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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]

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]

This paper conducts a comparative study of the scientific publications of the 31 province‐level regions of Mainland China during 2006–2018. The main results are as follows. First, the four indices, ordered by total number of papers indexed, across all provinces, are the Science Citation Index (SCI), Chinese Science Citation Database (CSCD), Chinese Social Science Citation Index (CSSCI), and Social Science Citation Index (SSCI). The number of international publications from these regions shows an increasing trend, while the number of domestic research outputs shows a fluctuating trend. Second, the international and domestic publications produced by these regions greatly improved over the study period. Third, different regions vary greatly not only in research output but also in research preference. The top five preferred disciplines of international and domestic academic achievement are very different; the former all belong to the natural sciences, while more than 7 of 10 of the latter belong to the social sciences. Moreover, the adjacency matrices of the similarity of the 31 regions show that there is some correlation between the research spotlight of a region and its economic and cultural patterns. In general, scientific research in China has developed rapidly in recent years, and different regions vary greatly not only in research achievement but also in research preference. Key points: Scientific output is unevenly distributed between Chinese provinces, with Beijing, Jiangsu, Shanghai, Hubei, and Shaanxi each contributing almost half total output.The majority of Chinese‐authored articles in international journals are in STM areas, with national journals capturing the majority of social science articles.Beijing has the highest number of articles in Q1 and Q2 journals within Web of Science, followed by Jiangsu and then Shanghai.The different regions of china specialize in different academic disciplines, with some universities excelling social sciences and others in pure and applied sciences. [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]

Featured Application: The LM-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the LM algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current changes. With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. [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]

In response to the international appeal of developing low-carbon economy and realizing carbon peaking and neutrality goals, China has been exploring the construction of the carbon emissions trading market for years. Without the guidance of advanced technology, there would not be a low-carbon economy in the first place. Based on panel data of 30 provinces and cities in China from 2005 to 2020, this paper takes Beijing—which is the first pilot city in China—as the only treated group and uses the Synthetic Control Method to study the impact of the pilot policy on low-carbon technology innovation. The results show that, firstly, the number of low-carbon technology patents in Beijing increases significantly after the implementation of the pilot policy, proving a positive influence on technology innovation. Secondly, the policy effect has a certain time lag and is sensitive to the shock from both domestic and foreign carbon market, but this effect is gradually stable over time. This paper confirms that technological innovation is the key means of promoting the development of the low-carbon economy and calls on various carbon trading markets to pay attention to the internal mechanism of promoting low-carbon technology innovation to stimulate the vitality of market entities. [ABSTRACT FROM AUTHOR]

The coordination and stability of the regional economy (RE) and ecological quality (EQ) are essential for the synergistic development of the Beijing–Tianjin–Hebei urban agglomeration (BTH). In this paper, based on remote sensing data and economic statistics data, this paper analysed the current situation and evolution of the RE and EQ in the BTH region. Based on the coupled coordination degree (CCD) model, this paper has developed an evaluative framework and quantifies the spatiotemporal coupled coordination of the RE and EQ in the BTH region from 2000 to 2020. The results show that: (1) in 2020, the GDP of the poverty-stricken counties (PSCs) around BTH accounted for 4.57% of the total BTH region, and the area of high-quality ecosystems, Net Primary Productivity (NPP), soil conservation, water conservation, and sand fixation accounted for more than 40% of the total BTH region; (2) the GDP growth of the PSCs accounted for 4.45% of the total growth in BTH, the reduced area of high-quality ecosystems accounted for 21.04%, and the increase in NPP, soil conservation, water conservation, and sand fixation accounted for more than 40% of the total growth in the BTH region; (3) the CCD of the RE and EQ in the BTH region showed a tendency to increase, but the gap between the regions gradually expanded, and the development type changed from a lagging RE to a lagging EQ, in which the coupled coordination grade of the PSCs was in a dissonance stage for a long time, and the level of economic development had to improve. Deepening the understanding of the spatiotemporal coupling relationship between the RE and EQ and adopting appropriate development policies is of great significance for achieving the coordinated and integrated development of the BTH region. [ABSTRACT FROM AUTHOR]

In the knowledge economy era, innovation has become a key emphasis for urban competitions. This paper constructs a theoretical research framework that integrates the basic understandings, influencing factors and ensuing results of intercity innovative competition relations. On the basis of data from the general programs of the National Natural Science Foundation of China from 2005 to 2019, this paper constructs intercity innovative competition relations in China, analyses their spatial distribution and quantitative characteristics, and quantitatively investigates the impact of urban innovation capacity and multidimensional proximity (e.g., geographical proximity, institutional proximity and cognitive proximity) on intercity innovative competition relations through a negative binomial model. The study obtained the following findings: (1) In terms of the overall intercity innovative competition relations, the intensity of China's intercity innovative competition relations gradually increased from 2005 to 2019, with a spatial clustering towards cities with high administrative ranks (e.g., municipalities directly under the central government, sub-provincial cities and provincial capitals); Beijing is always at the centre of innovative competition relations, but its standing has slightly slipped in recent years. (2) From the perspective of disciplines, cities can become benchmarks in particular fields of innovative competitions by competing according to their disciplinary strengths; intercity innovative competition relations in China vary across various academic disciplines. (3) In terms of influencing factors, urban innovation capacity has significant positive effects on intercity innovative competition relations; geographical proximity, institutional proximity and cognitive proximity all have significant positive effects on innovative competition relations; and interactions occur between multidimensional proximities, including a complementary effect between geographical proximity and institutional proximity, a substitutive effect between cognitive proximity and geographical proximity, and a substitutive effect between cognitive proximity and institutional proximity. [ABSTRACT FROM AUTHOR]

Purpose: To study the relationship between three variables, namely, the number of supermarkets around, the average distance to medical institutions designated to be covered by basic medical insurance, and the presence of parks around, and the sustainable development of elderly care institutions.Patients and Methods: This paper is based on the census database of elderly care institutions in Beijing. We ran a self-developed Python program to mine the database after adding three variables, namely, the number of supermarkets around, the average distance to medical institutions designated to be covered by basic medical insurance (hereinafter referred to as "designated medical institutions"), and the presence of parks around, and then used the least squares method in regression analysis.Results: The number of years of depreciation for fixed assets had some degree of influence on the occupancy rate of an elderly care institution under sustained operation. The occupancy rate of above 80% was efficient for an elderly care institution. There was a progressive relationship between the external spatial factors and the internal resources of an elderly care institution in terms of influencing the occupancy rate of the institution. Optimizing the spatial distribution of elderly care institutions should first optimize the choice of location.Conclusion: The occupancy rate of an elderly care institution was closely related to the convenience of living conditions and that site selection was crucial for such a facility. We arrived at two recommendations for the future development of elderly care institutions, 1. Reducing the number of the institutions in areas with unfavorable geographic positions and serving senior care needs within an appropriate close distance, so as to better optimize the spatial distribution of senior care facilities, improve the efficiency of resource allocation. 2. Strike a balance between the economic benefits of operating entities and the social benefits associated with older people. [ABSTRACT FROM AUTHOR]

Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern. Moreover, the visualization methods are weak in wholeness, intuitiveness and interactivity perspectives. In this paper, we design a geospatial dashboard-based student behavior analysis and visualization system considering students characteristics and spatiotemporal pattern. This system includes four components: user monitoring, data mining analysis, behavior prediction and spatiotemporal visualization. Furthermore, a deep learning model based on LSTNet to predict student behaviour. Our work takes WiFi log data of a university in Beijing as dataset. The results show that this system can identify student behavior patterns at a finer granularity by visualization method, which is helpful in improving learning and living efficiency. [ABSTRACT FROM AUTHOR]

Nations worldwide are mobilizing to harness the power of Artificial Intelligence (AI) given its massive potential to shape global competitiveness over the coming decades. Using a dataset of 2.2 million AI papers, we study inter-city citations, collaborations, and talent migrations to uncover dependencies between Eastern and Western cities worldwide. Beijing emerges as a clear outlier, as it has been the most impactful city since 2007, the most productive since 2002, and the one housing the largest number of AI scientists since 1995. Our analysis also reveals that Western cities cite each other far more frequently than expected by chance, East–East collaborations are far more common than East–West or West–West collaborations, and migration of AI scientists mostly takes place from one Eastern city to another. We then propose a measure that quantifies each city's role in bridging East and West. Beijing's role surpasses that of all other cities combined, making it the central gateway through which knowledge and talent flow from one side to the other. We also track the center of mass of AI research by weighing each city's geographic location by its impact, productivity, and AI workforce. The center of mass has moved thousands of kilometers eastward over the past three decades, with Beijing's pull increasing each year. These findings highlight the eastward shift in the tides of global AI research, and the growing role of the Chinese capital as a hub connecting researchers across the globe. [ABSTRACT FROM AUTHOR]

With the rapid development of the energy industry and the growth of the global energy demand in recent years, the development of the photovoltaic industry has become increasingly significant. However, the development of the PV industry is constrained by high land costs, and land in central cities and industrial areas is often very expensive and unsuitable for the installation of PV equipment in large areas. With this background knowledge, the key to evaluating the PV potential is by counting the rooftop information of buildings, and an ideal solution for extracting building rooftop information is from remote sensing satellite images using the deep learning method; however, the deep learning method often requires large-scale labeled samples, and the labeling of remote sensing images is often time-consuming and expensive. To reduce the burden of data labeling, models trained on large datasets can be used as pre-trained models (e.g., ImageNet) to provide prior knowledge for training. However, most of the existing pre-trained model parameters are not suitable for direct transfer to remote sensing tasks. In this paper, we design a pseudo-label-guided self-supervised learning (PGSSL) semantic segmentation network structure based on high-resolution remote sensing images to extract building information. The pseudo-label-guided learning method allows the feature results extracted by the pretext task to be more applicable to the target task and ultimately improves segmentation accuracy. Our proposed method achieves better results than current contrastive learning methods in most experiments and uses only about 20–50% of the labeled data to achieve comparable performance with random initialization. In addition, a more accurate statistical method for building density distribution is designed based on the semantic segmentation results. This method addresses the last step of the extraction results oriented to the PV potential assessment, and this paper is validated in Beijing, China, to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Cities serve as both political and economic hubs. Sustainable development has long been acknowledged as crucial to the well-being of the environment, people, and society. In order to improve the current state of spatial affairs and attain long-term resilience, humanity is looking for reliable and sustainable urban planning approaches. Urban agriculture has received a lot of attention in recent years as an enduring and pervasive kind of landscape. Although the contribution of urban agriculture has been well documented in many studies on economic, social and ecological aspects, there has been little discussion of its practical value as a tool for spatial development. Additionally, the potential of urban agriculture as a landscape approach remains underdeveloped. In summary, current research and practice lacks a scientific framework for considering urban agriculture as a landscape approach to intervene in urban spaces. To this end, this paper explores the potential of urban agriculture as a landscape approach in sustainable urban planning and design through qualitative case study. Taking Songzhuang in Beijing as an example, we discuss and summarise the operational value and potential of urban agriculture from a design perspective. The findings suggest that landscape-based urbanism that includes urban agriculture can harmonise social, economic, environmental and ecological elements. Finally, in order to provide a generalised approach, this paper proposes a scientific framework for articulating a landscape approach to urban agriculture to guide future research and practice. [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]

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]

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts' waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing's historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of "1 h rainfall intensity water logging index" and "the waterlogging risk index in historical and cultural districts" were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts. [ABSTRACT FROM AUTHOR]

Industrial heritage is regarded as an important stock of spatial resources in cities, which highlights its utilization value for urban regeneration in high-density urban areas. With the dramatic increase in the number of industrial heritage reuse projects, how to scientifically evaluate the satisfaction with their spatial reuse is a key part of the solution for the mutual balance between heritage preservation and urban renewal. This paper takes eight industrial heritage conversion and utilization projects in the high-density core area of Beijing's central city as examples; establishes an evaluation system for the satisfaction with the spatial reuse through six dimensions, namely, functional replacement, transportation accessibility, carrying capacity, public space, boundary form, and recognition of value; and uses the IPA method to evaluate the cases. This method is used to determine the degree of user satisfaction with the spatial reuse of industrial heritage in the core area of Beijing's central city and to summarize the advantages and problems of its reuse. The results of this study reveal a trend toward the "community-oriented" re-generation of industrial heritage in the core area of Beijing's central city, and this paper proposes recommendations for adaptive use to support high-quality urban regeneration work. [ABSTRACT FROM AUTHOR]

Beijing is a typical energy-imported city, and the study of energy consumption factors in Beijing is important for the sustainable development of Beijing. There are many influencing factors of energy consumption in Beijing, and the influence degree of each factor differs in different development periods. By constructing the IPAT model of energy consumption change in Beijing, this paper breaks down the factors that cause the change in energy consumption in Beijing into population, affluence, and technological progress. The results show that Beijing is a highly energy-consuming city and the change in energy consumption in Beijing is the superposition of multiple effects. The energy consumption change caused by population growth in Beijing continues to increase through data from 2006 to 2020, but begins to show a downward trend, with a maximum change in energy consumption of 19.8 million tons of standard coal. The largest change in energy consumption is caused by per capita GDP growth, which reached 50.3 million tons of standard coal in 2018–2019, and the effect caused by affluence is greater than the impact of population changes. Technological advances always reduce energy consumption and are increasingly effective. To reduce energy consumption and improve environmental quality, the following two things need to be done: first, to continuously control the population of Beijing, and second, to enhance the development and introduction of innovative technologies for energy conservation. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to apply the bibliometric analysis to summarize acupuncture therapy for stroke, demonstrated and evaluated the trends, major research hotspots and frontier areas. Materials and Methods: Articles on acupuncture for stroke were selected from the Web of Science Core Collection (WoSCC) from the inception of the database up until 2023. CiteSpace software was performed to conduct the collaborative analysis of networks of countries, institutions, authors and cited authors, journals and cited journals, cited references, keywords clustering and burstiness analysis. Results: A total of 1141 articles were retrieved. China was the most productive country (851) and had the greatest centrality (0.43). Beijing Univ Chinese Med (86) contributed to the most publications. Chen LD (31) and Tao J (31) were the most prolific authors, of which all from Fujian Univ Tradit Chinese Med. Wu P (124) from Canadian College of Naturopathic Medicine, Canada, was the most cited author. Evidence-based Complementary and Alternative Medicine (89) was the most productive journal, while Stroke (744) was first cited journals. Stimulation, recovery, ischemic stroke, electroacupuncture, rehabilitation were the most high-frequency keywords. Future research in this area will pay more attention to the evaluation of the effectiveness of acupuncture therapeutics in treating stroke, conducting the clinical research on cognitive ability, quality of life and partial function of stroke patients, and basic research related to mechanisms. Conclusion: The publications on acupuncture in stroke have shown major development, but the international cooperation for academic exchange among researchers and institutions remained to be strengthened to promote interdisciplinary and academic innovation. Furthermore, except for the molecular mechanism of acupuncture in treating functional rehabilitation of stroke, exploring the more high-quality clinical studies may become a key point based on the evidence-based medicine. [ABSTRACT FROM AUTHOR]

For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step "coarse-to-fine registration" algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%/26.2%, the precision by 41.0%/15.5%, the recall by 41.0%/16.0%, and the F1-score by 42.7%/15.8%. For the registration, the method can reduce the translation errors by 4.656 m/2.815 m, as well as the rotation errors by 0.538°/0.228°, which indicates its great potential in smart 3D applications. [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]

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]

The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms. [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]

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]

With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

Scholars have done a lot of research on urban settlement, but it is difficult to give consideration to the temporal and spatial attributes of settlement at the same time in its display and analysis. Most of them focused on the analysis of regional settlement, single point settlement curve and settlement rate map at a certain time, but few combined time and space for collaborative analysis. Therefore, in this paper, 32 scenes Sentinel-1B SAR data are used to obtain settlement data of Beijing via PS-InSAR method. Secondly, combined with the temporal and spatial attributes of settlement results, the subsidence law revealed by using spatio-temporal cube slicing and attribute filtering. Finally, subsidence development trend and the detection of abnormal subsidence are explored by emerging hot spots (ESH) analysis. The experimental results show that the settlement funnel center in Beijing is mainly concentrated near the junction of Chaoyang district and Tongzhou district. The settlement range tends to expand. There are several local continuous subsidence areas in the settlement oscillating area. Spatio-temporal analysis makes the development trend of urban settlement more intuitive. Emerging hotspot analysis combined with Getis-Ord Gi* statistics and Mann-Kendall trend test could more effectively analyze the settlement trend of the study area and detect new potential settlement centers, so that to provide auxiliary decision-making for urban safety early warning and city development. [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]

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]

The article focuses on a study which aims at measuring atmospheric column carbon dioxide (CO2) from a sun photometer in Beijing, China from 2010-2012. In the study, CO2 measurements were acquired, translated into cloud free data, and converted into an index related to column CO2 amount. Seasonal variations of CO2 levels in the atmosphere were also analyzed.

Optimizing resource efficiency and mitigating climate change have become consensuses of human society. However, there is still a gap in assessing the carbon emission efficiency (CEE) and identifying the influence of various factors, especially in rapid urbanizing regions. In this paper, we built a stochastic frontier analysis model to assess CEE and conducted a case study in the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA), a typical area of collaborative development in China. A comprehensive influencing factor index was constructed to analyze and identify the key influencing factors of CEE. The results revealed that the average CEE among the 13 cities increased in volatility from 2000 to 2019. The average CEE in Langfang was lowest, while that in Tangshan was highest. The input-related factors had a negative effect on CEE, including carbon emissions per capita, employment per ten thousand people, total assets per capita, and energy intensity. GDP per capita, the urbanization level, and the proportion of the tertiary sector's GDP had positive impacts on CEE. Future policy formulation should focus on the transition from labor- and material-intensive industries to knowledge- and technology-intensive industries. All the results can contribute to achieving high-quality development and dual-carbon target of rapid-urbanizing areas. [ABSTRACT FROM AUTHOR]

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The underground hub of Xinghuo Station of M3 Line of Beijing Metro is an integral part of the underground comprehensive hub of Xinghuo Station of high-speed railway, and it is constructed by open-excavation method. Due to the different requirements of the main structure function inside the foundation pit of the hub, the foundation pit adopts the three-level layout. One side of the foundation pit is adjacent to the existing high-speed railway Xinghuo station, and the other side is adjacent to the urban road, the foundation pit is affected by four layers of groundwater. In order to solve the difficulties encountered in the design of the multi-stage foundation pit support structure of the underground hub of the Xinghuo Station on the metro line M3, this paper carries out the research of the foundation pit composite support structure system. Firstly, the spatial layout characteristics, surrounding environment and groundwater occurrence characteristics of the hub foundation pit are analyzed, and the problems of the conventional support structure system are discussed. Based on the concept of graded support and graded water stop, the combined foundation pit support structure system is developed. The paper presents in detail the layout, structural parameters and step sequence of multi-stage foundation pit excavation and support structure system at all stages, then establishes the three-dimensional numerical calculation model of multi-stage foundation pit excavation and support, analyzes the pit bottom uplift and the deformation of the retaining structure, and finally discusses the deformation characteristics of the combined retaining structure of the foundation pit based on the measured results of the deformation of the retaining structure at all stages. The results of numerical simulation and on-site monitoring show that the uplift of pit bottom and the deformation of supporting structure meet the requirements of control values. [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]

With the increasing demand for electric vehicle (EV) charging, there are cases of complicated trading between EV users and charging operators or electrical utilities. This paper proposes a new trading model based on consortium blockchain. Firstly, mutual trust interconnected transaction networks and channels between charging operators are established. The PBFT consensus algorithm is used to verify EV charging transactions, and smart contracts are used to complete the process. In this model, charging station nodes of each company can verify transactions, the interconnection and independent management of charging transactions are realized, users' charging methods are enriched, and the flexibility of charging services is improved. Finally, this paper uses hyperledger fabric to build an experimental environment. The feasibility of the above method is verified with an actual distribution scenario of some charging stations in Beijing. [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]

Consumer demand and retailing models nowadays are being upgraded more frequently. More and more retailers are switching to the Omni-channel retailing model. Choosing a reasonable location for a front distribution center (FDC) helps control an enterprise's cost and improves its service level. This is especially true in the existence of fierce competition. In this paper, two important and contradictory objectives are proposed for the first time in the FDC location problem: minimizing the distribution costs from the facility and minimizing the fixed cost of the facility's location. For these objectives, a bi-objective programming model is established by considering the factors of a facility's capacity, demand and rent fluctuation. Meanwhile, the FDC location problem has been solved by compromising programming and elite set multi-objective simulated annealing algorithm respectively. Taking the FDC locations set of an e-commerce enterprise in a region of Beijing as an empirical sample, this paper uses the above algorithms to re-plan the FDC locations of the enterprise. This algorithm provides support for retail enterprises by helping find the best FDC location. Based on the empirical results, some comments and future research directions are also proposed. [ABSTRACT FROM AUTHOR]

Many developed cities in the world put forward a spatial development strategy in their construction planning. Analyzing the development level of the urban spatial structure and the influence of driving factors has become a hot topic. Based on 709,232 points of interest data in Beijing, China, this paper integrates nighttime light data and population density data to select eight key driving factors from three perspectives: urban function configuration, economic activity intensity and population spatial distribution. Geodetector is used to optimize data discreteness and highlight the spatial heterogeneity of the development level. We use the technique for order of preference by similarity to ideal solution (TOPSIS) model improved with the entropy weight method to grade the spatial differentiation characteristics of the comprehensive development level. The driving factors and their effects on space are further discussed using Geodetector. The results are as follows: (1) The quartile method can achieve the optimal dispersion of all urban functions. The standard deviation can achieve the optimal dispersion of economic activity intensity and population spatial distribution; (2) A comparison with the "Beijing Urban Master Plan (2016–2035)", finds that the optimized evaluation system can effectively reflect the spatial heterogeneity of urban spatial structure development. It verifies the rationality of the evaluation methods and factors; (3) The driving force of the population under single-factor driving is 0.8428. The dual-factor driving force with population participation ranges from 0.8992 to 0.9550. The results of the study are significant and reflect the prominent role of population on the development level of spatial structure in Beijing. This paper aims to provide a new idea for the study of the interior space planning of large inland cities. [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]

Isotope ratio infrared spectroscopy (IRIS) provides an in-situ technique for measuring δ13C in atmospheric CO2. A number of methods have been proposed for calibrating the IRIS measurements, but few studies have systematically evaluated their accuracy for atmospheric applications. In this study, we carried out laboratory and ambient measurements with two commercial IRIS analyzers and compared the accuracy of four calibration strategies. We found that calibration based on the 12C and 13C mixing ratios (Bowling et al., 2003) and that based on linear interpolation of the measured delta using the mixing ratio of the major isotopologue (Lee et al., 2005) yielded accuracy better than 0.06‰. Over a 7-day atmospheric measurement in Beijing, the two analyzers differed by 9.44±1.65‰ (mean ±1 standard deviation of hourly values) before calibration and agreed to within -0.02±0.18‰ after properly calibration. However, even after calibration the difference between the two analyzers showed a slight correlation with concentration, and this concentration dependence propagated through the Keeling analysis resulting in a much larger difference of 2.44‰ for the Keeling intercept. The high sensitivity of the Keeling analysis to the concentration dependence underscores the challenge of IRIS for atmospheric research. [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]

Government-enterprise-university synergy (GEUS) is an effective way to mobilize government, enterprises, and universities to collaborate on education, but these three parties involved in GEUS may, out of bounded rationality, choose to collaborate in ways that benefit themselves and harm others. To guide the three parties to better cooperation, this study creates an evolutionary game model among the three parties and evaluates the applicability and validity of the model by selecting the educational cooperation data in Beijing. It is shown that participation in education cooperation is the best course of action for all three parties. The intensity of willingness to participate in the GEUS is on the order of high to low for universities, enterprises, and the government. If the three parties wish to accomplish education collaboration sooner, they can increase default payments, boost government revenues, raise corporate participation in distribution, and reduce government and government spending. These results highlight the inherent regularities of GEUS and provide concrete implementation strategies to improve the efficiency of education cooperation. [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]

Short-term origin–destination (termed as OD) prediction is crucial to improve the operation of urban rail transit (termed as URT). The latest research results show that deep learning can effectively improve the performance of short-term OD prediction and meet the real-time requirements. However, many advanced neural network design ideas have not been fully applied in the field of short-term OD prediction in URT. In this paper, a novel parallel interactive attention network (termed as PIANet) for short-term OD prediction in URT is proposed to further improve the short-term OD prediction accuracy. In the proposed PIANet, a novel omnidirectional attention module (termed as OAM) is proposed to improve the representational power of the network by calculating the feature weights in the channel–spatial dimension. Moreover, a simple yet effective feature interaction is proposed to improve the feature utilization. Based on the two real-world datasets from the Beijing subway, the comparative experiments demonstrate that the proposed PIANet outperforms the state-of-the-art deep learning methods for short-term OD prediction in URT, and the ablation studies demonstrate that the proposed OAMs and feature interaction play an important role in improving the short-term OD prediction accuracy. [ABSTRACT FROM AUTHOR]

Given the complex interplay between economic and cultural–historical factors, this paper explores scene perception and its mechanism in individual and collective cognition of historical commercial streets. Based on a perception model constructed from scene theory, the study utilizes user scene experience feedback from Beijing Road, a commercial pedestrian street in Guangzhou. It conducts a systematic assessment using hierarchical analysis (AHP) across three dimensions: authenticity, theatricality, and legitimacy. The findings reveal the following: (1) according to visitors, traditional cuisine, cultural and creative arts, and Lingnan's intangible cultural heritage received low ratings; (2) historical relics and traditional cuisine are dominant factors, highlighting public attention to cultural heritage, but the role of historical–cultural characteristics and festival activities is relatively marginalized; (3) cultural–historical relics and traditional cuisine are the leading factors that contribute to an authentic experience, while historical context and festival activities constitute secondary factors; (4) cultural landscapes and arts are central to theatrical experiences, with modern visual elements playing a lesser role; (5) diversified commercial formats and public services are the core factors influencing legitimacy experience, reflecting the role of market diversity and policy orientation in building consumer trust. The design of historical and cultural streets should prioritize the preferences of those experiencing them to promote cultural identity and historical continuity. Additionally, the synergy between commercial diversity and policy orientation should be strategically emphasized to form a business ecosystem supporting sustainable development. Future commercial street renewals should focus on the key role of visual narrative in shaping brand culture. This paper offers insights into the dynamic process of constructing scene experience from perspectives of spatial materialization and emotional empowerment to cultural identity, providing references for strategies in historical place renewal. [ABSTRACT FROM AUTHOR]

China is in the transition period of urbanization, and the imbalance of land use generated during the original rapid development of megacities still exists, resulting in the preservation of a large number of urban villages in the peripheral areas of cities. Unlike formal housing, these villages are heavily influenced by the confrontation and interweaving between top-down policies of local governments and bottom-up spontaneous construction by local villagers, resulting in a diverse and complex status quo. At the same time, self-built housing in urban villages has become a gathering place for a large number of low-income migrants due to its relatively low rental prices. In terms of the research of urban villages on the current situation of housing, the real needs of users and housing performance are mostly subjective judgments and conclusions. Therefore, the entry point of this paper is the leased space and the leasers, taking Laojuntang Village in Chaoyang District of Beijing as the research object and analyzing its current problems and the real needs of users. This paper adopts an integrated approach of fuzzy Kano model evaluation and building performance simulation. After analyzing the data and discussing the influencing factors, this paper identifies the specific problems and user needs that exist in leased spaces in Laojuntang Village: (1) The architectural performance of leased space is a Must-be Quality, which is the bottom-line requirement and must be improved. (2) The quality of the basic facilities and equipment of the leased space is mostly a One-dimensional Quality, Attractive Quality, and the degree of importance is lower than that of the building performance. (3) The external activity facilities of the leased space are mostly undifferentiated needs, for which the local users are not particularly prominent. (4) The scale and plot ratio of local housing is relatively high, and users are dissatisfied and complaining, which falls under the category of Reverse Quality. [ABSTRACT FROM AUTHOR]

As a politically and culturally important city cluster, the Beijing–Tianjin–Hebei (BTH) region is the most prominent area in China where the imbalance between the supply and demand of water resources restricts the sustainable and healthy development of the regional social economy. In the context of global warming, research into water demand prediction that takes climate change into consideration would be more in line with the strategic goal of the low-carbon sustainable development of future cities. At the same time, the prediction of agricultural water demands against a background of climate change is urgently needed, while industrial water consumption is weakly correlated with climate change, an investigation of the statistical relationship between the two is needed. Thus, in this paper, future climate data from the BTH region under the scenarios RCP2.6, RCP4.5 and RCP8.5 were generated using a statistical downscaling model, and then coupled with agricultural and industrial water demand prediction models to simulate and analyze the impact of climate change on the agricultural and industrial water demands, respectively. The results show that during the forecast period (2020–2035), the reference crop evapotranspiration (ET0) growth rates in the Beijing, Tianjin and Hebei areas under the RCP2.6 scenario are 1.438 mm·a−1, 1.393 mm·a−1 and 2.059 mm·a−1, respectively. Under the RCP4.5 scenario, they are 2.252 mm·a−1, 2.310 mm·a−1 and 2.827 mm·a−1, respectively. Under the RCP8.5 scenario, they are 3.123 mm·a−1, 2.310 mm·a−1 and 2.141 mm·a−1, respectively. Furthermore, under each climate scenario, the increase in evapotranspiration in the Hebei area is the largest, followed by that in the Tianjin area, and that in the Beijing area is the smallest. For water consumption per CNY 10,000 of industrial added value during the forecast period, under the three different climate scenarios, a downward trend is seen in the Beijing area, with rates of 0.158, 0.153 and 0.110, respectively, but in the Tianjin area, there is an upward trend, with an upward tendency in rates of 0.170, 0.087 and 0.071, and an upward trend in the Hebei area, with an upward tendency in rates of 0.254, 0.071 and 0.036, respectively. This study will help the BTH region to rationally allocate agricultural and industrial water against the background of future climate change, and strengthen the coordination and cooperation between the different regions to promote the healthy and sustainable development of the cities. [ABSTRACT FROM AUTHOR]

The issues of housing and traffic in China's mega cities have become increasingly pressing problems, particularly for middle/low-income tenant workers. These tenants are from less advantaged socioeconomic backgrounds, which has resulted in a significant geographical separation between their workplace and their residence. Although a large number of studies have confirmed that built environment factors have a solid impact on residents' commuting distance, few studies have investigated the mechanism underlying the nonlinear influence on middle/low-income tenants. This paper aims to provide an in-depth analysis of the key factors and nonlinear influencing mechanism of the built environment on middle/low-income tenant workers' commuting distance by establishing a gradient-boosting decision tree model, using Beijing as an empirical case. The paper reveals three primary findings: (1) An important nonlinear relationship between the surrounding built environment and peoples' jobs–housing spatial proximity can be observed for those middle/low-income tenant workers who use slow and public modes of commuting. Specifically, the density of public transport stations, road networks, and workplaces, and the land use mix play a dominant role. (2) A limited effect of built environment factors can be found for the same group of tenant workers who choose cars as their mode of commuting. (3) The differences in self-selected commuting modes have a significant mediating effect on the relationship between the built environment and jobs–housing situation among middle/low-income tenant workers. Given this, effective policy guidance for residents' travel modes is necessary to optimize the built environment indicators to achieve the best effect. In addition, we should consider giving priority to the matching indicators such as land use mix and resident population density. Another possibility is to strengthen the connection to the public transport stations, which in turn can optimize the walkability in residential environments. [ABSTRACT FROM AUTHOR]