. However, the nonlinearity and nonstationarity of passenger flow time series bring challenges to short-term passenger flow prediction. To solve this challenge, a prediction model based on improved variational mode decomposition (IVMD) and multi-model combination is proposed. Firstly, the mixed-strategy improved sparrow search algorithm (MSSA) is used to adaptively determine the parameters of the VMD with envelope entropy as the fitness value. Then, IVMD is applied to decompose the original passenger flow time series into several sub-series adaptively. Meanwhile, the sample entropy is utilized to divide the sub-series into high-frequency and low-frequency components, and different models are established to predict the sub-series with different frequencies. Finally, the MSSA is employed to determine the weight coefficients of each sub-series to combine the prediction results of the sub-series and get the final passenger flow prediction results. To verify the prediction performance of the established model, passenger flow datasets from four different types of Nanning Metro stations were taken as examples for carrying out experiments. The experimental results showed that: (a) The proposed hybrid model for short-term passenger flow prediction is superior to several baseline models in terms of both prediction accuracy and versatility. (b) The proposed hybrid model is excellent in multi-step prediction. Taking station 1 as an example, the MAEs of the proposed model are 3.677, 5.7697, and 8.1881, respectively, which can provide technical support for subway operations management. [ABSTRACT FROM AUTHOR]

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability, and the development of the artificial intelligence industry. To provide investors with a more reliable reference in terms of artificial intelligence index investment, this paper selects the NASDAQ CTA Artificial Intelligence and Robotics (AIRO) Index as the research target, and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics. Specifically, this paper uses the ensemble empirical mode decomposition (EEMD) method and the modified iterative cumulative sum of squares (ICSS) algorithm to decompose the index returns and identify the structural breakpoints. Furthermore, it combines the least-square support vector machine approach with the particle swarm optimization method (PSO-LSSVM) and the generalized autoregressive conditional heteroskedasticity (GARCH) type models to construct innovative hybrid forecasting methods. On the one hand, the empirical results indicate that the AIRO index returns have complex structural characteristics, and present time-varying and nonlinear characteristics with high complexity and mutability; on the other hand, the newly proposed hybrid forecasting method (i.e., the EEMD-PSO-LSSVM-ICSS-GARCH models) which considers these complex structural characteristics, can yield the optimal forecasting performance for the AIRO index returns. [ABSTRACT FROM AUTHOR]

Pingshan County, Hebei was affected by topography, geological structure, ecological environment and other factors, geological disasters such as landslides occurred frequently. Nine evaluation factors including topographic relief, slope, aspect, river network density, fault zone density, stratigraphic lithology, NDVI, land use type and geological disaster point density were selected. The weights of each evaluation factor were calculated by AHP and catastrophe theory, and the combination model of AHP and catastrophe theory was established and applied according to the minimum information entropy weight method. The results of geological disaster risk assessment in Pingshan County based on three methods were compared. The results show that the evaluation results of the combined model have higher accuracy and are in line with the development characteristics of geological disasters in this area. Combined model method combines subjective and objective, considering the influence of factors, the evaluation results are reliable. This study provides a new attempt and method for geological disaster risk assessment in Pingshan County and similar areas. [ABSTRACT FROM AUTHOR]

Fairness is an important feature of communication networks. It is the distribution, allocation, and provision of approximately equal or equal performance parameters, such as throughput, bandwidth, loss rate, and delay. In an optical burst switched (OBS) network, fairness is considered in three aspects: distance, throughput, and delay. Studies on these three types of fairness have been conducted; however, they have usually been considered in isolation. These fairness types should be considered together to improve the communication performance of the entire OBS network. This paper proposes a combined delaythroughput fairness model, where burst assembly and bandwidth allocation are improved to achieve both delay fairness and throughput fairness at ingress OBS nodes. The delay fairness and throughput fairness indices are recommended as metrics for adjusting the assembly queue length and allocated bandwidth for priority flows. The simulation results showed that delay and throughput fairness could be achieved simultaneously, improving the overall communication performance of the entire OBS network. [ABSTRACT FROM AUTHOR]

Considering that the demand of tourism destination is variable on the scale of urban agglomeration, the selection process of travel destination is divided into two stages. The traditional transportation combination model based on the multinomial Logit cannot reflect this characteristic. And it is the lack of consideration of the influence of travel distribution and the dynamic transfer of passenger flow between various transport routes. Therefore, this thesis established a combination model of travel demand distribution and transportation assignment with two-stage terminal selection characteristics based on the nested Logit. Based on the analysis of tourists' trip process on the scale of urban agglomeration, a tourist flow transport network with travel destination nest structure is constructed. The generalized cost impedance function of transportation route is constructed based on the direct cost of transportation mode and the indirect cost of travel time. Based on the characteristics of two-stage destination selection of tourists, the form of travel distribution function of tourist flow is given. Through the first-order optimization conditions, it proved that the volume of travel distribution and tourism passenger transport assignment can meet the two-stage equilibrium conditions in the equilibrium state. Based on the idea of MSA algorithm, it designed the solution algorithm of the model and verified the feasibility of the model and algorithm in a simplified example. The calculation results show that the two-stage equilibrium assignment model proposed in this paper can obtain the volume of travel distribution and transportation assignment at the same time, meanwhile compared with the multinomial logit model, the nested Logit structure fully considers the attraction measure of the city destination and the scenic spot destination, which is more in line with the choice behavior of the tourists when choosing the transportation route. Thus, it provides a new comparable method for the optimal allocation of tourism passenger flow transport network resources on the scale of urban agglomeration, and can provide data support for the transportation organization plans of government decision-making departments and tourism transport enterprises.

Accurate forecasting of runoff is necessary for water resources management. However, the runoff time series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved forecasting accuracy, a novel combination model based on complementary ensemble empirical mode decomposition (CEEMD) for runoff forecasting is proposed and applied in this paper. Firstly, the original runoff series is decomposed into a limited number of intrinsic mode functions (IMFs) and one residual based on CEEMD, which makes the runoff time series stationary. Then, approximate entropy is introduced to judge the complexity of each IMF and residual. According to the calculation results of approximate entropy, the high complexity components are predicted by Gaussian process regression (GPR), the medium complexity components are predicted by support vector machine (SVM), and the low complexity components are predicted by autoregressive integrated moving average model (ARIMA). The advantages of each forecasting model are used to forecast the appropriate components. In order to solve the problem that the forecasting performance of GPR and SVM is affected by their parameters, an improved fireworks algorithm (IFWA) is proposed to optimize the parameters of two models. Finally, the final forecasting result is obtained by adding the forecasted values of each component. The runoff data collected from the Manasi River, China is chosen as the research object. Compared with some state-of-the-art forecasting models, the comparison result curve between the forecasted value and actual value of runoff, the forecasting error, the histogram of the forecasting error distribution, the performance indicators and related statistical indicators show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of runoff correctly. HIGHLIGHTS CEEMD is introduced to obtain the IMF components and residual component.; Each IMF component and residual component is analyzed by approximate entropy, and suitable forecasting models are determined.; An IFWA is proposed to optimize the parameters of SVM and GPR models.; The excellent performance of the proposed model is evaluated by comparing the predictive results to other state-of-the-art comparative models.;

The aim of the present research is to estimate the saffron yield by land characteristics through a combination of adaptive neural-fuzzy inference system and particle swarm optimization in Siminehrood catchment, south of Urmia Lake, Iran. To achieve this target, 150 representative soil profiles were descripted in saffron fields. Then each genetic horizon was sampled for soil analysis. Climate rating was calculated from meteorological data by Food and Agriculture Organization framework. Saffron observed yield obtained from saffron field data. The results showed that coarse fragment, gypsum, electrical conductivity, organic carbon, cation exchange capacity, pH, calcium carbonate equivalent and climate rating have the highest correlation with saffron yield. The range of saffron estimated yield was between 1937–4124 and 1843–4025 kg per hectare for combination model and fuzzy model, respectively. Combination model doses a more accurate estimation compared with the observed yield values (2020–4200), statistical validation indicator results confirm this too. High agreement obtained from combination model between estimated saffron yield map with observed yield map. Finally, a combination of adaptive neural-fuzzy inference system and particle swarm optimization model can be employed as a powerful, low time-consuming and accurate method for estimating saffron yield in pre-decision for saffron cultivation. [ABSTRACT FROM AUTHOR]