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Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [ABSTRACT FROM AUTHOR]

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. [ABSTRACT FROM AUTHOR]

Accurate prediction of short-term passengers' origin and destination (OD) demands is key to efficient operation and management of urban rail transit (URT), especially in the case of congestion or an incident. However, short-term OD demand forecasting is more challenging than passenger flow forecasting, due to its uncertainty and high dimensions. So far, most OD prediction models capture the spatio-temporal dependencies of OD flow by means of training models on historical data, but what characteristics and laws influence the performance of OD prediction are still unknown. In this paper, we propose temporal Pearson correlation coefficients and approximate entropy, as well as spatial correlations, as indicators to reflect the inherent time–space correlations and complexity of the OD flow. Then, by analyzing automatic fare collection data of the Beijing and Shanghai URT system, this paper deeply discusses the relationships between the spatio-temporal correlations and complexity of the OD flow and the predictive performances of different models with regard to different intervals. Finally, this paper proposes the predictable problem of travel demands and points out that the spatial correlations of the OD matrix are more important than the temporal correlations and complexity in the short-term prediction of travel demands. In particular, the number of principal components of the OD flow can be a key indicator to measure the forecasting performance of a model. A reasonable interval is very important for short-term OD forecasting, and in the Beijing URT system, 30 min is a preferable choice for workdays and 50 min for weekends. All these findings are beneficial to guide users to build a suitable model or improve the existing model to obtain better prediction performances. [ABSTRACT FROM AUTHOR]

The PM2.5 index is an important basis for measuring the degree of air pollution. The accurate prediction of PM2.5 concentration has an important guiding role in air pollution prevention and control. The Pearson Correlation Coefficient (PCC) is a common index used to mine the correlation between meteorological factors and other air pollutants. However, this index cannot be used to mine non-linear correlations, nor can it quantitatively analyze the weight of each related attribute. In order to accurately explore the correlation between meteorological factors and other air pollutants and to achieve an accurate prediction of PM2.5 concentration, this paper proposes a short- and long-time memory (LSTM) network prediction model based on Copula entropy (CE) and the adaptive genetic algorithm (AGA). By calculating CE, the correlation between multiple meteorological factors and various atmospheric pollutants and PM2.5 was analyzed. The correlation of influencing factors was sorted according to the size of the correlation coefficients. The contribution rate of meteorological factors and atmospheric pollutants to PM2.5 concentration was determined, used as the weight of each influencing factor and predicted as the input data of the prediction model. In this paper, a long- and short-term memory network (LSTM) suitable for time series data was selected as the prediction model, while the selection of model parameters was taken into account, and the relevant parameters were sought by an adaptive genetic algorithm (AGA). The air pollutant data and meteorological data of Beijing from 1 January 2016 to 31 December 2016 were selected, and MAE and RMSE were used as evaluation indexes. By comparing the experimental results of the CE-AGA-LSTM with those of other eight prediction models (LR, SVM, RF, ARMA, ST-LSTM, LSTM, CE-LSTM and CE-RNN), we found that among the models, the CE-AGA-LSTM model provided the lowest MAE and RMSE values, i.e., 14.5 and 21.88, respectively. At the same time, the loss rate and accuracy of the CE-AGA-LSTM model were evaluated, and the experimental results verified the validity of the model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department 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.)

• To solve the forecasting problems of time-lead and nonlinearity in a system, a novel grey multivariable model is proposed. • To reduce the morbidity of the model, the solution of the proposed model is deduced through a derivation method. • The grey prediction evolution algorithm is employed to find the optimal value of parameters. • The validity of the new model was verified by three examples and applied to house price forecasting. Time lags or leads and non-linearity of complex systems are critical to address in accurate scientific prediction. However, few studies have focused on time-lead and nonlinear models with limited information. Therefore, this paper aims to develop a novel grey multivariate model with time-lead operator τ and nonlinear factor r from the perspective of this problem. Next, the solution of the proposed model is deduced through a derivation method to reduce the morbidity of the model. Then, the parameter identification of the model is given on the basis of the least-squares method. The grey prediction evolution algorithm is employed to find the optimal value of parameters τ and r to meet the minimum errors. Finally, house prices of Wuhan, Beijing and Shanghai in China are performed to verify the validity and reliability of the new model. The results demonstrate the model we proposed has satisfactory prediction precision, demonstrating that time-lead and nonlinear factor in the model is reasonable. At the end of the paper, the house prices of these three cities from 2022 to 2028 are also predicted. The average annual growth rate in the three regions is 2.49%, 3.57% and 2.97%, respectively. These results are useful to policy analysis regarding house price correlations. They provide insights for market investors and policy makers. [ABSTRACT FROM AUTHOR]

PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. When using common point prediction models for PM2.5 concentration prediction, the influence of various uncertainties on PM2.5 concentrations makes the prediction results suffer from poor accuracy. To address this issue, this paper proposes the quantile regression neural network (QRNN) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations calculated by the QRNN model are imported into the KDE model to obtain the probability density predictions of PM2.5 concentrations. In the paper, empirical analyses are carried out with the cities of Beijing and Jinan in China as well as six other datasets, and the prediction performance of the model is assessed by using evaluation criteria in both point prediction and interval prediction. The simulation reveals that the predictive performance of the LASSO-QRNN-KDE model is well, and the model is not only effective in filtering high-dimensional data, but also has a higher accuracy compared to common research models. In addition, the model is able to describe the uncertainty of PM2.5 concentration fluctuations and carry more information on the variation of PM2.5 concentrations, which can provide a novel and excellent PM2.5 concentration prediction tool for relevant policy makers. [ABSTRACT FROM AUTHOR]

This paper aims at the imbalanced characteristics and proposes a novel evolutionary cost‐sensitive support vector machine (CSSVM) by integrating cost‐sensitive learning, support vector machine, and genetic algorithm for carbon price trend prediction. First, carbon price trend prediction is converted into a binary‐class prediction problem for CSSVM, in which a higher misclassification cost is imposed on the minority samples. In comparison, a more negligible misclassification cost is imposed on most samples. Second, a genetic algorithm (GA) is used to optimize all parameters of CSSVM synchronously. Taking Beijing, Hubei, and Guangdong carbon markets as samples, the empirical results show that the proposed model has a higher classification accuracy and lower misclassification costs compared with other popular prediction models. Furthermore, the sensitivity analysis verifies that the proposed approach is robust. [ABSTRACT FROM AUTHOR]

Accurate and effective PM2.5 concentration prediction has important implications for public health and the ecological environment. To provide more accurate early warnings for haze prevention, urban planning, and people's travel planning, this paper proposes a new combined PM2.5 concentration prediction model. Firstly, the original sequence is decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), followed by decomposition and reconstruction of the data by adaptive variational mode decomposition (AVMD) and sample entropy (SE), and then the reconstructed subseries are predicted by long and short-term memory networks (LSTM). The empirical analysis was carried out with three datasets from Beijing, Tianjin, and Baoding, and the following conclusions can be drawn: (1) The validity and robustness of the proposed model were verified, with R2 (0.982), RMSE (2.792), and MAPE (9.088%) being optimal in all comparison experiments. (2) The incorporation of secondary decomposition and pattern reorganization algorithms can effectively handle data with high volatility and non-linearity. (3) Compared with traditional machine learning models, the long and short-term memory network is more suitable for time series prediction. The model provides a novel and effective PM2.5 concentration prediction tool for the government and the public. [ABSTRACT FROM AUTHOR]

The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the system, and expands the modeling structure of this model. The modeling mechanism of the classical feedforward neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the grey model of the carbon emission dynamic system is established with a neural network. Then, the properties of the model are studied, the parameters of the model are optimized, and the modeling steps are obtained. Finally, the validity of the model is analyzed by using the carbon emissions of Beijing from 2009 to 2018. The results of the four cases show that the simulation and prediction errors of the new model are all less than 10%, and case 1 shows the best results of 1.56% and 2.07%, respectively, which are used to predict the carbon dioxide emissions in the next 5 years in Beijing. The prediction results are in accordance with the actual trend, which indicates the effectiveness and feasibility of the model. [ABSTRACT FROM AUTHOR]

Air pollution has become a critical factor affecting the health of human beings. Forecasting the trend of air pollutants will be of considerable help to public health, including improving early-warning systems. The article designs a novel hybrid deep learning framework FPHFA (FPHFA is the abbreviation of the title of this paper) for PM2.5 concentration forecasting is proposed, which learns spatially correlated features and long-term dependencies of time series data related to PM2.5. Owing to the complex nonlinear dynamic and spatial features of pollutant data, the FPHFFA model combines multi-channel one-dimensional convolutional neural networks, bi-directional long short-term memory neural networks, and attention mechanisms for the first time. Multi-channel 1D CNNs are applied to capture trend features between some sites and overall spatial characteristics of PM2.5 concentration, Bi LSTMs are used to learn the temporal correlation of PM2.5 concentration, and the attention mechanism is used to focus more effective information at different moments. We carried out experimental evaluations using the Beijing dataset, and the outcomes show that our proposed model can effectively handle PM2.5 concentration prediction with satisfactory accuracy. For the prediction task from 1 to 12 h, our proposed prediction model performs well. The FPHFA also achieves satisfactory results for prediction tasks from 13 to 96 h. [ABSTRACT FROM AUTHOR]

Air pollution is a growing problem and poses a challenge to people's healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management. In this paper, a novel PM2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is constructed by deep learning techniques based on the principles related to spatial big data. This model consists of the convolutional block attention module (CBAM), the convolutional neural network (CNN), and the bi-directional long short-term memory neural network (Bi LSTM). CBAM is applied to the extraction of feature relationships between pollutant data and meteorological data and assists in deeply obtaining the spatial distribution characteristics of PM2.5 concentrations. As the output layer, Bi LSTM obtains the variation pattern of PM2.5 concentrations from spatial data, overcomes the problem of long-term dependence on PM2.5 concentrations, and achieves the task of accurately forecasting PM2.5 concentrations at multiple sites. Based on real datasets, we perform an experimental evaluation and the results show that, in comparison to other models, CBAM-CNN-Bi LSTM improves the accuracy of PM2.5 concentration prediction. For the prediction tasks from 1 to 12 h, our proposed prediction model performs well. For the 13 to 48 h prediction task, the CBAM-CNN-Bi LSTM also achieves satisfactory results. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) has become an important method of predicting extreme weather events, but orography is one of the key factors affecting the performance of NWPs. In this paper, based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data, a method for constructing a global orographic dataset suitable for NWP spectral models is investigated. The Yin-He global spectrum model (YHGSM) is used to simulate the early and peak periods of the extreme rainfall event on 20 July 2021 in Henan Province, China, and the heavy rain in Beijing in order to verify the effectiveness and superiority of the proposed orographic construction method. It is demonstrated that in a few cases the direct two-dimensional filter can sometimes simulate more intense rainfall, but in general, the bidirectional one-dimensional filter is better than the direct two-dimensional filter in orographic processing, and the bidirectional one-dimensional filter can filter out more of the small-scale orographic information. The effect of the higher orographic resolution before conversion to spectral space is not very obvious, but it is demonstrated that the simulation results are better for the heavy-rainfall level. In conclusion, in most cases, the simulations conducted using the new global orographic dataset based on ASTER data are better than those obtained using the model's original orography, especially for torrential and extreme rainfall. These conclusions provide a reference for future predictions of and research on extreme rainfall events. [ABSTRACT FROM AUTHOR]

To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy. [ABSTRACT FROM AUTHOR]

Virtual Coupling (VC) is regarded as a breakthrough to the traditional train operation and control for improving the capability and flexibility in railways. It brings benefits as trains under VC are allowed to operate much closer to one another, forming a virtually coupled train set (VCTS). However, the safe and stable spacing between trains in the VCTS is a problem since there are no rigid couplers to connect them into a fixed formation, especially in high-speed scenarios. Due to the close spacing, the interference between trains becomes non-negligible as various maneuvers of the preceding train can significantly affect driving behaviors of the following train; this results in fluctuating spacing and therefore an unstable VCTS. Aiming at minimizing the interference and maintaining constantly safe spacing between trains in the VCTS, this paper presents a distributed model predictive control (DMPC) approach for solving the high-speed VCTS control problem. Particularly, the proposed control method focuses on the feasibility and stability of this problem, with considerations of the coupled constraint of safety braking distance and the individual constraints of speed limit variations and restricted traction/braking performance. To guarantee feasibility and stability, the terminal controller and invariant set of the DMPC are designed. For rigor, sufficient conditions of feasibility and stability are mathematically proved and derived. Based on the data of the Beijing-Shanghai high-speed railway line, numerical experiments are conducted to verify the correctness of derived sufficient conditions and the effectiveness of the proposed control method under interference and disturbances. [ABSTRACT FROM AUTHOR]

Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM. [ABSTRACT FROM AUTHOR]

Predicting the passenger flow of public transport in a newly developed area of a city is very urgent for designing a precise and efficient public transport network. This paper proposes a new prediction model by exploring the relationship between the passenger flow of a station and its surrounding area's factors. First, in order to obtain more accurate factors affecting the passenger flow, the city is divided into multiple regions with similar internal traffic properties and moderate spatial size using the data of urban road network and buildings. Second, to effectively solve the problem of fuzziness of the station's attraction scope, the concept of the membership degree and fuzzy processing method is proposed. Finally, the station's passenger flow prediction model is launched based on Xgboost. The experimental results on three districts in Beijing show that our method outperforms all baselines significantly, which improves the accuracy by more than 20%. [ABSTRACT FROM AUTHOR]

The generalized regression neural network (GRNN) is one of the most effective neural models in the field of information prediction. In this paper, we take advantage of the hesitant fuzzy set (HFS) and develop the GRNN under the hesitant fuzzy environment. However, the performance of the GRNN only depends on the smooth factor after determining the input and output variables. To determine the most appropriate smooth factor of network, we propose an improved fruit fly optimization algorithm with fast decreasing step (FDS-FOA) based on a dynamic step size function. A specific implementation process of the optimized GRNN based on FDS-FOA is also presented. Moreover, we apply the proposed model to the prediction of air quality index (AQI) in Beijing. Comparative analysis and sensitivity analysis are further conducted to illustrate the advantages of the optimized GRNN based on FDS-FOA. [ABSTRACT FROM AUTHOR]

Accurate monitoring of air quality can no longer meet people's needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time-series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long- and short-term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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.)

Based on a set of deep learning and mode decomposition methods, a short-term prediction model for PM2.5 concentration for Beijing city is established in this paper. An ensemble empirical mode decomposition (EEMD) algorithm is first used to decompose the original PM2.5 timeseries to several high- to low-frequency intrinsic mode functions (IMFs). Each IMF component is then trained and predicted by a combination of three neural networks: back propagation network (BP), long short-term memory network (LSTM), and a hybrid network of a convolutional neural network (CNN) + LSTM. The results showed that both BP and LSTM are able to fit the low-frequency IMFs very well, and the total prediction errors of the summation of all IMFs are remarkably reduced from 21 g/m3 in the single BP model to 4.8 g/m3 in the EEMD + BP model. Spatial information from 143 stations surrounding Beijing city is extracted by CNN, which is then used to train the CNN+LSTM. It is found that, under extreme weather conditions of PM2.5 < 35 g/m3 and PM2.5 > 150 g/m3, the prediction errors of the CNN + LSTM model are improved by ~30% compared to the single LSTM model. However, the prediction of the very high-frequency IMF mode (IMF-1) remains a challenge for all neural networks, which might be due to microphysical turbulences and chaotic processes that cannot be resolved by the above-mentioned neural networks based on variable–variable relationship. [ABSTRACT FROM AUTHOR]

The accurate and stable prediction of electricity consumption is essential for intelligent power systems in rapidly developing countries. Grey prediction model is one of choices for prediction under the condition of limited historical data. Nonetheless, it seems rather sceptical using single-variable grey prediction model to predict the dynamics of a complex system. This paper presents a novel multivariable grey prediction model based on first-order linear difference equation for long-term electricity consumption prediction. The proposed model solves the problem of parameter estimation and variable prediction deriving from different approaches through rewriting the whitenization equation of multivariable grey model (MGM(1, m)). To validate the effectiveness of the proposed hybrid model, the electricity consumption is estimated and predicted over the data from Shanxi province and Beijing city in China from 1999 to 2018. The results show that the hybrid model provides a better estimation and prediction performance compared with other prediction model for predicting electricity consumption. [ABSTRACT FROM AUTHOR]

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively. • The coupling mechanism between multiple energy sources of the integrated energy system is analyzed. • An endogenous-exogenous uncertainty method is proposed to extract the dynamic coupling relationship between multiple loads. • An endogenous-exogenous uncertainty-based GA-LSTM multivariate load prediction model is developed. • Optimizing model parameters using fine-tuned migration learning to deepen the impact of endo-exogenous uncertainty. [ABSTRACT FROM AUTHOR]

PM2.5 (particulate matter with a size/diameter ≤ 2.5 μm) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM2.5 are non-linear and non-stationary, it is difficult to predict future PM2.5 distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban PM2.5 prediction model based on variational mode decomposition modified by the correntropy criterion, the state transition simulated annealing (STASA) algorithm, and a support vector regression model to overcome the disadvantages of traditional forecasting techniques which consider different environmental factors. Two experiments were performed with the model to assess its effectiveness and predictive ability: in experiment I, we verified the performance of STASA on benchmark functions, while in experiment II, we used PM2.5 data from different epochs and regions of Beijing to verify its forecasting performance. The experimental results showed that the proposed model is robust and can achieve satisfactory prediction results under different conditions compared with current forecasting techniques. [ABSTRACT FROM AUTHOR]

In this paper, a classification model for the migration of residents without local household registration in Beijing is established through the algorithm of Support Vector Machine (SVM) and the model is verified by using the migration data of Beijing, which is collected from various surveys. Our result shows that, compared to BP Neural Network and Logistic Regression, SVM performs better in terms of accuracy and generalization for these particular classification tasks. We identify ten classification features, which, we believe, are crucial as the determining factors to predict the migration trend in Beijing. These ten features include age, education, occupation, income, family status, housing status, leisure status, insurance status, temporary residence permit status and residence time. Our research shows that, taking into account the population demographic attributes and behavioral characteristics, our SVM classification model is able to predict the migration trend with a high accuracy rate. We believe that the results presented in this paper will provide valuable practical insights for various governmental departments of megacities in grasping the migration trend of different types of residents without local household registration, as well as in improving the residence policies, in order to encourage outward migration and tackle the issue of rapid population growth. [ABSTRACT FROM AUTHOR]

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency. On this basis, considering the correlation between the spatiotemporal trends of short-term taxi traffic, a ConvLSTM neural network model with Long Short Term Memory (LSTM) time series processing ability and Convolutional Neural Networks (CNN) spatial feature processing ability is constructed to predict the travel demand for urban taxis. The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing. The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM, Autoregressive Integrated Moving Average model (ARIMA), CNN, and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 metrics. Notably, the SMAPE metric exhibits a remarkable decline of 21.03% with the utilization of our proposed model. These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting. [ABSTRACT FROM AUTHOR]

This study combines statistical methods and a Markov model to analyze interregional differences in land use in Beijing since 2003 and to predict land use changes for 2015 and 2019. First, the paper proposes a new concept, land use flow, which counts the change in area from the beginning to the end of the period of interest, to analyze changing land use patterns using statistical records from 2003 to 2011. Second, based on land use data between 2003, 2007 and 2011, this paper applied a Markov model to the prediction of Beijing land use in 2015 and 2019. The results show that: (1) the area of arable land decreased significantly across all of Beijing, with the greatest decrease, 6,953 ha, occurring in Tongzhou. The amount of urban land increased significantly, particularly in eastern and southern Beijing, in areas such as Chaoyang, Tongzhou and Daxing. The amount of land use for orchards fluctuated depending on the distance from the city center; (2) Haidian experienced the greatest change in its ratio of land use flow to urban land (100 %), while Tongzhou had the greatest reduction in arable land (−67.6 %); (3) the annual rate of land use change for Beijing as a whole was 0.89 %. Fangshan had the highest rate of annual conversion to grassland (13.2 %), while the Daxing District had the highest rate of change to urban land (5.1 %). Shijingshan experienced the greatest rate of annual change to other uses due to its small base (135.38 %); and (4) the predictions suggest that urban land is increasing and arable land is decreasing in the 14 districts and counties of Beijing. Several conclusions can be drawn from these results. First, the concept of land use flow is useful for analyzing land use change and simplifies previous methods that were based on descriptions of the change orientation. Moreover, an understanding of the sub-flow of land use is improved by the linkage between land use change and the Markov model. Second, the predictions of land use in 2015 and 2019 suggest problems of urban sprawl and diminishing arable land in the patterns of land use that exist in Beijing and that will continue in the future. In terms of sustainability and Beijing's goal of being a world city, the findings can help local authorities better understand and address a complex urban master plan, and develop improved land use management strategies that can better balance urban expansion and ecological conservation. [ABSTRACT FROM AUTHOR]

This paper deals with the train scheduling problem for metro networks taking into account time-dependent passenger origin–destination demands and train speed profiles. The aim is to adjust train schedules online according to time-dependent passenger demands so that passenger satisfaction and operational costs are jointly optimized. An extended passenger absorption model that explicitly includes time-dependent passenger origin–destination demands is developed, where the term "absorption" refers to passengers boarding trains. Then, the passenger absorption model is extended to a bi-level framework, where passenger demands and rolling stock availability are considered at the higher level, and detailed timetables and train speed profiles are included at the lower level. A bi-level model predictive control (MPC) approach is developed for the integrated problem. The optimization problems of both levels of the bi-level MPC approach can be converted into mixed-integer linear programming (MILP) problems, which enables us to solve them with existing MILP solvers. We then show that the recursive feasibility of both the higher-level and the lower-level optimization problems can be guaranteed. In this way, we can achieve real-time train scheduling for the metro system. Numerical experiments, based on real-life data from the Beijing metro network, illustrate the effectiveness of the extended passenger absorption model and the proposed bi-level MPC approach. • An extended passenger absorption model is developed to design train departure frequencies considering passenger demands. • A train scheduling model is presented to include detailed timetables, train speed profiles, and train orders. • A bi-level model predictive control approach is proposed for the train scheduling problem. [ABSTRACT FROM AUTHOR]

Air quality prediction is a global hot issue, and PM 2.5 is an important factor affecting air quality. Due to complicated causes of formation, PM 2.5 prediction is a thorny and challenging task. In this paper, a novel deep learning model named temperature-based deep belief networks (TDBN) is proposed to predict the daily concentrations of PM 2.5 for the next day. Firstly, the location of PM 2.5 concentration prediction is Chaoyang Park in Beijing of China from January 1, 2018 to October 27, 2018. The auxiliary variables are selected as input variables of TDBN by Partial Least Square (PLS), and the corresponding data is divided into three independent sections: training samples, validating samples and testing samples. Secondly, the TDBN is composed of temperature-based restricted Boltzmann machine (RBM), where temperature is considered as an effective physical parameter in energy balance of training RBM. The structural parameters of TDBN are determined by minimizing the error in the training process, including hidden layers number, hidden neurons and value of temperature. Finally, the testing samples are used to test the performance of the proposed TDBN on PM 2.5 prediction, and the other similar models are tested by the same testing samples for convenience of comparison with TDBN. The experimental results demonstrate that TDBN performs better than its peers in root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). [ABSTRACT FROM AUTHOR]

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants. [ABSTRACT FROM AUTHOR]

Carbon trading is an effective way to limit global carbon dioxide emissions. The carbon pricing mechanisms play an essential role in the decision of the market participants and policymakers. This study proposes a carbon price prediction model, multi-decomposition-XGBOOST, which is based on sample entropy and a new multiple decomposition algorithm. The main steps of the proposed model are as follows: (1) decompose the price series into multiple intrinsic mode functions (IMFs) by using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); (2) decompose the IMF with the highest sample entropy by variational mode decomposition (VMD); (3) select and recombine some IMFs based on their sample entropy, and then perform another round of decomposition via CEEMDAN; (4) predict IMFs by XGBoost model and sum up the prediction results. The model has exhibited reliable predictive performance in both the highly fluctuating Beijing carbon market and the comparatively stable Hubei carbon market. The proposed model in Beijing carbon market achieves improvements of 30.437%, 44.543%, and 42.895% in RMSE, MAE, and MAPE, when compared to the single XGBoost models. Similarly, in Hubei carbon market, the RMSE, MAE, and MAPE based on multi-decomposition-XGBOOST model decreased by 28.504%, 39.356%, and 39.394%. The findings indicate that the proposed model has better predictive performance for both volatile and stable carbon prices. [ABSTRACT FROM AUTHOR]

The large airport terminal is the city's transportation hub and logistics center, and its building electricity consumption is twice or even higher than that of general large public buildings. Energy-saving and consumption reduction of the terminal building is not only the need to improve the operational efficiency and reduce the operation cost of the airport but also the need to realize the goal of "double carbon" in China. In this study, a large airport terminal in Beijing is taken as the research object. According to the characteristics of its main electrical equipment system, and the measured data of passenger flow from January 2020 to February 2023, combined with Pearson correlation analysis, K-means cluster analysis and multiple regression analysis, the influence and correlation characteristics of building scale, passenger flow and outdoor meteorological parameters on its electricity consumption are analyzed. The results show that: 1) The daily electricity consumption of the terminal can be analyzed by five levels according to the daily passenger flow, namely, 10,000 < N ≤ 30,000,30,000 < N ≤ 50,000, 50,000 < N ≤ 70,000, 70,000 < N ≤ 100,000, and 100,000 < N ≤ 130,000; 2) The electricity consumption of the terminal can be divided into three categories: basic electricity consumption related to the building scale of the terminal, variable electricity consumption I related to passenger flow, and variable electricity consumption II related to outdoor air temperature and passenger flow. Based on this, the terminal's daily electricity consumption prediction model is constructed, and the measured data verify the model's effectiveness. 3) Put forward the evaluation reference value of the daily electricity consumption of different passenger flow in this terminal, When the year passenger flow reaches the design value of 45 million, the normal daily electricity consumption level ranges from 0.44 to 0.48 kW·h/(m2·day). The research results can provide methods and evaluation reference for electricity consumption prediction, and accurate control of electricity consumption of main equipment systems in airport terminal. [ABSTRACT FROM AUTHOR]

Based on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site selection model was found. A comparison was performed between the prediction model functioning with a buffer and without one, and the accuracy of the location model was verified by comparing the actual change trend and the predicted trend in two years. The following conclusions were obtained: (1) coffee shops in the main urban area of Beijing are clustered in an area within 12 km of the main urban center, and also around the core commercial agglomeration area; (2) the random forest (RF) model is the best model in this study, and the accuracy values before and after buffer analysis were 0.915 and 0.929, respectively; and (3) after verifying the accuracy of the model through two years of data, we recommend the establishment of a main road buffer zone for site selection, and the success rate of site selection was found to reach 72.97%. This study provides crucial insight for coffee shop prediction model selection and potential store location selection, which is significant to improving the layout of leisure spaces and promoting economic development. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of air pollution incidents has seriously affected people's health and life. Therefore, PM 2.5 , as the main pollutant, is an important research object of air pollution at present. Effectively improving the prediction accuracy of PM 2.5 volatility makes the PM 2.5 prediction content perfect, which is an important aspect of PM 2.5 concentration research. The volatility series has an inherent complex function law, which drives the volatility movement. When machine learning algorithms such as LSTM (Long Short-Term Memory Network) and SVM (Support Vector Machine) are used for volatility analysis, a high-order nonlinear form is used to fit the functional law of the volatility series, but the time-frequency information of the volatility has not been utilized. Based on EMD (Empirical Mode Decomposition) technique, GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model and machine learning algorithms, a new hybrid PM 2.5 volatility prediction model is proposed in this study. This model realizes time-frequency characteristic extraction of volatility series through EMD technology, and integrates residual and historical volatility information through GARCH model. The simulation results of the proposed model are verified by comparing the samples of 54 cities in North China with the benchmark models. The experimental results in Beijing showed that MAE (mean absolute deviation) of hybrid-LSTM decreased from 0.00875 to 0.00718 compared with LSTM, and hybrid-SVM based on the basic model SVM also significantly improved generalization ability, and its IA (index of agreement) improved from 0.846707 to 0.96595, showing the best performance. The experimental results show that the hybrid model is superior to other considered models in terms of prediction accuracy and stability, which verifies that the hybrid system modeling method is suitable for PM 2.5 volatility analysis. [ABSTRACT FROM AUTHOR]

Long-term observation of the urban ecological footprint will present useful knowledge of anthropogenic impact on and sustainable solutions for cities. This paper proposed a new framework to predict dynamic change and intrinsic structure of urban ecological footprint with the Markov chain. The system dynamic model based on Markov chain was then established for estimating Beijing's footprint during the period of 2001–2020. The results showed that Beijing's footprint kept stable in the long term due to steady consumption pattern and environmental mitigation policy. The footprint intensity has been decreasing constantly due to the expanding population against the stable total footprint. Energy consumption was found to be the major contributor to Beijing's footprint. Sensitivity analysis was also presented by testing the population and economic growth under five scenarios. This work may provide insights into the land metabolism mechanism and guidance for urban planning. [ABSTRACT FROM AUTHOR]

Water shortages and groundwater depletion are critical issues in the world, leading to unsustainable agricultural production and adverse ecological impacts. Here, the new Multifactor-Quantitative joint Prediction Model (MQPM) is developed to quantitatively predict the Groundwater Storage Anomalies (GWSA), which includes an annual multifactor module and a monthly quantitative module. The correlative coefficients from two modules between simulated GWSA and observed GWSA reach up to 0.98 and 0.87, respectively. Taking Beijing as an example, results show that GWSA trends before South-to-North Water Diversion (SNWD) (2005–2014) and after SNWD (2015–2018) are at a rate of −3.00 × 108 m3/yr and 1.95 × 108 m3/yr, respectively, which reflects the effectiveness of water diversion. Additionally, the predicted results show that GWSA from the multifactor module will increase to 45.97 × 108 m3 by 2028. The quantitative module designs four scenarios under different climate changes and policies, from which the predicted GWSA with values ranging from 28.49 × 108 m3 to 63.06 × 108 m3. For the latter module, the groundwater level will recover to ∼8.9 m up to 2028, combining multiple favorable conditions. Finally, factors consisting of water diversion, climate change, and water-saving policies have a vital influence on groundwater variations, and contributions of these factors to the GWSA account for 46%, 27%, and 27%, respectively. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM), especially vanilla LSTM (VLSTM), has been widely used in air quality prediction field. However, VLSTM has many more parameters, thereby making training slow and prediction performance unstable. The VLSTM network input data have not been selected for better efficiency. In this paper, we propose an air quality prediction model based on the improved VLSTM with multichannel input and multiroute output (IVLSTM-MCMR). The proposed model includes the IVLSTM and MCMR modules. The proposed IVLSTM module is developed by improving the VLSTM inner structure of VLSTM in order to reduce the number of parameters that help to accelerate the convergence. A new historical information usage approach is further proposed to obtain a stable training process. For the MCMR module, a multichannel data input model (MC) with an improved linear similarity dynamic time warping is introduced to choose the valid data as the input of IVLSTM. A multiroute output model (MR) is designed to integrate the results from MC, in which the results of different target stations with different features are output by different routes. We evaluate the proposed model with the collected data from Beijing, China, and the experimental results show that our model achieves improvements regarding the predication performance. • An air quality prediction model IVLSTM-MCMR is proposed based on deep learning. • The number of parameters in IVLSTM-MCMR is reduced to accelerate the convergence. • The improved linear similarity dynamic time warping is introduced in IVLSTM-MCMR. • The integration of multi-channel data input and multi-route output is designed. [ABSTRACT FROM AUTHOR]

[Display omitted] • The measurement errors in long-time series remote sensing images are corrected by statistical and spatio-temporal prediction model. • The ecological degradation of the Miyun Reservoir basin from 1985 to 2021 is mainly caused by anthropogenic factors. • Through statistical theory, the ecological development trend in the experimental area is analyzed, and the conclusion is verified by land use evolution. Miyun Reservoir, located in the Miyun District, Beijing, China, is the largest comprehensive water conservancy project and is an important ecological protection area in the North China region. Changes within the basin are the driving factors affecting the ecosystem in the watershed; therefore, it is important to analyze the changes in the ecological environment of Miyun Reservoir. For the analysis of a long time series of image data remotely sensed by satellite, the outliers caused by atmospheric, lighting, and sensor measurement errors are significant, and it is difficult for traditional algorithms to effectively recover the true image value. To address this, this paper proposes a theoretical model for predicting spatio-temporal variation based on deep learning to identify and correct invalid and anomalous values in extended time series data. This study corrected and analyzed the results of Remote Sensing based Ecological Index inversion of Landsat data of the Miyun Reservoir watershed from 1985 to 2021. The findings and conclusions of this study are important for the analysis of long time series image data from satellite remote sensing and for improving regional ecological evaluation and sustainable development planning. [ABSTRACT FROM AUTHOR]

Background: Based on the high prevalence and occult-onset of osteoporosis, the development of novel early screening tools was imminent. Therefore, this study attempted to construct a nomogram clinical prediction model for predicting osteoporosis. Methods: Asymptomatic elderly residents in the training (n = 438) and validation groups (n = 146) were recruited. BMD examinations were performed and clinical data were collected for the participants. Logistic regression analyses were performed. A logistic nomogram clinical prediction model and an online dynamic nomogram clinical prediction model were constructed. The nomogram model was validated by means of ROC curves, calibration curves, DCA curves, and clinical impact curves. Results: The nomogram clinical prediction model constructed based on gender, education level, and body weight was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. An online dynamic nomogram was constructed. Conclusions: The nomogram clinical prediction model was easy to generalize, and could help family physicians and primary community healthcare institutions to better screen for osteoporosis in the general elderly population and achieve early detection and diagnosis of the disease. [ABSTRACT FROM AUTHOR]

As one of the key technologies of HF communication, the maximum usable frequency (MUF) prediction method has been widely discussed. To experimentally confirm the reliability of commonly used MUFs prediction models for high-frequency communication, we have compared maximum observed frequencies (MOFs) and predicted MUFs to assess the accuracy of two typical prediction models. The root-mean-square error (RMSE) and relative RMSE (RRMSE) between oblique sounding MOFs and the predicted MUFs were used to assess the model's accuracy. The oblique sounding path was from Changchun to Jinyang, and the vertical-sounding ionosonde was located in Beijing, which was approximately the midpoint of the oblique sounding circuit. The statistical analysis results show that: (a) the trend of prediction results from the Lockwood and the Istituto Nazionale di Geofisica e Vulcanologia (INGV) model are in good agreement with the observations: the mean RMSE and RRMSE of the INGV model are less than those of the Lockwood model; (b) in the four different periods (sunrise, daytime, sunset, and nighttime) of the whole day, the maximum difference of RMSE between the Lockwood and INGV model is 0.14 MHz (the INGV performs better than the LWM), with the corresponding differences of RRMSE being 0.31% at sunrise and 0.68% at daytime; (c) in the four seasons of spring, summer, autumn, and winter, the minimum RMSE values of the Lockwood and INGV models are 1.51 MHz and 1.37 MHz, respectively, which are obtained in winter, and the corresponding RRMSEs are 11.47% and 11.79%, respectively; (d) in the high and low solar activity epochs, the mean RMSEs of the Lockwood and INGV models are 1.63 MHz, and 1.54 MHz, with corresponding mean RRMSE values of 11.47% and 11.55%. In conclusion, the INGV model is more suitable for MUF prediction over Beijing and its adjacent mid-latitude regions from the RMSE comparison of the two models. [ABSTRACT FROM AUTHOR]

Indwelling catheter is a routine procedure in surgical patients. Studies have shown that prolonged indwelling urinary catheterization increases the risk of postoperative urinary tract infection. Although early removal of the urinary catheter after operation can reduce the risk of postoperative urinary symptoms and tract infections, it may lead to postoperative anesthetic dysuria. Therefore, this study investigates the urinary retention and related risk factors in patients after thoracoscopic lobectomy under general anesthesia. The clinical data of 214 patients who underwent thoracoscopic lobectomy in the Department of Thoracic Surgery of a tertiary class A cancer hospital in Beijing from July 2020 to April 2021 were collected. A risk prediction model was established by logistic regression analysis, and the prediction effect was determined using the area under the receiver operating characteristic (ROC) curve. The incidence of indwelling catheter after thoracoscopic lobectomy was 44.8% (96/214). Sex (OR = 21.102, 95% CI: 2.906–153.239, P = 0.003), perception of shame (OR = 74.256, 95% CI: 6.171–893.475, P = 0.001), age (OR = 1.095, 95% CI: 1.014–1.182, P = 0.021), and bed rest time (OR = 1.598, 95% CI: 1.263–2.023, P < 0.021) were the factors influencing urinary retention after thoracoscopic lobectomy. This model can effectively predict the occurrence of postoperative urinary retention in patients with lung cancer and help medical staff to intervene effectively before the onset of urinary retention, which provides reference for preventive treatment and nursing intervention. [ABSTRACT FROM AUTHOR]

• The control interval constraint method can increase the control interval of MPC. • The water level constraint method can avoid unnecessary control of minor deviations. • The simultaneous use of the two methods can greatly reduce the gate control times. Model predictive control (MPC) is an effective real-time control method in dealing with water level deviations in canal pools. However, the control interval must be relatively small if some canal pools have short time delay characteristics, resulting in frequent gate control and limited use in practical engineering. In this paper, control interval constraint and water level soft constraint methods were proposed to handle frequent gate control problems in MPC. The MPC method with these constraint methods was tested on a simulation model of the Beijing–Shijiazhuang part of the Middle Route of the South-to-North Water Diversion Project (MRP) with two kinds of disturbances common to this project. The results indicate that the control interval constraint can increase the gate control interval and avoid back-and-forth gate opening adjustments without affecting the water level regulation. The water level constraint helps avoid unnecessary control to minor water level deviations. With both constraints used together, the control times can be reduced by 60% and 37%, respectively, in the two test disturbances. This control method meets the demand of large control intervals in multi-cascade pools. [ABSTRACT FROM AUTHOR]

• A novel tri-stage online dispatch framework for a microgrid is proposed. • The proposed framework coordinates the EV charging behavior at different timescales. • Multiple uncertainties introduced by EV, PV, and load are taken into account. • The real-world data of eight EVs in Beijing is collected to model EV charging behavior. This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced. [ABSTRACT FROM AUTHOR]

Background The early prediction of intravenous corticosteroid (IVCS) resistance in acute severe ulcerative colitis (ASUC) patients remains an unresolved challenge. This study aims to construct and validate a model that accurately predicts IVCS resistance. Methods A retrospective cohort was established, with consecutive inclusion of patients who met the diagnosis criteria of ASUC and received IVCS during index hospitalization in Peking Union Medical College Hospital between March 2012 and January 2020. The primary outcome was IVCS resistance. Classification models, including logistic regression and machine learning-based models, were constructed. External validation was conducted in an independent cohort from Shengjing Hospital of China Medical University. Results A total of 129 patients were included in the derivation cohort. During index hospitalization, 102 (79.1%) patients responded to IVCS and 27 (20.9%) failed; 18 (14.0%) patients underwent colectomy in 3 months; 6 received cyclosporin as rescue therapy, and 2 eventually escalated to colectomy; 5 succeeded with infliximab as rescue therapy. The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and C-reactive protein (CRP) level at Day 3 are independent predictors of IVCS resistance. The areas under the receiver-operating characteristic curves (AUROCs) of the logistic regression, decision tree, random forest, and extreme-gradient boosting models were 0.873 (95% confidence interval [CI], 0.704–1.000), 0.648 (95% CI, 0.463–0.833), 0.650 (95% CI, 0.441–0.859), and 0.604 (95% CI, 0.416–0.792), respectively. The logistic regression model achieved the highest AUROC value of 0.703 (95% CI, 0.473–0.934) in the external validation. Conclusions In patients with ASUC, UCEIS and CRP levels at Day 3 of IVCS treatment appeared to allow the prompt prediction of likely IVCS resistance. We found no evidence of better performance of machine learning-based models in IVCS resistance prediction in ASUC. A nomogram based on the logistic regression model might aid in the management of ASUC patients. [ABSTRACT FROM AUTHOR]

This paper explores the travel time distribution of different types of urban roads, the link and path average travel time, and variance estimation methods by analyzing the large-scale travel time dataset detected fromautomatic number plate readers installed throughout Beijing. The results show that the best-fitting travel time distribution for different road links in 15 min time intervals differs for different traffic congestion levels. The average travel time for all links on all days can be estimated with acceptable precision by using normal distribution. However, this distribution is not suitable to estimate travel time variance under some types of traffic conditions. Path travel time can be estimated with high precision by summing the travel time of the links that constitute the path. In addition, the path travel time variance can be estimated by the travel time variance of the links, provided that the travel times on all the links along a given path are generated by statistically independent distributions. These findings can be used to develop and validate microscopic simulations or online travel time estimation and prediction systems. [ABSTRACT FROM AUTHOR]

Abstract: The use of groundwater flow models is prevalent in the field of environmental hydrogeology. Models have been applied to investigate a wide variety of hydrogeological conditions. Recently, groundwater models have been applied to predict the fate and transport of contaminants for risk evaluation purposes. In this paper, a transient groundwater flow model in Daxing district of Beijing, China was developed. The conceptual model was built by analyzing the hydrogeological data. Hydraulic conductivities have been calibrated by the steady state model. The storage coefficients are calibrated by the transient model based on the available data observed from 1995 to 2000, which provides insights to understand the dynamic behavior of groundwater systems and to predict spatial-temporal distributions of groundwater levels in responding to changes. The model results help to identify the aquifer properties and to analyze the groundwater flow dynamics, the changes of groundwater levels, in addition, the improvement of the groundwater level monitoring network will be proposed through the analysis of groundwater levels. The calibrated transient model will be used later to predict the impacts of water resources management schemes on groundwater in the study area. [Copyright &y& Elsevier]

Abstract: This paper proposes a method for forecasting the ionospheric critical frequency, f 0F2, 1h in advance, using the support vector machine (SVM) approach. The inputs to the SVM network are the time of day, seasonal information, 2 month running mean sunspot number (R2), 3 day running mean of the 3h planetary magnetic ap index, the solar zenith angle, the present value f 0F2(t) and its first and second increments, the observation of f 0F2 at t−23h, the 30-day mean value at time, t, f mF2 (t) and the previous 30 day running mean of f 0F2 at t−23h f mF2(t−23). The output is the predicted f 0F2 1h ahead. The network is trained to use the ionospheric sounding data at Haikou, Guangzhou, Chongqing, Lanzhou, Beijing, Changchun and Manzhouli stations at high and low solar activities. The performance of the SVM model was verified with observed data. It is shown that the predicted f 0F2 has good agreement with the observed f 0F2. The performance of the SVM model is superior to that of the autocorrelation and persistence models, and that it is comparable to that of the neural network model. [ABSTRACT FROM AUTHOR]

Travel time prediction is playing an increasingly important part in advanced traveler information system (ATIS), which is of great significance to alleviate urban traffic congestion. Although graph convolutional networks have been widely used in road network traffic prediction, spatiotemporal dynamic modeling of urban traffic is still an intractable task. In this study, we propose an improved graph convolutional network (IGC-Net) for travel time prediction. Specifically, we design a modified adjacency matrix by fusing distance and correlation matrix with original adjacency matrix to capture spatial dynamic feature. We then establish three components based on temporal property to capture recent, daily-periodic, and weekly periodic correlations. The comparison experiments with baseline models and variants on a real-world dataset in Beijing are conducted. The results show that the IGC-Net outperforms baseline models in different prediction horizons and has stronger robustness for dynamic traffic prediction. [ABSTRACT FROM AUTHOR]