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In the modern risk society, the general sense of collaborative governance is facing a profound crisis—poor governance efficiency due to the lack of authority. The paper introduces a case from China and finds a new mechanism in COVID-19 prevention and control which is called joint governance. Instead of the relatively equal status among multiple actors, joint governance is characterized by a dominant power forming alliances with other stakeholders. The political advantages that Beijing enjoys make it work. Our study suggests that joint governance is a new perspective of collaborative governance which brings politics back to administrative domains. [ABSTRACT FROM AUTHOR]

Urban tourism is considered a complex system, and multiscale exploration of the organizational patterns of attraction networks has become a topical issue in urban tourism, so exploring the multiscale characteristics and connection mechanisms of attraction networks is important for understanding the linkages between attractions and even the future destination planning. This paper uses geotagging data to compare the links between attractions in Beijing, China during four different periods: the pre-Olympic period (2004–2007), the Olympic Games and subsequent 'heat period' (2008–2013), the post-Olympic period (2014–2019), and the COVID-19(Corona Virus Disease 2019) pandemic period (2020–2021). The aim is to better understand the evolution and patterns of attraction networks at different scales in Beijing and to provide insights for tourism planning in the destination. The results show that the macro, meso-, and microscales network characteristics of attraction networks have inherent logical relationships that can explain the commonalities and differences in the development process of tourism networks. The macroscale attraction network degree Matthew effect is significant in the four different periods and exhibits a morphological monocentric structure, suggesting that new entrants are more likely to be associated with attractions that already have high value. The mesoscale links attractions according to the common purpose of tourists, and the results of the community segmentation of the attraction networks in the four different periods suggest that the functional polycentric structure describes their clustering effect, and the weak links between clusters result from attractions bound by incomplete information and distance, and the functional polycentric structure with a generally more efficient network of clusters. The pattern structure at the microscale reveals the topological transformation relationship of the regional collaboration pattern, and the attraction network structure in the four different periods has a very similar importance profile structure suggesting that the attraction network has the same construction rules and evolution mechanism, which aids in understanding the attraction network pattern at both macro and micro scales. Important approaches and practical implications for planners and managers are presented. [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 introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

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

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]

Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. [ABSTRACT FROM AUTHOR]

Ever since China has formally joined the WHO-backed global COVID-19 vaccine initiative known as COVAX, there is a presumed notion that China's vaccine diplomacy will make a significant contribution to the international public good and thus uplift Beijing's role as the rule-maker of international order. To scrutinize this, the paper asks if China succeeded in proliferating its weaponized vaccine policy to obtain maximum diplomatic gains and soft power projection to intensify its international image, geopolitical power, and domestic politico legitimacy. The authors argue that despite its vaccine diplomacy demonstrated the robust governance capacity and responsibility to be a great power. Yet, Beijing's geopolitical influence and international image are significantly overrated and not enough to play a more prominent role in the global power fulcrum/equilibrium. On the contrary, China enjoys a leading position on the domestic political front. Its successful portrayal of China's vaccine provision in the global market and remarkable configuration to leverage a deep-rooted nationalism has fundamentally provided China with a powerful rationale to divert its public's attention from Beijing's earlier inadequate handling of the outbreak. The evaluation of the paper reveals that China's vaccine diplomacy's influence in promoting international image and geopolitics is limited but has successfully stabilized its domestic political environment and enhanced its domestic legitimacy. [ABSTRACT FROM AUTHOR]

Ensemble forecasting systems have become an important tool for estimating the uncertainties in initial conditions and model formulations and they are receiving increased attention from various applications. The Regional Ensemble Prediction System (REPS), which has operated at the Beijing Meteorological Service (BMS) since 2017, allows for probabilistic forecasts. However, it still suffers from systematic deficiencies during the first couple of forecast hours. This paper presents an integrated probabilistic nowcasting ensemble prediction system (NEPS) that is constructed by applying a mixed dynamic-integrated method. It essentially combines the uncertainty information (i.e., ensemble variance) provided by the REPS with the nowcasting method provided by the rapid-refresh deterministic nowcasting prediction system (NPS) that has operated at the Beijing Meteorological Service (BMS) since 2019. The NEPS provides hourly updated analyses and probabilistic forecasts in the nowcasting and short range (0–6 h) with a spatial grid spacing of 500 m. It covers the three meteorological parameters: temperature, wind, and precipitation. The outcome of an evaluation experiment over the deterministic and probabilistic forecasts indicates that the NEPS outperforms the REPS and NPS in terms of surface weather variables. Analysis of two cases demonstrates the superior reliability of the NEPS and suggests that the NEPS gives more details about the spatial intensity and distribution of the meteorological parameters. [ABSTRACT FROM AUTHOR]

In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly in transportation industry. Due to the high density of trains in the daytime, we usually set up skylights at night (0:00–6:00 am) on high-speed railway for comprehensive maintenance. However, this arrangement contradicts with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly added overnight D-trains. Therefore, in this paper, a mixed logit model based on nonlinear random utility functions for different transport modes is proposed, in order to predict transfer passenger demand. According to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups while Metropolis–Hastings algorithm is adopted to iteratively solve the probabilities of discrete random variables in utility functions. After that, the unknown distributions of parameters are estimated and the optimal solution of this model is provided by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including improved fireworks-simulated annealing algorithm proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing–Shanghai corridor is implemented to demonstrate the performance and effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

A methodology is proposed to introduce knowledge graphs into the study of the Chinese cultural field for use in a newly designed, complete application. At present, the combination of culture and information technology has become a trend. Among various technologies, knowledge graphs are a very promising option. The contributions of this paper are as follows: it supplies for the first time a knowledge graph in the cultural field of the ancient capital of Beijing, establishes a domain knowledge base, and develops a platform for visual analysis and interactive question and answer. In this process, a framework for applying knowledge graphs to research in the cultural field is summarized, providing ideas for research in the cultural field. [ABSTRACT FROM AUTHOR]

The 2022 Winter Olympics were held in the three competition zones of Beijing, Yanqing and Zhangjiakou, China. The venues of this Winter Olympics were scattered and the terrain was complex. Moreover, the medical resources of Hebei and Beijing were relatively unbalanced. In the medical security of major events, the connection between first aid and in-hospital processes is of the utmost importance to rescue quality. 5th generation mobile network (5G) applications in medical scenarios are on the rise. It would be of great relevance to fully use 5G's low-latency and high-speed features to share the process information of patients, ambulance personnel, and the destination hospital's rescue team at emergency scenes and in transportation, improving rescue efficiency. This paper proposes a system scheme of cross-institutional emergency health information sharing based on 5G and augmented reality wearable devices. It also integrates the construction method of monitoring and other sign data sharing, in addition to testing the proposed scheme's service quality in 5G environments. In the deployment area of the 5G emergency medical rescue information sharing scheme for the Beijing Winter Olympic Games, we selected two designated medical support institutions for testing. The test adopted a combination of fixed-point and driving tests to experiment on the service data, voice service, and streaming media indicators. The 5G signal's coverage rate was close to 100%, the standalone connection's success rate was 100%, and the drop rate was 0. The average downlink rate of multiple scenarios was 620mbps, and the average uplink rate of 5G was over 71.8mbps, which is higher than the average 5G level in China. The downlink rate was more than 20 times larger than the 4th generation mobile network (4G) rate. This study's proposed scheme demonstrates the importance of 5G applications in emergency response and support, in addition to providing a suitable scheme for the integration of 5G networks in the medical scene. [ABSTRACT FROM AUTHOR]

The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people's lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R2 reaches 0.9995. [ABSTRACT FROM AUTHOR]

Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics, which makes them difficult to model complex human mobility patterns. The other is the machine learning models, most of which simply leverage the features of Origin-Destination (OD), ignoring the topological nature of the interaction network and the spatial correlation brought by the nearby areas. In this paper, we propose a 'preprocessing-encoder-decoder' hybrid learning model, which can make full use of geographic semantic information and spatial neighborhood effects, thereby significantly improving the prediction performance. Specifically, in the preprocessing part, we divide the study area into grids, and then incorporates features such as location, population, and land use types. The second step of the encoder designs a convolutional neural network (CNN) to achieve the fusion of neighborhood features, constructs a spatial interaction network with the grids as nodes and the flows as edges, and then uses the graph convolutional network (GCN) to extract the embeddings of the nodes. In the last step of the decoder, a random forest regressor is trained to predict the commuting flow based on the learned embedding vectors. An empirical study on a commuter dataset in Beijing shows that our proposed model is approximately 20% better than XGBoost (state-of-the-art), thus proving its effectiveness. [ABSTRACT FROM AUTHOR]

Craquelure is the most common defect on ancient polychrome paintings, which may deteriorate further to paint loss. Previous image processing methods, which can accurately recognize paint loss, have limited precision and efficiency in segmenting craquelure. This paper proposes a semantic segmentation method, Res-UNet, for the recognition of craquelure and paint loss in the Palace Museum, Beijing. The residual structure of ResNet-50 enables the avoidance of network degradation, and image features can be fully extracted. Using the unique skip connection module of U-Net, features of different levels are fused to improve segmentation accuracy and provide smoother craquelure edges. Three loss functions are combined to accelerate stable convergence. The model was tested on a newly built dataset based on 600 images. Experimental results supported by statistical tests show that Res-UNet is a capable method of craquelure recognition, with an accuracy rate of 98.19%, and F1-score of 93.42%. Hence, the proposed hybrid approach is a promising tool to support the preservation and restoration of valuable traditional Chinese polychrome architectural paintings. [ABSTRACT FROM AUTHOR]

Data fusion is an efficient way to achieve an improved accuracy and more specific inferences by fusing and aggregating data from different sensors. However, due to the increasing complexity of spatial data with massive and multi-source heterogeneous characteristics, the existing methods cannot satisfy quite well the requirement for the integrity of data and the accuracy of fusion results in some specific situations. By considering the geographical properties of spatial data, a multi-source heterogeneous spatial big data fusion method based on multiple similarity and voting decision (SDFSV) is proposed in this paper, which develops a three-step record linking algorithm to improve the quality of entity recognition for the incremental fusion of massive data. Then, a one-time voting algorithm is introduced into the proposed method, so that the data conflicts can be significantly reduced and thus the accuracy of the data fusion can be improved. And a relation deduction method based on rule and entity recognition is presented to enhance the data integrity. In addition, in order to promote traceability and interpretability of fusion results, it is necessary to construct a data traceability mechanism. Experimental results show that SDFSV has an improved performance by using the data of Beijing Medical Institutions collected from 10 data sources. [ABSTRACT FROM AUTHOR]

Scholars and analysts have sought to clarify the Chinese Communist Party's motives for carrying out the Belt and Road Initiative (BRI), a series of infrastructure investments in countries across the globe. While many emphasize Beijing's geopolitical interests, this paper argues that the BRI should also be understood as a large-scale effort to create business activities for Chinese companies and workers outside of China. We define the contours of this "building out" strategy by analyzing the stages that typify BRI projects: project selection, project financing, project construction, and post-construction operation and management. Our analysis draws, in large part, on data that we have compiled from a sample of BRI projects. After showing macro-level trends in the data, we offer three in-depth case studies to clarify the underlying mechanisms at work. Our findings suggest that BRI projects are engineered to direct finance and construction bids to Chinese companies, which are typically state-owned. [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]

Dissolved organic matter (DOM) plays an important role in the global carbon cycle, and an in-depth analysis of its chemical composition is fundamental to the study of its environmental and biogeochemical behavior and significance. DOM is a complex mixture of organic substances, and determining its molecular composition is a long-standing challenge in the field of analytical chemistry. The development and application of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has enabled the identification of the chemical composition of DOM at the molecular level. This paper summarizes progress made in the molecular characterization of DOM based on the FT-ICR MS technique, including DOM sample pretreatment methods, mass spectrometry ionization techniques, data acquisition, data processing and presentation, and molecular structure characterization. Focusing on the work done by the instrument at the State Key Laboratory of Heavy Oil Processing in the China University of Petroleum, Beijing, we introduce applications of FT-ICR MS in the fields of earth science, environmental science and engineering, and look ahead to further research directions in the future. [ABSTRACT FROM AUTHOR]

Microvibration induced by natural disturbance and human activities has an adverse effect on the operation of the large-scale and ultraprecise facilities in the world. Under such circumstances, a passive vibration control method is generally deployed for such vibration-sensitive facilities, taking the High Energy Photo Source (HEPS) in Beijing as an example, a 3 m-thick mass concrete layer forming a ring foundation was cast at the facility, where a 1 m-thick reinforced concrete slab (RC slab) lies. Since microvibration control plays a crucial role in the operation of such large-scale scientific and ultraprecise facilities and few studies have been reported for large-scale concrete layer as antimicrovibration devices, this paper presents four field tests in Beijing, China, to evaluate the vibration control capacity of a mass concrete layer. Based on a large number of field tests, the effect of applying the concrete layer is discussed, and a reference is provided for the construction of similar facilities. The vibration signals, generated by shock excitation and ambient excitation, are measured through a highly sensitive and high-accuracy vibration acquisition system. It is concluded that the existence of the 1 m-thick RC slab has little influence on the microvibration signal frequency distribution in the vertical direction and that the signals from the concrete layer and subsoil differ by approximately 10 Hz in the vertical direction while differing by approximately 5 Hz in the horizontal direction. The microvibration control ability of the concrete layer is favorable in a higher frequency band over 20 ~ 30 Hz and more than 50% attenuation can be gained through the concrete layer; however, the microvibration control ability is not significant below 20 ~ 30 Hz. The vibration levels across different heights of the concrete layer section are the same. To prevent adverse vibration disturbance below 20 ~ 30 Hz, it is suggested that the traffic and road surface conditions should be taken into consideration when choosing the construction location. In addition, a long-term monitoring shows that 75% vibration energy at the site is firmly related to the construction activities which are approximately 1.4 km from the site. [ABSTRACT FROM AUTHOR]

Land subsidence induced by groundwater level decline has spatiotemporal variations. Taking the Interferometric Synthetic Aperture Radar (InSAR) results and the groundwater subsidence data acquired by the monitoring stations as the source material, this paper aims to reveal the spatiotemporal heterogeneity of groundwater-land subsidence in Beijing plain by using the Wind Rose Map (WRM) method and the Change Point Analysis (CPA) method. The WRM results show that the amount and variation in subsidence differs in different directions. This method detected the formation of new subsidence centers and the slowdown of land subsidence in 2008. The CPA results show that obvious changes are detected in subsidence development at the Wangsiying (WSY), Tianzhu (TZ) and Wangjing (WJ) stations. However, there is a relatively stable trend of groundwater decline and land subsidence at the Tianzhu (TZ) station. The stages of land subsidence development show a significant response to groundwater. Moreover, changes in land subsidence also show delayed response behind the changes in groundwater level. The time-lag could be affected by the variation in amplitude of the groundwater level. [ABSTRACT FROM AUTHOR]

To study urban air quality, this paper proposes a novel categorical time series model, which is based on a linear combination of bounded Poisson distribution and discrete distribution to describe the dynamic and systemic features of air quality, respectively. Daily air quality level data of three major cities in China, including Beijing, Shanghai and Guangzhou, are analyzed. It is concluded that the air quality in Beijing is the worst among the three cities but is gradually improving, and its dynamics is also the most pronounced. Theoretically, the design of our model increases the flexibility of the probabilistic structure while ensuring a dynamic feedback mechanism without high computational stress. We estimate the parameters through an adaptive Bayesian Markov chain Monte Carlo sampling scheme and show the satisfactory finite sample performance of the model through simulation studies. [ABSTRACT FROM AUTHOR]

This paper investigated the dynamic relationship between ife expectancy (LE) and its inflencing factors including, health care, socioeconomic, and environment factors in Beijing City of China. Time-series data from 2000 to 2018 were collected and vector autoregression (VAR) modeling was performed. This study quantified the lagged effect of air pollution and socioeconomic factors on increased LE over the 19 years of the study period in Beijing. The results showed that a VAR model with optimum lag 3 was constructed between LE and three explanatory variables including per capita area of green land (AGL), the average wage of employed staff and workers (WAGE), and PM2.5. In addition, LE showed a decrease from its present value once the impulse of AGL and WAGE are given. In contrast, LE increase from its present value after the impulse of PM2.5 given in the ten-year period analysis. In sum, environment factors such as air pollution and area of green land are considered to be highly effective in influencing LE than socioeconomic factors in Beijing City. [ABSTRACT FROM AUTHOR]

The accurate prediction of short-term passenger flow is of high importance to efficiently manage the passenger flow of metro systems and adjust timetable accordingly. However, the existing methods of passenger flow prediction cannot achieve adequate accurate results due to its complex nonlinear spatiotemporal characteristics. To improve the accuracy of short-term passenger flow prediction, this paper proposes a deep learning model based on a spatiotemporal framework. Firstly, the graph convolutional network, which incorporates prior domain knowledge (such as travel time and origin–destination demand), is used to extract spatial features of passenger flow. Secondly, the attention mechanism is integrated into the gated recurrent unit to extract the time correlation of passenger flow. Finally, external factors are introduced to capture their impact on passenger flow as well. A case study of the Beijing Subway system is illustrated to verify the performance of the proposed model. The results show that compared with the existing models, the proposed model achieves the highest prediction accuracy and strong robustness. Furthermore, we demonstrate that the adjacency matrix based on travel time outperforms the one based on OD demand, especially during evening peak hours. In addition, it is also verified that the attention mechanism and external factors can improve the prediction performance of the proposed model. [ABSTRACT FROM AUTHOR]

Contemporary packages of housing retrofit equipment are based on models of expected energy savings with regard to globally standardized thermal comfort levels. Previous research shows that the energy savings realised after a housing retrofit is substantially lower than expected. Attempts to reduce energy demand by physical re-design, utilising technical standards for thermal comfort as well as financial incentives, tend to ignore the role of retrofit interventions in the construction of everyday practices of thermal comfort making. Thermal comfort practices of heating, cooling and ventilation are moderated by specific householders' motivations which constitute 'wants' and emerging 'needs' in the interaction with the housing retrofit equipment. This paper proposes that the interactions between the retrofitted buildings and the householders are the sum of material affordances, as signified by the design of the housing equipment on the one hand, and the practical affordances in practices-as-performances on the other. The study presents comfort practices in relation to recently retrofitted low-income housing estates in Beijing, Mianyang (Sichuan province, South-west China) and Amsterdam on the basis of 50 qualitative interviews with householders in each city. The paper concludes that the expected energy saving is counteracted by a poor match between conventional retrofit packages and householders' considerations about their thermal comfort. To better reduce energy demand and to mitigate energy poverty, retrofit packages should provide adaptive thermal comfort as preferred by householders, rather than fixed or tightly specified thermal comfort. Such a perspective may support a more flexible and inclusive use of housing equipment as part of retrofit programs. [ABSTRACT FROM AUTHOR]

Focuses on the use of fetal-cell injections for patients with spinal cord injuries and amyotrophic lateral sclerosis in Beijing, China. Historical background of the unconventional treatment for neurological disease; Assessment of the effectiveness of the technique of injecting fetal cells into patients; Claims of Hongyun Huang who developed the treatment concerning the efficacy of the technique in improving the quality of life for people with spinal injuries.

Urban sentiment, as people' perception of city environment and events, is a direct indicator of the quality of life of residents and the unique identity of a city. Social media by which people express opinions directly provides a way to measure urban sentiment. However, it is challenging to depict collective sentiments when integrating the posts inside a particular place, because the sentiment polarities will eventually be neutralized and consequently result in misinterpretation. It is necessary to capture positive and negative emotions distinguishingly rather than integrating them indiscriminately. Following the psychological hypothesis that two polar emotions are processed in parallel and can coexist independently, a novel dual-polarity metric is proposed in this paper to simultaneously evaluate collective positive and negative sentiments in geotagged social media in a place. This new measurement overcomes the integration problem in traditional methods, and therefore can better capture collective urban sentiments and diverse perceptions of places. In a case study of Beijing, China, urban sentiments are extracted using this approach from massive geotagged posts on Sina Weibo, a Twitter-like social media platform in China, and then their spatial distribution and temporal rhythm are revealed. Positive sentiments are more spatially heterogeneous than negative sentiments. Positive sentiments are concentrated in scenic spots, commercial and cultural areas, while negative sentiments are mostly around transportation hubs, hospitals and colleges. Following the principle of sense of place, multi-source data are integrated to evaluate the effects of influencing factors. The variation of spatial factors aggravates the heterogeneity of urban sentiment. The discovered spatiotemporal patterns give an insight into the urban sentiment through online behaviors and can help to improve city functionality and sustainability. [ABSTRACT FROM AUTHOR]

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

Microorganisms are an important part of atmospheric particulate matter and are closely related to human health. In this paper, the variations in the characteristics of the chemical components and bacterial communities in PM10 and PM2.5 grouped according to season, pollution degree, particle size, and winter heating stage were studied. The influence of environmental factors on community structure was also analyzed. The results showed that seasonal variations were significant. NO3− contributed the most to the formation of particulate matter in spring and winter, while SO42− contributed the most in summer and autumn. The community structures in summer and autumn were similar, while the community structure in spring was significantly different. The dominant phyla were similar among seasons, but their proportions were different. The dominant genera were no-rank_c_Cyanobacteria, Acidovorax, Escherichia-Shigella and Sphingomonas in spring; Massilia, Bacillus, Acinetobacter, Rhodococcus, and Brevibacillus in summer and autumn; and Rhodococcus in winter. The atmospheric microorganisms in Beijing mainly came from soil, water, and plants. The few pathogens detected were mainly affected by the microbial source on the sampling day, regardless of pollution level. RDA (redundancy analysis) showed that the bacterial community was positively correlated with the concentration of particulate matter and that the wind speed in spring was positively correlated with NO3− levels, NH4+ levels, temperature, and relative humidity in summer and autumn, but there was no clear consistency among winter samples. This study comprehensively analyzed the variations in the characteristics of the airborne bacterial community in Beijing over one year and provided a reference for understanding the source, mechanism, and assessment of the health effects of different air qualities. [ABSTRACT FROM AUTHOR]

With the continuous development of urbanization in China, the country's growing population brings great challenges to urban development. By mastering the refined population spatial distribution in administrative units, the quantity and agglomeration of population distribution can be estimated and visualized. It will provide a basis for a more rational urban planning. This paper takes Beijing as the research area and uses a new Luojia1–01 nighttime light image with high resolution, land use type data, Points of Interest (POI) data, and other data to construct the population spatial index system, establishing the index weight based on the principal component analysis. The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018. Then the population spatial distribution was visualize using GIS technology. After accuracy assessments by comparing the result with the WorldPop data, the accuracy has reached 0.74. The proposed method was validated as a qualified method to generate population spatial maps. By contrast of local areas, Luojia 1–01 data is more suitable for population distribution estimation than the NPP/VIIRS (Net Primary Productivity/Visible infrared Imaging Radiometer) nighttime light data. More geospatial big data and mathematical models can be combined to create more accurate population maps in the future. [ABSTRACT FROM AUTHOR]

for the longitudinal seismic response analysis of a tunnel structure under asynchronous earthquake excitations, a longitudinal integral response deformation method classified as a practical approach is proposed in this paper. The determinations of the structural critical moments when maximal deformations and internal forces in the longitudinal direction occur are deduced as well. When applying the proposed method, the static analysis of the free-field computation model subjected to the least favorable free-field deformation at the tunnel buried depth is performed first to calculate the equivalent input seismic loads. Then, the equivalent input seismic loads are imposed on the integral tunnel-foundation computation model to conduct the static calculation. Afterwards, the critical longitudinal seismic responses of the tunnel are obtained. The applicability of the new method is verified by comparing the seismic responses of a shield tunnel structure in Beijing, determined by the proposed procedure and by a dynamic time-history analysis under a series of obliquely incident out-of-plane and in-plane waves. The results show that the proposed method has a clear concept with high accuracy and simple progress. Meanwhile, this method provides a feasible way to determine the critical moments of the longitudinal seismic responses of a tunnel structure. Therefore, the proposed method can be effectively applied to analyze the seismic response of a long-line underground structure subjected to non-uniform excitations. [ABSTRACT FROM AUTHOR]

The accuracy of passive satellite cloud top height (CTH) retrieval shows regional dependence. This paper assesses the CTH derived from the FY-4A and Himawari-8 satellites through comparison with those from the ground-based millimeter radar at two sites: Yangbajing, Tibet, China (YBJ), and the Institute of Atmospheric Physics (IAP), Beijing, China. The comparison shows that Himawari-8 missed more CTHs at night than FY-4A, especially at YBJ. It is found that the CTH difference (CTHD; radar CTH minus satellite CTH) for FY-4A and Himawari-8 is 0.06 ± 1.90 km and −0.02 ± 2.40 km at YBJ respectively, and that is 0.93 ± 2.24 km and 0.99 ± 2.37 km at IAP respectively. The discrepancy between the satellites and radar at IAP is larger than that at YBJ. Both satellites show better performance for mid-level and low-level clouds than for high-level clouds at the two sites. The retrievals from FY-4A agree well with those from Himawari-8, with a mean difference of 0.08 km at YBJ and 0.06 km at IAP. It is found that the CTHD decreases as the cloud depth increases at both sites. However, the CTHD has no obvious dependence on cloud layers and fractions. Investigations show that aerosol concentration has little impact on the CTHD. For high and thin clouds, the CTHD increases gradually with the increase of the surface temperature, which might be a key factor causing the regional discrepancy between IAP and YBJ. [ABSTRACT FROM AUTHOR]

The 8 m laser interferometer prototype facility is currently being constructed at the Institute of Mechanics, Chinese Academy of Sciences in Beijing, China. It aims to perform laser interferometer experiments and pico-meter precision detection and calibration for Taiji pathfinder mission. The seismically isolated ground and passive vibration isolation are interconnected and the optical benches are stabilized by them, which can form two low-noise testbeds inside a 40 m3 ultra-high vacuum system. An on-ground laser interferometer demonstration used for satellite–satellite tracking will be constructed. In this article, the experimental facility and the employed methods will be described, and the technical details of subsystems will be covered in future papers. [ABSTRACT FROM AUTHOR]

Geothermal energy is a clean energy source that can potentially mitigate greenhouse gas emissions, as its use can lead to a lower mitigation cost. However, research on the economic impacts of the geothermal industry is scarce. This paper describes the effect of the geothermal industry, its economic input and output, using Beijing as a case study. This paper adopts the input–output model. The results show that the demand for and input use of the geothermal sector vary greatly across industrial sectors: electricity, heat production, the supply industry and general equipment manufacturing have the greatest direct consumption coefficient for the geothermal industry. When considering direct and indirect demand, it is clear that the geothermal industry has a great effect on different industrial sectors in diverse ways. Its influence coefficient and sensitivity coefficient are 1.2167 (ranked 11th) and 1.2293 (ranked 8th), respectively, revealing that it exerts obvious demand-pulling and supply-pushing effects on the regional economy. [ABSTRACT FROM AUTHOR]

With economic expansion having moderated to a "new normal" pace, supply structure of goods and competition state of logistics market has undergone major changes. In order to respond to these changes actively, China Railway Corporation has launched railway express freight block trains to meet with customers' demands. However, traditional operation conditions require every freight block train fully loaded to make best of transportation capacity. This has caused a series of problems to freight block trains, such as uncertain departure time, unstable running frequency and so on, which cannot meet with the customers' basic requirements for goods transport and logistics. So it is an urgent task to study the suitable cars' number and running frequency of railway express freight block trains so as to reduce customers' costs and increase China Railway Corporation's profits. Therefore, this paper analyzes the calculation method of customers' generalized transportation cost and profits of running a railway express freight block train. Then, an optimization model for cars' number and running frequency of freight block trains is proposed. The objective functions of this model are the minimum general transportation costs of customers and the maximum profits of China Railway Corporation. The model constraints are about transportation capacity, railway freight stations' operating capacity and so on. Taking railway express freight block trains operated between Beijing and Shanghai as an example, the cars' number and running frequency are calculated which can effectively reduce customers' cost and increase China Railway Corporation's profits. The results can prove the feasibility of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

Building data-driven intelligent transportation is a significant task for establishing data-centric smart cities, and exceptionally efficient and accurate traffic flow prediction (TFP) is a crucial technology in constructing intelligent transportation systems (ITSs). To address the computation and storage problems of processing traffic flow big data with the centralized model on a traditional mining platform, we propose a distributed long short-term memory weighted model combined with a time window and normal distribution based on a MapReduce parallel processing framework in this paper, named as WND-LSTM. More specifically, under the Hadoop distributed computing platform, a distributed modeling framework of forecasting traffic flow on MapReduce is developed to solve the existing issues of storage and calculation in handling large-scale traffic flow data with the stand-alone learning model. Moreover, a distributed WND-LSTM model is presented on the MapReduce-based distributed modeling framework to enhance the accuracy, efficiency, and scalability of short-term TFP. Finally, we forecast the traffic flow on the Sanlihe East Road of Beijing in China using the proposed WND-LSTM model with the real-world taxi trajectory big data. In particular, the extensively experimental results from a case study demonstrate that the MAPE value of WND-LSTM is 88.48%, 65.79%, 70.46%, 68.21%, 66.95%, 68.43%, and 70.41% lower than that of the autoregressive integrated moving average (ARIMA), logistical regression (LR), support vector regression (SVR), k-nearest neighbor (KNN), stacked autoencoders (SAEs), gated recurrent unit (GRU), and long short-term memory (LSTM), respectively, and achieves 71.25% accuracy improvement on average. [ABSTRACT FROM AUTHOR]

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

The investigation on the mechanical mechanism of the retaining and protection structure of superdeep excavations is an important subject, and its deformation control research is particularly important. Taking the deepest foundation pit with full-section overall excavation in Beijing as an example, this paper studies the stress and deformation characteristics of composite soil nailing wall and anchored soldier pile wall combined retaining system under complex geological conditions of multi-layer groundwater during the process of excavating 31.4 m deep. The Midas simulation software and monitoring data are used to analyze the construction process of foundation excavation. The simulated results and monitored values of anchor force, ground settlement, and soil deformation during foundation excavation are analyzed and discussed to verify the reliability of the model. The spatial effect of internal force and deformation of composite retaining and protection structure for superdeep foundation excavation is discussed, and the parameters affecting the retaining structure are analyzed and investigated. The research shows that with the increase of excavation depth, both the extremum of the bending moment and the extremum of lateral displacement of retaining piles are increasing, and the position of the extremum of bending moment is located near the excavation face; then, the bending moment reaches the maximum when the foundation pit is excavated to the bottom. Within a certain range, increasing the rigidity or the embedded depth of the retaining pile or the prestress of the anchor can effectively control the lateral displacement of the excavation. However, when the rigidity of retaining pile is too large, its lateral displacement does not change significantly. Similarly, when the embedded depth is too long or the prestress of the anchor is too powerful, the effect of controlling deformation is also not obvious. The research results will provide theoretical basis and practical experience for the design and construction of superdeep excavations. [ABSTRACT FROM AUTHOR]

Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction. [ABSTRACT FROM AUTHOR]

This paper extends existing Bourdieusian theorisations of the educational involvement of working-class parents by adding the less-examined axes of rural origin and migration status with an intersectional approach. It focusses on the 'labourer' families involved in internal rural–urban migration in China. Semi-structured interviews were conducted in Beijing and Shanghai with 32 migrant parents, teachers and head teachers. It examines how the intersection of rural origin, migration status and working-class identities shapes the parents' habitus and their exertion of capital in the urban education field. The findings show that the intersection of two aspects of their habitus—one, resulting from their rural background, leads them not to treat themselves as academic educators, and a second, arising from their migrant working-class status, the necessity to 'strive for survival'. Since the parents' actions do not match with the teachers' expectations of home-school cooperation, they are identified as 'incompetent'. [ABSTRACT FROM AUTHOR]

Anthropogenic heat (AH) is an important part of urban surface energy balance. Although AH affects regional climates through changing land-atmosphere interactions, the climate forcing from AH are not usually calculated in state-of-the-art regional climate simulations. In this paper, the spatial pattern of AH in 20 automatic weather station sites in the Beijing municipal administrative area is parameterized by employing nighttime light data. Two experiments were designed and performed to quantify the influence of AH on the surface balance of energy and water through running the Integrated urban Land Model (IUM). The results show that due to accounting for AH, the simulated LST increases; the net radiation decreases around noon; the absolute value of the ground heat flux increases around noon; the sensible heat flux increases in the daytime; the evapotranspiration decreases around noon and increases in the morning and evening; volumetric soil moisture and soil water storage decrease; aggregated evapotranspiration increases. [ABSTRACT FROM AUTHOR]

In order to research the new integrity evaluation technology of gas pipeline and promote the mature development of gas pipeline integrity management, this paper deeply analyzes the characteristics of Beijing gas pipeline and combines with the methods of finite element modeling, statistical analysis, mathematical fitting and experimental verification. Based on the Spangler-lowa method, the load effects of gas pipelines with different pressure grades are analyzed, a new method for evaluating the external load carrying capacity of gas pipeline with local corrosion defects was developed, and a method for evaluating the internal pressure carrying capacity of gas pipeline was determined. The research shows that the integrity structure evaluation of gas pipeline should be evaluated according to the pressure level. Medium- and low-pressure gas pipeline is mainly affected by external load, and deformation is the evaluation basis. The sub-high-pressure gas pipeline is affected by both external load and internal pressure, and its deformation and stress are taken as the evaluation basis respectively. High-pressure gas pipeline is mainly affected by internal pressure, and stress is the evaluation basis. [ABSTRACT FROM AUTHOR]

Aiming to provide a generic modeling framework for finding stop plan and tickets allocation of high-speed railway, we first propose a stop plan and tickets allocation collaborative optimization model in this paper, which is established to maximize the passenger satisfaction degree and the average seated occupancy rate. Due to the randomness and uncertainty of passenger demand, uncertain variables are set and the primal model is an uncertain model. And then, the model is transformed into equivalent deterministic model based on uncertainty theory. Because of the computational complexity of the model, especially for the large-scale real-world instances, we develop a Lagrangian relaxation (LR-based) heuristic algorithm that decomposes the primal problem into two sub-problems and thus is able to find good solutions in short time. Finally, a numerical experiment based on the operation data of high-speed railway from Beijing south Station to Shanghai Hongqiao Station is implemented to verify the effectiveness and feasibility of the proposed approaches. [ABSTRACT FROM AUTHOR]

Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi, ride-hailing service, subway, regular bus, airport bus, etc.), we accurately extract candidate stations, which are very popular with passengers and convenient for parking and transfer, through public transportation demand level calculation, iterative clustering and POI matching. Then, the candidate stations need to be partitioned appropriately by selecting suitable features and calculating the similarity of candidate stations, so as to make the stations within each group a moderate size and have a consistent spatial orientation. Finally, a line generation and vehicle scheduling algorithm, which is compatible with multi-vehicle, high success rate of ride-sharing matching and low cost, is designed to realize accurate and rapid operation scheduling within each group according to the situation of passengers booking tickets. We have carried out experiments in Wangjing and Yayuncun, and the results show that our method can satisfy passenger demand fast and accurately. [ABSTRACT FROM AUTHOR]

City management plays an important role in the era of urbanization. Understanding city regions and urban mobility patterns are two vital aspects of city management. Numerous studies have been conducted on these two aspects respectively. However, few work has considered combining city region partition and mobility pattern mining together while these two problems are closely related. In this paper, we propose region-aware mobility pattern mining framework, which jointly finds the precise origin and destination region partitions while extracting mobility patterns. We formulate it as an optimization problem of maximizing OD's correlations with spatial constraints. Kernelized ACE, is proposed to solve the problem by learning feature representations that guarantee both objectives. Evaluation results using Beijing's taxi data show that the extracted features are appropriate for this problem and our approach outperforms all the other methods with ∼ 0.3% spatial overlap and 86.43% OD correlation. Our case studies on New York City's urban dynamics and Beijing's three-year consecutive analysis also yield insightful findings that reveal city-scale mobility patterns and propose potential improvement for city management. [ABSTRACT FROM AUTHOR]

The natural estrogen 17β-estradiol (17β-E2) is a major endocrine disruptor. Accordingly, due to their frequent presence in global surface waters, prolonged exposure to estrogen-contaminated water may disrupt sexual development in animals. It has adverse effects on wildlife and humans. To date, the most effective strategy for estrogen removal from the environment is biodegradation using microorganisms. To this end, we isolated a strain of Lysinibacillus sphaericus, namely DH-B01, from a contraceptive factory in Beijing. The experimental results revealed that the bacterium has a high capacity to degrade estrogen, with a 17β-E2 degradation rate of about 97%, and produces the secondary metabolite estrone. In addition, a series of genes involved in steroid metabolism and stress response in L. sphaericus sp. DH-B01 were predicted, and several key genes with high similarity to those of other strains were subjected to sequence alignment to find their conserved regions. This is the first study of the ability of L. sphaericus strains to degrade estrogens and the degradation mechanism involved. This work advances the genomic study of estrogen-degrading strains and the study of bacterial estrogen degradation mechanisms. In this paper, a novel bacterial strain capable of degrading 17β-E2 was studied. L. sphaericus sp. DH-B01 can effectively degrade 17β-E2. During the degradation process, 17β-E2 can be gradually metabolized to a substance without estrogen activity. By analyzing the enzymatic reactions in the metabolic process, we found genes with high similarity to reported 17β-HSD. L. sphaericus sp. DH-B01 was found to degrade 17β-E2. There are many types of bacteria that are currently being studied for the degradation of estrogen, but L. sphaericus sp. DH-B01 is the only strain of L. sphaericus that has been shown to degrade estrogen. This work advances the genomic study of estrogen-degrading bacterial strains and the study of bacterial estrogen degradation mechanisms. Additionally, it explores the correlation between different L. sphaericus strains. The differences play an important role and further enrich the functionality and diversity of L. sphaericus strains. In subsequent studies, the specificity of L. sphaericus sp. DH-B01 can be applied to different environments for future environmental restoration. [ABSTRACT FROM AUTHOR]

While most discussions of the relationship between art and technology focus on "new media" practice, there are substantial opportunities to consider technology through "traditional media" such as painting and sculpture. Art and technology intersect through the process and desire of imagination and, in particular, through the attempt to imitate life itself in terms of creation. In this paper, I consider the practice of Beijing-based artist Zhou Song, who images and imagines new worlds as constituted by social robots. Drawing on the frameworks of estrangement, the uncanny, and Gilles Deleuze's notion of the encounter, I analyze several of Zhou's works in order to understand possibilities for thinking through the figuration of social robots in relation to our broader understandings of alterity. I argue that Zhou's hyperrealistic images, which use quotation as a device through which to balance the uncanny with the familiar, prompt an encounter that challenges the cognitive ordering of the world. This research contributes to the developing discourse on social robots through a cultural lens. [ABSTRACT FROM AUTHOR]

Using a heterogeneity stochastic frontier model (HSFM), we empirically investigated the economic efficiency of Beijing-Tianjin-Hebei from 2003 to 2016 and its influencing factors. The key findings of the paper lie in: 1) in Beijing-Tianjin-Hebei, the overall economic and technological efficiency tended to increase in a wavelike manner, economic growth slowed down, and there was an obvious imbalance in economic efficiency between the different districts, counties and cities; 2) the heterogeneity stochastic frontier production functions (SFPFs) of Beijing, Tianjin and Hebei were different from each other, and investment was still an important impetus of economic growth in Beijing-Tianjin-Hebei; 3) economic efficiency was positively correlated with economic agglomeration, human capital, industrial structure, infrastructure, the informatization level, and institutional factors, but negatively correlated with the government role and economic opening. The following policy suggestions are offered: 1) to improve regional economic efficiency and reduce the economic gap in Beijing-Tianjin-Hebei, governments must reduce their intervention in economic activities, stimulate the potentials of labor and capital, optimize the structure of human resources, and foster new demographic incentives; 2) governments must guide economic factors that are reasonable throughout Beijing-Tianjin-Hebei and strengthen infrastructure construction in underdeveloped regions, thus attaining sustainable economic development; 3) governments must plan overall economic growth factors of Beijing-Tianjin-Hebei and promote reasonable economic factors (e.g., labor, resources, and innovations) across different regions, thus attaining complementary advantages between Beijing, Tianjin, and Hebei. [ABSTRACT FROM AUTHOR]

Limited evidence was seen as the association between fine particulate matter (PM2.5) and physician visits for allergic rhinitis (AR), especially in countries with extreme air pollution exposure. This paper addressed the issues about the association between PM2.5 and daily outpatient visits for AR among individuals residing in Beijing, China. Data on daily outpatient visits for AR obtained from Beijing Medical Claim Data for Employees and daily PM2.5 concentrations available from US embassy reports were linked by date from January 1, 2010, to June 30, 2012. A time-series analysis was conducted with a generalized additive Poisson model to assess the association between PM2.5 and AR, adjusting for daily average temperature, relative humidity, day of the week, calendar time, and public holiday. Totally, 229,685 outpatient visits for AR were included in the analysis. The daily mean (SD) concentration of PM2.5 was 99.5 (75.3) μg/m3 during the study period. We found that a 10-μg/m3 increase in PM2.5 content was associated with a 0.47% (95% CI: 0.39% to 0.55%) increase in the number of outpatient visits on the same day. Furthermore, results from subgroup analyses suggested that the association was consistently significant among the groups of different ages (< 65 years and ≥ 65 years) and gender. However, this study failed to find a statistically significant association in the autumn season but found significant positive associations during the spring and summer seasons (P for interaction < 0.001). This study indicated a possible association between PM2.5 and AR outpatients, which may benefit further researches in studying PM2.5 and its influence on diseases in a real and seriously air-polluted context. [ABSTRACT FROM AUTHOR]

The catastrophic Sanyanyu and Luojiayu debris flows, which were induced by heavy rain, struck Zhouqu County in Gannan Prefecture, Gansu Province, at approximately midnight, 7 August 2010 (Beijing time, UTC + 8), causing 1765 fatalities and huge economic loss. The ZHQ seismic station is located approximately 170 m west of the outlet of the Sanyanyu gully, and its power system was destroyed by the Sanyanyu debris flow when its leading edge reached the vicinity of the seismic station. In this paper, seismic signals recorded approximately 10 min before its termination are collected and analyzed to study the Sanyanyu debris flow. A double-exponential model is first proposed to quantitatively characterize seismic energy distributions in the frequency domain, which reveals that the peak frequency of seismic signals is around 5 Hz. Influenced by the Doppler effect, the peak frequency of the N–S component is the highest, and the U–D component is the lowest. Time–frequency analysis is applied to the seismic signals. From the spectrogram, it is easily observed that the formation time of the Sanyanyu debris flow is around 23:33:10. The entire debris flow is divided into three phases with distinct frequency characteristics, using 23:36:20 and 23:37:35 as crucial times. The frequency energy distributions in the first two phases are relatively stable, and are constrained in 0–8.8 Hz and 0–17.8 Hz, respectively. For the third phase, the upper boundary of frequency energy increases in a nearly linear manner, reaching approximately 35 Hz at the end. We calculate synthetic seismograms of the Sanyanyu debris flow. Generally, synthetic seismograms have morphological features and characteristics in key stages similar to those of the actual seismic records, and their maximum values are of the same magnitude. Our results suggest that the seismic source of a debris flow can be represented by a single-force model, and reveal that real-time monitoring and rapid identification of potential debris flows using broadband seismic network records is possible, which can provide approximately 15 min of pre-event warning for local residents and hopefully save many lives. [ABSTRACT FROM AUTHOR]