Search

The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

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]

In the face of escalating urban pluvial floods exacerbated by climate change, conventional roof systems fall short of effectively managing precipitation extremes. This paper introduces a smart predictive solution: the Smart Internal Drainage Roof (SIDR) system, which leverages forecasted data to enhance the mitigation of pluvial floods in Central Business District (CBD) areas. Unlike traditional approaches, SIDRs utilize a synergistic combination of Rule-based Control (RBC) and Model Predictive Control (MPC) algorithms, tailored to optimize the operational efficiency of both grey and green roofs. Within the examined 1.3 km2 area in Beijing, China, SIDRs, covering 11% of the site, decreased total flooded areas by 30%–50% and eliminated 60%–100% of high-risk zones during three actual events. Moreover, SIDRs streamlined outflow processes without extending discharge time and reduced flood duration at a high-risk underpass by more than half. The SIDR's distinct features, including a high control resolution of 5 min, integration with existing waterproofs, and advanced 2D dynamic runoff visualization, position it as a scalable and cost-efficient upgrade in urban flood resilience strategies. [Display omitted] • SIDRs employ RBC and MPC algorithms to mitigate pluvial floods in CBDs. • SIDRs reduce 30%–50% of flooded areas and 60%–100% of high-risk zones. • Runoff control dynamics of SIDRs are illustrated on high-precision 2D girds. • SIDRs can streamline outflow processes without prolonging discharge time. [ABSTRACT FROM AUTHOR]

This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper. [ABSTRACT FROM AUTHOR]

Free-floating bike sharing (FFBS) attracts increasing research focusing on usage patterns, determining factors, and integrated transportation. However, existing researchers tend to overlook the variation in usage characteristics over various time ranges, particularly the usage pattern at night. This paper is conducted to fill the gap through a series of analysis approaches on FFSB in Beijing. The characteristics of the usage pattern, including time-varying usage and traveling distance distributions, are initially illustrated. Subsequently, the spatial patterns of FFBS are visualized and thoroughly analyzed in different time ranges and origin-destination (O-D) flows. A statistical model evaluating the environmental effects of FFBS trips revealed the source of FFBS usage. In addition to focusing on the nighttime, the usage patterns varying day and night are compared through the analysis. The findings explain the usage pattern variation and the unique pattern at night, providing valuable insight for improving the management of the FFBS system. [ABSTRACT FROM AUTHOR]

Urban last-mile express delivery in China encounters several challenges. This paper presents the establishment of a sharing logistics center aimed at enhancing the overall efficiency of urban last-mile express delivery while optimizing the utilization of essential resources. The successful implementation of shared delivery within sharing logistics center necessitates the creation of a robust collaborative mechanism. Recognizing that cooperative benefit allocation is dynamically influenced by factors such as resource input, operational efficiency, risk management, and other cost-related considerations, this study introduces a multi-weight H-Shapley value method for benefit allocation. By conducting empirical analyses of urban last-mile express delivery in Beijing within a sharing logistics service framework, our findings reveal that the revised benefit allocation model better aligns with the interests of participating entities and positively correlates with their contributions. Analyzing the impact of delivery volume and express operational costs changes, it is found that when the delivery volume and express operational costs of the sharing logistics center change, the benefits of participating enterprises move in the same direction. The benefit allocation model established in this study enriches the existing body of research in the field of shared delivery and offers valuable insights for benefit allocation issues that necessitate consideration of the dynamic effects of multiple parameter variations. [ABSTRACT FROM AUTHOR]

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry. [ABSTRACT FROM AUTHOR]

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence. [ABSTRACT FROM AUTHOR]

A solar heat pump based on the photovoltaic photothermal (PV/T) module is a new technology that can improve the photovoltaic efficiency and recovery of waste heat in photovoltaic conversion. The comprehensive efficiency of a system can thus be greatly improved. At present, there is little research on the simulation of a solar heat pump based on the Simulink 2018 software. In this paper, PV/T modules are modeled and simulated using the Simulink software based on the typical meteorological parameters in Beijing city during summer and winter conditions. Considering the system characteristics of a simultaneous operation, the models of all components such as the compressor, condenser, evaporator, expansion valve, and heat storage tanks contained in a system are established based on the system's working principle, energy conservation equations, and some empirical formulas and then simulated. As PV/T modules are the key influencing factor for system performance, the model and simulation process are introduced in detail in this paper. The results show that the surface temperature of PV/T modules under different operating conditions are all significantly lower than that of ordinary photovoltaic panels. The average temperature of PV/T modules is only 15.2 °C, which is 20.2 °C lower than that of ordinary panels under summer conditions, and the average temperature of PV/T modules is only 0.5 °C, 17.8 °C lower than that of ordinary panels under winter conditions. The average photoelectric efficiency can be improved by 15.4% and 8.9%. And, the temperature change amplitude of PV/T modules is lower, which weakens the temperature fluctuation of the modules. The photovoltaic efficiency is therefore further increased. As a result, the power generation and photovoltaic efficiency are both improved significantly. [ABSTRACT FROM AUTHOR]

The hosting of large-scale sports events not only brings significant economic benefits and cultural influence to cities and countries, promotes the construction and sustainable development of urban ecological environment, but also brings certain risks to the urban ecological environment. However, research on the issue of risk is still relatively inadequate. This paper uses the method of literature research and empirical analysis, through consulting relevant literature and data, analyzes the relationship between large-scale sports events and urban ecological environment and the risks brought by the urban ecological environment, and puts forward the corresponding preventive measures. It is found that the relationship between large-scale sports events and urban ecological environment is very important, and the two influence and promote each other. However, the hosting of largescale sports events may lead to problems such as water pollution, soil pollution, noise pollution and air pollution, and also consume a lot of resources and energy. In order to reduce the risk brought by large-scale sports events to the urban ecological environment, we should take measures such as comprehensive management, scientific planning, supervision and management, so as to protect the urban ecological environment and promote its sustainable development. Finally, taking the 2008 Beijing Olympic Games as an example, this paper analyzes the scientific practice of promoting urban ecological environment protection by large-scale sports events. The purpose of this study is to provide reference for preventing the risk brought by large-scale sports events to urban ecological environment, and also provide important theoretical and practical significance for the sustainable development of large-scale sports events. [ABSTRACT FROM AUTHOR]

Recent research has played a pivotal role in advancing our understanding of cloisonné enamel production in China during the 19th and 20th centuries. However, Christian workshops, whether operating under missionary subcontracting or owned by the Catholic Church, have yet to be accurately identified and contextualized. This article delves into three significant contexts. Firstly, it identifies and contextualizes the Christian connections and interactions of the Decheng private cloisonné workshop, involving the French Lazarist Bishop Alphonse Favier, in Beijing. Secondly, it identifies the cloisonné workshop stablished by the Lazarists in the Beitang complex in Beijing and elucidates the role it played. Finally, this paper presents new evidence concerning cloisonné Christian objects crafted by the Tushanwan Jesuit workshop in Shanghai. Some of the primary works of these three workshops are identified for the first time. Additionally, this paper shows that certain cloisonné crosses, some of them thought to be originally Japanese, including those referred to as Namban, were, in fact, crafted in Beijing during the 1920s. These preliminary results will contribute to placing Chinese Christian cloisonné within the history of Chinese Art and its interactions at a global level in the context of evangelization in China, the production of the so-called export art, and the processes of indigenization carried out by the Lazarists and the Jesuits. [ABSTRACT FROM AUTHOR]

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

Objective Our objective was to investigate the basic information of the personnel and facilities of nuclear medicine in Beijing. Methods This survey was performed by the Beijing Quality Control Center in 2018. The investigation included personnel, equipment, and clinical applications, and data were then compared with previous surveys. The paper questionnaires were used for the survey, which required information about the personnel, devices, and clinical applications. Results About 38 nuclear medicine departments in Beijing were involved in the survey. The number of nuclear medicine staff was 531 in 2018, showing an increase of 58.7% over the past decade. Positron emission tomography/computed tomography (PET/CT), single-photon emission computed tomography (SPECT), and single-photon emission computed tomography/computed tomography (SPECT/CT) represented the main nuclear medicine facilities, and the total number of surveyed departments was 18, 24, and 34, respectively. The quality control results showed significant improvement from the 2005 levels. The total number of scintigraphy procedures was estimated at 199,607 (153,185 SPECT and 46,422 PET/CT). The estimated annual number of scintigraphy images was 8.9 per 1,000 population for SPECT and 2.7 per 1,000 population for PET/CT during 2018. The most frequent radioiodine-targeted therapy was 131 I-targeted therapy for hyperthyroidism in 2018. Conclusion Nuclear medicine has experienced rapid growth in the past 10 years in Beijing, either in personnel, equipment, and scintigraphy. Future efforts will focus on the use of new isotopes in the diagnosis, implementing quality strategy, and enhancing training. [ABSTRACT FROM AUTHOR]

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules. [ABSTRACT FROM AUTHOR]

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5. [ABSTRACT FROM AUTHOR]

For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this paper is to offer a thorough examination of the clinical presentations, etiology, and treatment strategies associated with perivascular epithelioid cell tumors (PEComas). Methods: This retrospective study examined the comprehensive archival data of PEComa cases diagnosed at Beijing Hospital from 2015 to 2023. The pathology slides of all patients were thoroughly reassessed by two experienced pathologists. A thorough retrospective analysis was undertaken, incorporating clinicopathological data including gender, age at diagnosis, initial clinical manifestations, signs, disease onset site, tumor markers, imaging findings, therapeutic modalities, pathological features, immunohistochemical profiles, treatment responses, and prognostic indicators. Patients were evaluated for disease severity according to established pathological classification criteria and were followed up until the designated analysis cut-off date. In instances where patients were unable to be monitored on-site, they were contacted via telephone for postoperative follow-up inquiries. Results: This study included 11 patients with ages ranging from 17 to 66 years old, presenting with the disease in multiple anatomical sites, including the retroperitoneum (2/11), liver (4/11), kidney (4/11), lung (1/11), and broad ligament of the uterus (1/11). Most patients presented with non-specific clinical symptoms and were subsequently diagnosed with space-occupying lesions upon physical examination. The tumor demonstrated progressive growth and enlargement, which could result in compression of neighboring organs. Preoperative imaging alone is insufficient for a definitive diagnosis of PEComa, but MRI can provide an initial evaluation of the tumor's potential malignancy. Molecular marker testing specific to PEComa, such as HMB-45 (90.0%), SMA (81.8%), Melan-A (90.9%), vimentin (90.9%), and Desmin (36.3%), was conducted on all patients. No adjuvant therapies were administered postoperatively. Upon analysis, no instances of relapse at the primary site or the development of new tumors at other sites were observed. Regular imaging reviews of three patients with malignant PEComa post-surgery showed no evidence of recurrence. Conclusions: The clinical presentation, tumor biomarkers, and imaging characteristics of PEComa lack specificity, necessitating dependence on pathology and immunohistochemistry for precise diagnosis. The mainstay of treatment consists of surgical resection, with patients typically experiencing a favorable prognosis. [ABSTRACT FROM AUTHOR]

The urban underlying surface may affect the thunderstorm process. However, current research on this phenomenon is still in its infancy. This paper aimed to analyze the influence of the urban underlying surface on the evolution of thunderstorm organization through ground observation and numerical simulation. The results indicated that when the thunderstorm system with strong synoptic conditions passed through the built-up area of Beijing, it exhibited obvious bifurcation and detour. The dynamic field of near-surface cold pools could serve as diagnostic indicators for understanding how the urban underlying surface affects the thunderstorm process. The large-scale compact-rise clusters in the city center could alter the movement direction and path of the cold pool outflow, thereby influencing the thunderstorm organization process. In addition to the spatial configuration of the building complex, the city size might also be an important factor influencing the thunderstorm process. This study might provide a fundamental foundation and technical support for predicting and assessing urban thunderstorm disasters. [ABSTRACT FROM AUTHOR]

China's policies about reducing carbon emissions mainly focus on saving energy and reducing pollutant emissions. However, the carbon emissions of the logistics industry remain high. Thus, logistics companies should not only reduce distribution costs, but also consider the impact of carbon emissions when planning distribution routes. This paper studies the supermarket delivery distribution route planning problem considering carbon emissions. Aiming at the lowest economic operating cost composed of fixed cost, driving cost, and carbon emission cost, we propose a grey wolf optimized genetic algorithm to generate delivery route plans, where the social rank and hunting behavior of the grey wolf optimization algorithm are integrated into the selection operation of the genetic algorithm. Finally, a real case of a large third-party logistics enterprise in Beijing is studied. The case study results verify the effectiveness and applicability of the model and algorithm. [ABSTRACT FROM AUTHOR]

The fossils of Hamipterus tianshanensis (Wang et al. in Curr Biol 24:1323–1330, 2014) and their eggs have important scientific significance because they can provide unique information about the reproduction, development, and evolution of pterosaurs. The fossils and the rock surrounding them have, however, been weathered, which including powdering and flaking, since they were relocated from Xinjiang to Beijing. The high content of soluble salts is a significant factor in fossil deterioration because the dissolution–recrystallization process can generate tremendous pressure and lead to decreased mechanical strength. This study evaluated the electrokinetic desalination performance for the fossils, and two types of poultices employed including paper pulp from Bioline® and CKS121 (cellulose: kaolin: sand = 1:2:1, w/w). Mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), ion chromatography (IC), and other methods were applied to evaluate the desalination effect. The surface salt content reduction by applied direct current (DC) was about 70%, and the inner salt content reduction was about 80%. The experimental results suggest that the electrokinetic method is a promising way to desalinate fossils. Nonetheless, cracks appeared in the surrounding rock crack after electrokinetic desalination, which can be explained by the montmorillonite swelling-induced stresses. Pre-consolidation, especially for electro-chemical method may solve the cracking problem for the clay-rich sandstone desalination. [ABSTRACT FROM AUTHOR]

• 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]

Under the background of global climate change, rainstorm and flood disasters have become the most serious cataclysm. Under the circumstances of an increasingly severe risk situation, it is necessary to enhance urban disaster resilience. Based on the disaster resilience process of prevention, absorption, and enhancement, and considering the safety factors such as personnel, facility, environment and management, this paper forms a dual dimension of the urban disaster resilience assessment model covering the key elements of urban disaster response and the core capacity of urban disaster recovery. Furthermore, if taking into account the characteristics of rainstorm and flood disasters, the paper screens the key indicators to build up an assessment index system of an urban rainstorm and flood disaster. The practical application was implemented in Beijing to have an assessment of the ability to recover from rainstorm and flood disasters in all districts of Beijing. And then, some pertinent suggestions for enhancing the resilience of Beijing to rainstorm and flood disasters were proposed. [ABSTRACT FROM AUTHOR]

This study presents a novel bus charging station planning problem considering integrated photovoltaic (PV) and energy storage systems (PESS) to smooth the carbon‐neutral transition of transportation. This paper illustrates a two‐stage stochastic programming model capturing the uncertainty of PV power outputs and designs a step‐wise solution approach in which a conventional charging station location problem is solved in the first step and an improved L‐shaped algorithm is developed in the second step to determine charging station locations with PESS. The numerical experiments indicate that the step‐wise solution approach can provide high‐quality solutions within acceptable computational time. A case study is performed using a real‐world transit network in Beijing, China, with 34 bus routes and 15 candidate bus charging stations. Compared with the benchmark model, both recharging cost and carbon emission are reduced considerably. This paper provides novel insights into the development of integrated transportation and renewable energy systems. [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]

The performance of general circulation models (GCMs) in simulating horizontal winds is important because the distribution and variation in horizontal winds are central to investigating atmospheric dynamic characteristics and processes. Also, horizontal wind data can be used to extract some of the required information on gravity waves, tides, and planetary waves. In this context, the present paper evaluates the capability of the Institute of Atmospheric Physics atmospheric general circulation model high-top version (IAP-HAGCM) in simulating the horizontal winds and tides of the troposphere and lower stratosphere by presenting a climatological and statistical comparison against observations of the powerful Beijing mesosphere–stratosphere–troposphere (MST) radar (39.78°N, 116.95°E) during 2012–2014. The results illustrated that the IAP-HAGCM can successfully reproduce the time–altitude distribution of the monthly mean zonal wind and diurnal tide amplitude, albeit with some underestimation. The mean correlation coefficients and root-mean-square error for the zonal (meridional) winds were 0.94 (0.73) and 6.60 m s−1 (2.90 m s–1), respectively. Additionally, the IAP-HAGCM can capture the temporal variation in both the zonal and meridional winds. It is worth noting that, compared with the seven coupled model intercomparison project phase 6 (CMIP6) models, the IAP-HAGCM performs better in meridional wind simulations below 15 km. However, there are discrepancies in altitudinal ranges with large wind velocities, such as the westerly jet, in the transition region of the troposphere and stratosphere, and in February, April, July, and September. It is suggested that model users should take advantage of the model's simulation ability by combining this information regarding when and where it is optimal with their own research purposes. Moreover, the evaluation results in this paper can also serve as a reference for guiding improvements of the IAP-HAGCM. [ABSTRACT FROM AUTHOR]

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF). [ABSTRACT FROM AUTHOR]

Ultraviolet radiation (UV) has strong chemical and biological effects on human health and ecosystems, and it plays an important role in the atmospheric environment by affecting photochemical processes, etc. Clouds and aerosols are the main factors affecting UV radiation and analyzing the quantitative impact of them on UV radiation is of great significance. Using the observation data of UV radiation in Beijing from 2005 to 2020, as well as the data of aerosol optical depth (AOD), single scattering albedo (SSA), and other related parameters, this paper simulated the surface UV radiation in two scenarios of cloudless without aerosol and cloudless with aerosol based on the TUV (Tropospheric Ultraviolet-Visible model), and quantitatively evaluated the attenuation of UV radiation by aerosol and cloud in the Beijing area. The results show that UV radiation is more sensitive to changes in AOD. Fixing the SSA value to 0.9, when the AOD increases from 0.2 to 1.0, the UV radiation decreases from 21.16 W/m2 to 12.64 W/m2 at 12:00; when AOD is maintained at 0.64, the SSA increases from 0.7 to 0.95, and the UV radiation increases from 14.55 W/m2 to 19.91 W/m2. The average annual attenuation rates of ultraviolet radiation by aerosols and clouds from 2005 to 2020 are 30.64% and 40.22%, respectively; the monthly averaged attenuation rates are 30.48% and 42.04%, respectively; and the daily averaged attenuation rates are 31.02% and 50.45%, respectively. [ABSTRACT FROM AUTHOR]

Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors' preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection. [ABSTRACT FROM AUTHOR]

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations. [ABSTRACT FROM AUTHOR]

To predict and explore the return period wind speed distribution in Beijing urban area, based on the short-term measured wind speed record, the measured independent storm peak samples are extracted and the Monte–Carlo numerical simulation method is used to randomly generate pseudo-independent storm peak samples with the same distribution as the independent storm peak samples. In addition, the appropriate threshold is used to select and extract the over-threshold independent storm peak samples from the measured samples. And, then, the return period wind speed of the whole wind direction is predicted. Further, the traditional Cook method is used to predict and analyze the return period wind speed of each wind direction according to the measured wind speed data. The results show that the return period wind speed prediction results of the whole wind direction corresponding to the simulated pseudo samples and the measured over-threshold samples are basically close to each other, and the maximum relative deviation is less than 7%. The mean relative deviation of the return period wind speed prediction results is about 10.6% between each wind direction and the whole wind direction, and the mean relative deviation is about 9.4% between the RPWS prediction result of each wind direction and the design value of the code. In addition, the return period wind speed prediction values of the west by southwest, northwest and north by northeast wind directions exceed the design value of the code, while the predicted values of the other wind directions are within the envelope range of the design value of the code. Finally, the research content of this paper can provide some reference for the wind resistance design analysis of building structures in Beijing area. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is a highly photochemically reactive gas, has a lifetime of only a few hours, and at high concentrations is harmful to human beings. Therefore, it is important to monitor NO2 with high-precision, time-resolved instruments. To this end, a Pandora spectrometer has been installed on the roof of the laboratory building of the Aerospace Information Research Institute of the Chinese Academy of Sciences in the Olympic Park, Beijing, China. The concentrations of trace gases (including NO2 , HCHO , O3) measured with Pandora are made available through the open-access Pandora database (https://data.pandonia-global-network.org/Beijing-RADI/Pandora171s1/ , last access: 11 July 2023). In this paper, an overview is presented of the Pandora total and tropospheric NO2 vertical column densities (VCDs) and surface concentrations collected during the first year of operation, i.e., from August 2021 to July 2022. The data show that NO2 concentrations were high in the winter and low in the summer, with a diurnal cycle where the concentrations reached a minimum during the daytime. The concentrations were significantly lower during the 2022 Winter Olympics in Beijing, showing the effectiveness of the emission control measures during that period. The Pandora observations show that during northerly winds, clean air is transported to Beijing with low NO2 concentrations, whereas during southerly winds, pollution from surrounding areas is transported to Beijing and NO2 concentrations are high. The contribution of tropospheric NO2 to the total NO2 VCD varies significantly on daily to seasonal timescales; i.e., monthly averages vary between 50 % and 60 % in the winter and between 60 % and 70 % in the spring and autumn. A comparison of Pandora-measured surface concentrations with collocated in situ measurements using a Thermo Scientific 42i-TL analyzer shows that the Pandora data are low and that the relationship between Pandora-derived surface concentrations and in situ measurements is different for low and high NO2 concentrations. Explanations for these differences are offered in terms of measurement techniques and physical (transport) phenomena. The use of Pandora total and tropospheric NO2 VCDs for validation of collocated TROPOspheric Monitoring Instrument (TROPOMI) data, resampled to 100 m × 100 m , shows that although on average the TROPOMI VCDs are slightly lower, they are well within the expected error for TROPOMI of 0.5 Pmolec.cm-2 + (0.2 to 0.5) ⋅ VCDtrop (1 Pmolec.cm-2 = 1 × 10 15 moleccm-2). The location of the Pandora instrument within a sub-orbital TROPOMI pixel of 3.5 km × 5.5 km may result in an error in the TROPOMI-derived tropospheric NO2 VCD between 0.223 and 0.282 Pmolec.cm-2 , i.e., between 1.7 % and 2 %. In addition, the data also show that the Pandora observations at the Beijing-RADI site are representative of an area with a radius of 10 km. [ABSTRACT FROM AUTHOR]

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively. [ABSTRACT FROM AUTHOR]

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing's subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing's Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention. [ABSTRACT FROM AUTHOR]

To efficiently handle the projected growth in air traffic, operational concepts such as "corridors-in-the-sky" have been developed in future Air Traffic Management (ATM) plans. Corridors-in-the-sky are defined as reserved airspace where aircraft move in a common direction along parallel lanes on high flight levels. The on and off ramps play an important role in connecting this airspace structure to the jet route. This paper addresses the scheduling problem for flight sequences entering the corridors-in-the-sky via the on-ramp. To satisfy the requirements of various stakeholders, this paper proposes time-based and cost-based aircraft scheduling optimization models for the on-ramp of the corridors-in-the-sky. An improved Particle Swarm Optimization (PSO) algorithm is also developed to solve a case study of the air route B215 in the Beijing control area. Computational results indicate that the optimized models have advantages in reducing the delay time and cost for both single aircraft and flight sequences, compared with the First-come, First-served (FCFS) approach. [ABSTRACT FROM AUTHOR]

The ratio of the total electron content (TEC) to the F2-layer peak electron density (NmF2) is known as the ionospheric equivalent slab thickness (EST, also known as τ), and it is a crucial indicator of the ionosphere. Using TEC and NmF2 data from the years 2010 to 2017, this work conducts a comprehensive statistical analysis of the ionospheric slab thickness in Beijing, which is in the midlatitude of East Asia. The outcomes show that the τ have different diurnal variations at different seasons for high/low-solar-activity years. On the whole, daytime τ significantly greater than nighttime τ in summer, and it is the opposite for the τ in winter regardless of the solar cycle, whereas the τ during equinox shows different morphology for high/low-solar-activity years. Specifically, daytime τ is larger than nighttime τ during equinox in years of high-solar activity, while the opposite situation applies for the τ during equinox in years of low-solar activity. Moreover, the pre-sunrise and post-sunset peaks are most pronounced during winter for low-solar-activity years. In summer, there is a great increase in τ during the morning hours when compared with other seasons. Furthermore, the τ decreases with the solar activity during nighttime, whereas it seems there is no correlation between daytime τ and solar activity. This paper explained the primary diurnal variations in τ across different seasons during high-/low-solar-activity years by analyzing relative fluctuations of TEC and NmF2 throughout the corresponding period. In addition, based on the disturbance index (DI), which is calculated by instantaneous τ and its corresponding median, this paper found that the storm-time τ might increase when compared with its median value during the daytime, while it may both increase and decrease during the nighttime, especially around dawn and dusk hours. To further analyze the physical mechanism, an example on 2 October 2013 is also presented. The results indicate that the positive disturbance of τ during the main phase of a geomagnetic storm might be caused by the prompt penetration electric field and neutral wind during the storm, and the τ increases during the early recovery phase might be due to the disturbance dynamo electric field as well as the neutral wind during the storm. Moreover, there is a negative disturbance of τ in the recovery phase during the most disturbed sunrise hours, and it might be due to the electric field reversal, neutral wind or other factors during this period. This paper notes the differences of τ in midlatitude between different longitudinal sectors from the related climatology and storm-time behavior, as it would be helpful to improve the current understanding of τ at midlatitudes in East Asia. [ABSTRACT FROM AUTHOR]

In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr−1, ±28.91% yr−1, ±126.15% yr−1, and ±43.58% yr−1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr−1, −2% yr−1, −26% yr−1, and −4% yr−1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory. [ABSTRACT FROM AUTHOR]

In the process of development, global economies are prioritizing environmental protection and firms are also recognizing the importance of minimizing environmental impact during production along with maximization of profits through green investments. It is vivid that green investments are vital for environmental preservation. So this paper contributes to literature by investigating the role of internal and external factors affecting the decision making of Chinese firms regarding adoption of green investments and impact of green investments on environmental, social, and economic performance of firms. The data is collected from directors/senior managers of the firms. We received 463 valid responses from listed companies with Shenzhen, Beijing, and Shanghai Stock Exchange. The "structural equation modeling" with "maximum likelihood estimation" is employed for empirical analysis. The empirical findings reveal that adaptation to climate change and its mitigation is the most important driver of green investment. Moreover, green investment positively contributes to enhancing the social, economic, and environmental performances of Chinese firms. Based on the findings of the study, green investment should be adopted as a corporate strategy by firms for profit maximization, competitive advantage, and improvement in social well-being without compromising the environment. Policy makers can promote green investment by offering policy instruments such as tax incentives, guaranteed credits, grants, and investor education. Training courses may be offered to raise environmental awareness among firms and the general public. [ABSTRACT FROM AUTHOR]

The traction load of electrified railways is a special user of the power system, which will have a significant impact on and pose a challenge to the power system after grid connection. Considering the impact and fluctuation of traction load connected to the power grid, this paper proposed a method to study the influence of traction load on the transient stability of the power grid based on parameter identification. Firstly, according to the operation characteristics of traction load, a comprehensive traction load model based on the measurement-based method of induction motor parallel power function model is selected, and the objective function of parameter identification is determined. Then, the specific steps of using the improved gray wolf optimizer (IGWO) algorithm to achieve the parameter identification of the traction comprehensive load model are introduced, including chaotic map, nonlinear convergence factor, and individual position update. Next, with the IEEE39 bus system as the network background, the load parameters identified by the measured data of a line of the Beijing–Shanghai high-speed railway are imported into the Power System Analysis Software Package (PSASP) 7.0 for transient simulation to analyze the impact of traction load on the transient stability of the power grid. Finally, three typical load models are compared with the traction load model to draw relevant conclusions, and it is verified that the traction load characteristics are different from the general load model, which provides reference for the connection of electrified railway traction load to the power grid and the economic and technical construction of the power system. [ABSTRACT FROM AUTHOR]

Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution. [ABSTRACT FROM AUTHOR]

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method. [ABSTRACT FROM AUTHOR]

High-data-throughput and multimodal-acquisition experiments will prevail in next-generation synchrotron beamlines. Orchestrating dataflow pipelines connecting the data acquisition, processing, visualization and storage ends are becoming increasingly complex and essential for enhancing beamline performance. Mamba Data Worker (MDW) has been developed to address the data challenges for the forthcoming High Energy Photon Source (HEPS). It is an important component of the Mamba experimental control and data acquisition software ecosystem, which enables fast data acquisition and transmission, dynamic configuration of data processing pipelines, data multiplex in streaming, and customized data and metadata assembly. This paper presents the architecture and development plan of MDW, outlines the essential technologies involved, and illustrates its current application at the Beijing Synchrotron Radiation Facility (BSRF). [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive site survey of the Delingha site on the northern Tibetan Plateau from 2012 to 2014. In this campaign, we investigated 24 h continuous astronomical seeing, meteorological properties, and |$C_{n}^{2}$| in the ground layer (GL). The results show that the median seeing is 3.46 arcsec during the day and 1.80 arcsec during the night. The best nighttime seeing occurs in summer (1.66 arcsec), while the worst occurs in autumn (1.96 arcsec). Seeing exhibits obvious diurnal behaviour, with the worst seeing occurring around mid-day (11:00 to 14:00, Beijing time), and the best seeing just before sunset (18:00 to 20:00, Beijing time). Moreover, the Delingha site has good meteorological properties for astronomical observations during the measurement period, especially the low median wind speed (2.8 m s−1) and great temperature inversion (day: −0.3 K, night: 0.6 K). As for the vertical distribution of |$C_{n}^{2}$|⁠ , the exponential h −0.46 dependence of |$C_{n}^{2}$| varying with height in the GL is also obtained, and its power index is significantly related to the seasons. Furthermore, we calculated the contribution of optical turbulence in the GL to the whole atmosphere. Turbulence within the GL contributes significantly in summer, and its contribution can reach more than 35 per cent. Finally, we present the complete dependence analysis of seeing on meteorological parameters at the Delingha site. The results show that the best seeing tends to occur when the temperature inversion is sufficiently large, but the dependence of seeing on wind speed is not significant. [ABSTRACT FROM AUTHOR]

During the "Parade Blue" period in 2015, Beijing and its surrounding areas implemented mandatory temporary source control strategies, which provided experimental conditions for studying the effects of source emission control measures on the sources of atmospheric PM2.5. Analyzing the source contribution rate of the main particulate matters via the source analysis method of the receptor model is an important method for studying the atmospheric pollution. Previous studies on the "Parade Blue" period only discussed the change in PM2.5 concentration during the source control period and the source non-control period, but did not select appropriate emission sources and acceptor components to analyze the contribution ratio of each emission source to fine particulate matter pollution. In this paper, based on the receptor component spectrum and emission source component spectrum, the chemical mass balance receptor model was used for source analysis. The results showed that outdoor PM2.5 concentration was 26.31 μg/m3 in the source control period, which was less than the 40.08 μg/m3 in the source non-control period. In the source control period, motor vehicle emissions, industrial combustion and urban fugitive dust contributed significantly to the fine particulate pollution, accounting for 76–81%, 8–9% and 11–16%, respectively. In the source non-control period, the contributions of motor vehicle emissions and industrial combustion accounted for 57–59% and 41–43%, respectively, significantly higher than that of urban fugitive dust, which accounted for less than 2%. The correlation between the contribution rate of emission sources and the source control strategy was studied and analyzed during the "Parade Blue" period, and the effectiveness of the source control strategy was proved. [ABSTRACT FROM AUTHOR]

With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway transport. Concurrently, the advent of the ATO system provides a notable impact on real-time rescheduling during disruptions, as it equips dispatchers with precise insights into train operation statuses. This paper is dedicated to a thorough analysis of how the transition to automatic driving in train operations influences the real-time rescheduling model. Based on the distinctive impact of the ATO system on real-time rescheduling, we have proposed a mixed-integer linear programming model that combines train re-timing, reordering, and the minimization of passenger delays. To validate the effectiveness of our model, we present several experiments conducted using data from the Beijing–Shanghai high-speed railway line. The results unequivocally demonstrate that our ATO-based model significantly mitigates train delay time, demonstrating its practical value in optimizing high-speed railway operations. [ABSTRACT FROM AUTHOR]

Suburban railways in megacities exert a pivotal role in propelling urbanization and shaping urban agglomeration. However, previous study endeavors have overlooked the transformations occurring in urban expansion along suburban railways, with a particular dearth of attention on the spatio-temporal evolution of landscape ecology and urban function. Therefore, this study employs the megacity of Beijing as an example. It utilizes remote sensing and point-of-interest (POI) data spanning from 2008 to 2022 to construct an indicator system from two essential dimensions: urban form and function. We explored the spatio-temporal characteristics of alterations in urban expansion within the gradient buffer zone adjacent to the suburban railway network in Beijing. The results showed that: (1) The rates of urban expansion were highest in 2008–2013 and lowest in 2013–2018; moreover, suburban railways had the greatest impact on the built-up area within 2–4 km along the route, and the impact gradually decreased beyond 4 km. (2) The direction of urban expansion shifted northward in the direction of latitude and eastward in the direction of longitude from 2008 to 2022, with the shift in latitude being more distinct. (3) The number of urban functions gradually increased from 2008 to 2018, but the number of medical services suddenly increased and the number of other urban functions decreased from 2018 to 2022; in addition, urban functions other than scenic spots were mainly distributed in the main urban areas, with very few clusters distributed near stations. (4) The landscape shape index became more irregular and fragmented from the center along the route to the edge of the buffer zone from 2008 to 2013, and the degree of fragmentation was highest in the 2–4 km buffer zone. In summary, this paper analyzes the spatio-temporal characteristics of urban expansion along suburban railways through four indexes, namely expansion rate, expansion direction, urban function, and landscape shape, and the results of this study are of great significance to the development and planning of suburban railways in megacities. [ABSTRACT FROM AUTHOR]

Based on the provincial statistical data of the Beijing-Tianjin-Hebei region this paper uses the health distance model and obstacle degree model to explore the spatiotemporal mismatch of the development of the Beijing-Tianjin-Hebei regional innovation chain and analyzes the obstacle factor affecting the healthy development of Beijing-Tianjin-Hebei innovation chain. The results show that: (1) In terms of timing characteristics, the overall mismatch of the innovation chain in the Beijing-Tianjin-Hebei region shows a downward trend, and the absolute and relative difference of regional mismatch both show a "V" shaped development trend, which first decreases and then increases. (2) In terms of spatial characteristics, the mismatch of the innovation chain in the Beijing-Tianjin-Hebei region develops from unbalanced to balanced and then unbalanced, and the spatial pattern evolves from low in the middle and high around to high in the southeast and low around. (3) In terms of the obstacle factors, scientific research and development institutions, the number of college students, and the number of innovation platforms at or above the provincial and ministerial level are the obstacle factors to the more frequent occurrence. Plain Language Summary: preface This study aims to examine the spatio-temporal differentiation of the regional innovation chain in the Beijing-Tianjin-Hebei region of China. It investigates the mismatch between different components of the innovation chain and analyzes the factors that hinder its healthy development. The research utilizes the entropy method, health distance model, and obstacle degree model to calculate and analyze the innovation chain mismatch. Arc GIS geometric method is employed to divide the mismatch into different levels. Data from 2015 to 2019 is used to assess the evolution of the innovation chain mismatch over time and across different regions. The study reveals that the innovation chain mismatch in the Beijing-Tianjin-Hebei region has shown a declining trend during the study period. The overall mismatch has weakened, indicating significant progress in the coordinated development of the region. Beijing has played a leading role, while Tianjin and Hebei Province have also witnessed improvements, although to a lesser extent. The development difference among the three regions has been reduced, but significant disparities still exist. The findings highlight the importance of regional collaboration in promoting innovation chain development. The study provides insights for policymakers and stakeholders to enhance the coordination and integration of innovation activities in the Beijing-Tianjin-Hebei region. It emphasizes the need for further efforts to reduce development disparities and enhance the efficiency of innovation development. It is important to note that this study focuses specifically on the Beijing-Tianjin-Hebei region, and the findings may not be directly applicable to other regions or countries. The analysis is based on available data and selected models, which may have certain limitations and assumptions. Further research is needed to explore additional factors and dynamics influencing the innovation chain in the region. [ABSTRACT FROM AUTHOR]

Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety. [ABSTRACT FROM AUTHOR]

Nowadays, sustainability-related issues have attracted growing attention due to fossil fuel depletion and environmental concerns. Considering many cities have gradually replaced taxis with electric vehicles (EVs), to reduce greenhouse gas emissions and traditional energy consumption, this paper studies the optimization strategy of battery swapping for electric taxis (ETs), and it is not only to ease congestion in the battery swapping station (BSS) but also for electric taxis to address their range anxiety and maximize their benefits. Firstly, based on the road network, the Dijkstra algorithm is adopted to provide the optimal path for ETs to BSSs with the minimum energy consumption. Then, this paper proposes the optimization objective function with minimum cost, which contains the battery service cost based on the battery's state of charge, waiting cost caused by waiting for swapping battery in BSSs and the carbon emission reduction benefit generated during ETs driving to BSSs, and uses a mixed-integer linear programming (MILP) algorithm to solve this function. Finally, taking the Leisure Park of Laoshan City in Beijing as an example, the numerical simulation is carried out and the proposed battery swapping strategy is efficient to alleviate the congestion of BSSs and maximize the total benefit of ETs, and the cost based on the proposed strategy is 14.21% less than that of disorderly swapping. [ABSTRACT FROM AUTHOR]