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Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction. [ABSTRACT FROM AUTHOR]

InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter's experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation. [ABSTRACT FROM AUTHOR]

The construction layout of charging piles restricts the rapidly development of EVs. To address this area, this paper first simulates the current layout of charging piles in Beijing and finds that there is an unbalanced distribution problem of oversupply and idle resources. Then, based on the current situation, this study takes the total charge station construction cost as the objective function and unfolds in three levels, using MIP for conditional constraints. Finally, an improved particle swarm algorithm is employed to simulate electric vehicles by generating demand points in the region to obtain the optimum location of charging stations. This method overcomes the limitation of using static data in traditional research, and the simulation can reflect the dynamic law of electric vehicle operation, which is more consistent with the real situation. The calculation results show that the method adopted in this study can reasonably plan the layout of charging stations, relieve the charging pressure of some charging stations, and minimize the overall service cost of new charging stations based on the continuation of the existing layout. [ABSTRACT FROM AUTHOR]

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

• Propose the concepts of line strategy (LS) and node strategy (NS) and a novel methodology adopting the decision variables of LS and NS proportions. • Impose transfer constraints explicitly and mix all the passengers with the same maximal number of transfers together. • Establish the LNS (line and node strategy) -based model on the original transit networks and reduce the scope of the feasible transfer nodes by the maximal transfer constraints. • Adopt both the method of successive averages (MSA) and a projection algorithm to solve the LNS-based model. • The performance of the LNS-based model and algorithm on a transit subnetwork of Beijing showcases its huge advantage. Passengers traveling on transit networks involve two kinds of decision-making strategies: deciding which lines are attractive at an origin or transfer node (denoted line strategy, LS), or deciding which node to transfer at when riding a line (denoted node strategy, NS). Combining these two strategies, this paper proposes a novel variational inequality formulation for the user equilibrium passenger assignment problem. The inclusion of the NS eliminates the need for passenger assignment on a large augmented graph, reducing the modeling complexity and making it easier to track all passengers' travel routes. Moreover, constraints on the maximal number of transfers—which are crucial in practical decision-making on transit networks—are explicitly included, further drastically reducing the set of passengers' feasible strategies. Furthermore, some extant strategy-based transit assignment models are shown to be a special case of the proposed model when the transfer constraint is removed. Finally, the properties of the proposed model are illustrated on a small network, and the model and algorithm exhibit huge advantages on the chosen transit subnetwork of Beijing. [ABSTRACT FROM AUTHOR]

To address the uncertainty of surgery duration, this paper investigates surgery planning scheduling problem with multiple servers, which proposes chance constraints of operating rooms overtime to guarantee the surgery durations of patients is no more than the time limit of operating rooms with a high probability. A stochastic chance-constrained program is proposed to determine which operating rooms to operate, and surgeries to operating rooms allocation. Based on a finite support set of the surgery duration, this paper introduces 0-1 variables to formulate the chance constraints, and derives 0-1 integer linear program counterpart. To improve the efficiency of the model, this paper presents two classes of valid inequalities and uses the longest path algorithm to separate the second class of valid inequalities, which are implemented in a branch-and-cut framework. Computational experiments based on real-life data from hospital in Beijing are conducted to verify the algorithm performance and determine the optimal planning scheme, so as to take full utilization of healthcare resources, i.e. operating rooms. [ABSTRACT FROM AUTHOR]

Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks. [ABSTRACT FROM AUTHOR]

Railway transport becomes a more popular transportation in many countries due to its large transport capacity, low energy consumption, and benign environment. The passenger train service planning is the key of the rail operations system to balance the transport service and the passenger demand. In this paper, we propose a mixed binary linear programming formulation for the passenger train service planning to optimize the train route, frequency, stop schedule, and passenger assignment simultaneously. In addition, we analyze the computational complexities of the model and develop a Benders decomposition algorithm with valid inequalities to solve this problem. Finally, our model and algorithm are tested on a real-world instance of the Beijing-Shanghai high-speed railway line. The computational results show that our approach can solve these problems within reasonable solution time and small optimality gaps (less than 2.5%). [ABSTRACT FROM AUTHOR]

This paper studies a location-allocation problem to determine the selection of emergency shelters, medical centers, and distribution centers after the disaster. The evacuation of refugees and allocation of relief resources are also considered. A mixed-integer nonlinear multiobjective programming model is proposed to characterize the problem. The hierarchical demand of different refugees and the limitations of relief resources are considered in the model. We employ a combination of the simulated annealing (SA) algorithm and the particle swarm optimization (PSO) algorithm method to solve the complex model. To optimize the result of our proposed algorithm, we absorb the group search, crossover, and mutation operator of GA into SA. We conduct a case study in a district of Beijing in China to validate the proposed methodology. Some computational experiments are conducted to analyze the impact of different factors, such as the target weight setting, selection of candidate shelters, and quantity of relief resources. [ABSTRACT FROM AUTHOR]

• Spatiotemporal usage patterns of station-free shared bikes are explored. • A topic-based two-stage algorithm is proposed to discover city functional regions. • Region functions are labeled by static POI data and dynamic mobility patterns. Station-free shared bike (SFSB) is a new travel mode that shared bikes are allowed to park in any proper places. It implies that the users are more likely to park the SFSB as close as their destinations. This advantage makes the SFSB data to be an ideal source to understand the land-use distribution. Inspired by the idea in text mining, this paper proposes a topic-based two-stage SFSB data mining algorithm to understand the SFSB user's travel behavior and to discover city functional regions. A region, a function and human mobility patterns are treated as a document, a topic and words, respectively. Then, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. The point-of-interest data is combined to annotate the clustered regions to discover the real functions. At last, the proposed method is tested using 14-day SFSB data in Beijing and the results are estimated by the Satellite Map data. The proposed methods and the results can be applied to infer the individual's travel purpose through functional regions and to improve land-use planning, etc. [ABSTRACT FROM AUTHOR]

• A reliable path finding model is proposed to minimize the fuel consumption budget. • Specified on-time arrival probability index is ensured by reliable path finding model. • A heuristic label setting algorithm is developed to exactly solve the formulated problem. • Real traffic data is applied for the applicability and efficiency verification. Transport activities are regarded as a major source of fuel consumption and CO 2 emission production. To reduce the negative impact of traffic-related CO 2 emission, this paper proposes a reliable path-finding algorithm for improving fuel efficiency in stochastic networks under the uncertainty of travel time and fuel consumption with the consideration of spatial correlation. A reliable constrained path-finding model is developed and formulated to minimize the fuel consumption budget while guaranteeing the specified on-time arrival probability. A heuristic label setting algorithm is developed to precisely solve the formulated problem. The proposed algorithm overcomes the time-consuming drawbacks of traditional path enumeration algorithms. The applicability and efficiency of the proposed algorithm are verified on real-world traffic data acquired from the Beijing and Xi'an networks in China. The experiments demonstrate that our proposed algorithm can significantly reduce fuel consumption compared to existing studies. The experiment in Beijing shows that using the proposed algorithm can reduce 0.9 kg of CO 2 emissions on average per trip compared to existing studies. [ABSTRACT FROM AUTHOR]

With the development of economy, energy dilemma and environment protection give urgent pressure on automobile industry. More and more countries are making new energy vehicle policy. How to choose a proper routing when driving an electric vehicle? Does charging stations layout in a city meet the demand of electric vehicles? This paper gives out the electric vehicles' routing optimizing algorithm. The algorithm focus on decision policy before an electric vehicle starts. There are 3 kinds of situations: the situation moving to destination, moving to a charging station and staying origin. Each situation has its judgment and it is discussed in detail. Except that, the electric vehicles' routing optimization is solved in this paper. By applying routing optimizing algorithm, a multi-agent simulation model is build and the model is running with real data of charging stations in Beijing. The simulation results show that this model is valid. Through analysis of simulation data, this paper concludes that the layout of charging stations in Beijing is not reasonable, because charging stations are not visited by electric vehicles equally. 4 charging stations have higher visiting frequency. More charging stations should be built near these areas. [ABSTRACT FROM AUTHOR]

A new Monte Carlo transport code RMC has been being developed by Department of Engineering Physics, Tsinghua University, Beijing as a tool for reactor core analysis on high-performance computing platforms. To meet the requirements of reactor analysis, RMC now has such functions as criticality calculation, fixed-source calculation, burnup calculation and kinetics simulations. Some techniques for geometry treatment, new burnup algorithm, source convergence acceleration, massive tally and parallel calculation, and temperature dependent cross sections processing are researched and implemented in RMC to improve the efficiency. Validation results of criticality calculation, burnup calculation, source convergence acceleration, tallies performance and parallel performance shown in this paper prove the capabilities of RMC in dealing with reactor analysis problems with good performances. [ABSTRACT FROM AUTHOR]

The adverse effects caused by PM2.5 have drawn extensive concern and it is of great significance to identify its spatial distribution. Satellite-derived aerosol optical depth (AOD) has been widely used for PM2.5 estimation. However, the coarse spatial resolution and the gaps caused by data deficiency impede its better application at the urban scale. Additionally, obtaining accurate results in unsampled spatial areas when PM2.5 ground sites are insufficient and distribute sparsely is also a challenging issue for PM2.5 spatial distribution estimation. This paper aimed to develop a model, i.e., spatially local extreme gradient boosting (SL-XGB), combining the powerful fitting ability of machine learning and optimal bandwidths of local models, to better estimate PM2.5 concentration at the urban scale by using Beijing as the study area. This paper adopted simplified high-resolution MODIS aerosol retrieval algorithm (SARA) AOD at 500 m resolution as the major independent variable, hence, ensuring the estimation can be operated at a fine scale. Moreover, the extreme gradient boosting (XGBoost) model was adopted to fill the gaps in SARA AOD, thus improving its availability. Then, based on full-covered SARA AOD and other multisource data, the SL-XGB model, integrating multiple local XGBoost models and particular optimal bandwidths, was trained to estimate PM2.5 concentration. For comparison, SL-XGB and two other models, XGBoost and geographically weighted regression (GWR), were evaluated by 10-fold cross validation (CV). The sample-based CV results reveal that the SL-XGB performed the best as assessed through R2 (0.88), root mean square error (RMSE = 24.08 μg/m3) and mean prediction error (MPE = 16.90 μg/m3). Additionally, SL-XGB also performed the best in the site-based CV with a R2 of 0.86, a RMSE of 26.15 μg/m3 and a MPE of 17.97 μg/m3, which shows its good spatial generalization ability. These results demonstrate that SL-XGB can better simultaneously handle non-linear and spatial heterogeneity issues despite spatially limited data at the urban scale. As far as the PM2.5 concentration distribution was concerned, it presented a gradient increase in PM2.5 concentrations from the northwest to the southeast in Beijing, with abundant spatial details. Overall, the proposed approach for PM2.5 estimation showed outstanding performance and can support preventive pollution control and mitigation at the urban scale. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions. [ABSTRACT FROM AUTHOR]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]

While facing the needs for Vehicle Infrastructure Integration (VII) applications in traffic management, the paper deals with the problem of locating Road Side Units (RSU) for VII deployment. After analyzing the difference between traditional problems of locating traffic information detector and the problem of RSU location, a significance ranking model for RSU localization and three kinds of Significance Degree (SD) computing strategies are put forward. A VII simulation environment for the purpose of RSU localization optimization within VISSIM microscopic traffic simulation software is established developing add-on functions using VISSIM's Component Object Model (COM). A VII test bed of the Olympic Park network in Beijing is taken as an example to evaluate the performance of RSU localization model. The results of simulation experiments indicate that the mixed SD strategy considering both speed and route monitoring is superior to the other two SD strategies. Then, the impact of RSU number and OBE market penetration rate on the evaluation measures of traffic monitoring are studied with reference to the proposed mixed SD strategy. In this case, the evaluation measures of optimized RSU configurations generated by the ranking algorithm are always better than those of random RSU configurations. In addition, the benefits of optimized RSU configurations increase along with RSU number and market penetration rate while the benefits of random RSU configurations are more fluctuant. [ABSTRACT FROM PUBLISHER]

A new harmonic power standard has been developed at the National Institute of Metrology (NIM), Beijing, China, for the calibration of harmonic power analyzers under nonsinusoidal conditions at fundamental frequencies of 50 and 60 Hz. The standard is based on digital sampling techniques that do not require synchronization. A compensation algorithm is presented in this paper. A new definition of the uncertainty for harmonic measurement is proposed that is referred to the fundamental. A characterization signal and its application are introduced. Results have shown that over its operating range of up to 50 A, 500 V, and the sixtieth-order harmonic, the harmonic power standard has uncertainties (k = 2) of less than 30 μV/V, 36 μA/A, and 42 μW/VA for voltage, current, and power measurements, respectively. [ABSTRACT FROM AUTHOR]

Foreign Object Debris (FOD) refers to any foreign material on the airfield that may injure and threaten the aircraft and airport system. Due to the complex background on the airfield pavement and weak target echoes in long-distance monitoring, it is not easy to detect objects of various types and sizes. The existing FOD radar system's detection method has a short effective range, and the detectable objects' radar cross-section intensity is no less than −20 dBsm. In this paper, we propose an integrated FOD automatic target detection algorithm for millimeter-wave (MMW) surveillance radar to improve small target detection under long-range conditions of over 660 m. The signal form of FOD and a clutter model of ground clutter received by millimeter-wave radar are primarily utilized and established theoretically. The runway edge detection means that it is employed based on the in-continuity features as the runway region of interest during the automatic extraction step. Following the clutter map constant false alarm detection algorithm, we utilize a time-domain algorithm that functions as the vital detection processor. Moreover, an explicit definition of the FOD detection performance is developed in a characteristic quantitative way. This criterion involves an absolute reference value for all FOD radar systems. The well-designed FOD frequency-modulated continuous-wave MMW surveillance radar is utilized, and actual experiments are carried out in a real airport in Beijing, China. The results validate the proposed method's effectiveness and the superior performance of FOD target detection in long-range situations. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment's result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections. [ABSTRACT FROM AUTHOR]

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China's New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm. [ABSTRACT FROM AUTHOR]

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO2. Some first retrieval results of NO2 from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO2 show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO2 patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 1015 molec cm−2) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO2 from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO2 VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO2 monitoring. [ABSTRACT FROM AUTHOR]

Abstract: Nuclear technology applications have been very important research fields in Tsinghua University (THU) for more than 50 years. This paper describes two major directions and related projects running in THU concerning nuclear technology applications for radiation imaging and nuclear technology applications for astrophysics. Radiation imaging is a significant application of nuclear technology for all kinds of real world needs including security inspections, anti-smuggling operations, and medicine. The current improved imaging systems give much higher quality radiation images. THU has produced accelerating tubes for both industrial and medical accelerators with energy levels ranging from . Detectors have been produced for medical and industrial imaging as well as for high energy physics experiments such as the MRPC with fast time and position resolutions. DR and CT systems for radiation imaging systems have been continuously improved with new system designs and improved algorithms for image reconstruction and processing. Two important new key initiatives are the dual-energy radiography and dual-energy CT systems. Dual-energy CT imaging improves material discrimination by providing both the electron density and the atomic number distribution of scanned objects. Finally, this paper also introduces recent developments related to the hard X-ray modulation telescope (HXMT) provided by THU. [Copyright &y& Elsevier]