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• Index of recycling sustainability for assessing waste paper recycling. • Integrating methodology of GIS and value chain model. • Historical changes of informal recyclables distribution sites in Beijing. • Value chain of waste paper recycling in Beijing may face challenges in the future. • Policy implications to improve waste paper recycling in Beijing. China established a self-organized and market-driven recycling system, which was dominated by the informal sectors. In recent years, the amount of domestically-recycled waste paper grew slower than expectation in China, which may be resulted from a decline in economic sustainability of current recycling system. For understanding the waste paper recycling system in most cities in China, the economic mechanism remains unclear and the city-level data is extremely insufficient. In this work, an index of recycling sustainability (IRS, benefit divided by cost) is analyzed with a resolution of 1 km2 grid in Beijing City, by adopting value chain and GIS methodology. Five degrees of IRS are defined, from high-degree (IRS > 1.10) to low-degree (IRS < 0.95). Different stakeholders in the informal waste paper recycling system were interviewed to fill the data gap. Results show that: (1) from 2015 to 2018, the informal recycling of waste paper accounted for approximate 80% in Beijing; (2) the number of informal recyclables distribution sites decreased from 27 to 11, and their average distance to the city-center rose from 27.5 km to 40.9 km; (3) in 2015 and 2018, the grids with high-degree IRS accounted for 99.5% and 89.2%, respectively, indicating a sustainable waste paper recycling industry in Beijing; and (4) according to the scenario analysis, if the operating cost rises by 30%, the grids with low-degree IRS accounts for 98.5%, indicating a nontrivial challenges when the recycling cost keeps increasing in the future. Policy recommendations are put forward for a more sustainable paper waste recycling system in China. [ABSTRACT FROM AUTHOR]

The present study was a review of cases of central cystadenocarcinoma over the past 29 years and provides clinical, radiological, and pathological information about these rare lesions. Six cases of central cystadenocarcinoma treated between 1991 and 2019 at Peking University Hospital of Stomatology in Beijing, China, were retrospectively analysed. A comprehensive review of clinical records was summarised and the histological diagnosis was revised using the 2017 World Health Organization criteria. The mean age of patients with central cystadenocarcinoma was 63 (range 51-75) years, and the male:female ratio 1:1. The clinical signs included localised swelling, pain, lower lip numbness, and trismus. There were more cases in the mandible than in the maxilla. All lesions were unilocular or multilocular in radiolucent regions with or without clear margins. The preferred treatment of central cystadenocarcinoma was surgical excision with wide margins, and no local recurrence was found during follow-up. Central cystadenocarcinoma often occurred in middle-aged or elderly patients. Because cystadenocarcinoma is somewhat rare, metastatic tumours of the jaw should be considered when diagnosing cystadenocarcinoma. [ABSTRACT FROM AUTHOR]

• Au@Ag core-shell nanoparticles for highly sensitivite SERS assay. • Fabrication of Au@Ag-pLL on glass nanofibrous paper as SERS substrate. • A paper-based SERS nanosensor for the sensitive detection of methamphetamine in sewage. • Evaluation of community-wide illicit drugs consumption ifor wastewater-based epidemiology. Wastewater-based epidemiology (WBE) is a powerful technique for monitoring illicit drugs of abuse in the community. Here, we report upon a surface-enhanced Raman spectroscopy (SERS) sensor for the sensitive and selective detection of methamphetamine based upon the assembly of noble metal core-shell nanoparticles on a bespoke glassy nanofibrous electrospun paper matrix. The hierarchical structure of the fibrous paper, modified with the synthesized Au@Ag core-shells (Au@Ag) gave strong SERS signalling, enabling us to evaluate the community-wide prevalence of methamphetamine in wastewater treatment plants within Beijing. We show that, when normalized for the daily flow of the wastewater treatment plants and for population density, higher mass loads of drugs are generally found in sewage influent from urban areas, implying greater local methamphetamine usage than that in less populated areas. The user-friendly and disposable paper sensors demonstrate the applicability of rapid on-site illicit drug detection, illustrating the application to wastewater-based epidemiology, which has the potential to inform government agencies regarding societal interventions. [ABSTRACT FROM AUTHOR]

Air pollution poses a grave threat to human health and everyday life. Accurate air-quality prediction plays a crucial role in effectively preventing and controlling air pollution. A multi-graph spatial-temporal attention network is proposed to predict the air quality in a given area by analyzing the interconnections between stations and the individual characteristics of each station. Firstly, this paper constructs multi-scale spatial-temporal graphs from spatial and temporal perspectives to effectively capture the interconnections between stations, thereby comprehensively understanding the spatial-temporal relationships among them. Secondly, incorporating the nonlinear temporal correlation of station data, this paper proposes a temporal multi-graph attention-fusion module to integrate information from both neighboring stations and the station itself. Finally, predictions are made using a spatial-temporal graph network. The experiments in this paper were based on air-quality data from Beijing and Tianjin, and the following experimental results were obtained. (1) The proposed model outperforms the baselines significantly in both single-step and multi-step prediction tasks. (2) The ablation study confirms that the graph constructed in this paper contributes to improving the performance of the model. (3) Comparing the performance of attention components, the proposed attention mechanism exhibits better performance than other attention mechanisms. [ABSTRACT FROM AUTHOR]

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

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

The Chinese government aims to balance employment stabilization and emissions reduction when it initiates an emissions trading scheme (ETS) to control carbon emissions. However, the policymakers should not neglect the role of imperfectly competitive market structure and market power from incumbent firms. In view of this, classical framework of Berman and Bui's (2001) is extended in this paper to clarify the theoretical mechanism of employment effect of the ETS considering firm-level market power. In addition, based on the firm-level panel data from 2008 to 2019 of China, the employment effect of each pilot ETS is measured with the method of difference-in-difference-in-differences. Then a moderating effect model is established to verify the role of firm-level market power on the heterogeneous employment effect among the pilot ETSs. Moreover, the employment effect is further decomposed to evaluate the influential mechanism from the perspective of firms' strategic behavior. It is found that there is negative employment effect of China's ETS policy, due to the significant moderating role of firm-level market power. This decrease in labor demand is mainly dominated by a negative output effect rather than a positive substitution effect because strategic allowance sellers with stronger market power tend to hire fewer employees to reduce carbon emissions by controlling production scale. However, the heterogeneity of policy effects resulted by market structure is also identified in different pilot regions. Employment dividend has been found in Beijing and Tianjin where strategic allowance buyers manipulate the carbon market and hire more employees to consolidate their market power. Therefore, policymakers should improve the design of the ETS to overcome the adverse employment effect duo to the region-specific market structure. [ABSTRACT FROM AUTHOR]

Analysis of the accessibility impacts of logistics suburbanization is critical for logistics land-use planning practices. To understand these impacts, this paper conceptualizes logistics service accessibility from two perspectives in terms of urban logistics infrastructure networks (ULINs) and logistics land use stakeholders in the network, and proposes a novel quantitative approach for their measurement. We measure the service accessibility of ULINs via solving a bi-objective binary integer program which considers the facility spatial coverage and vehicle path planning. Further, cumulative-opportunity freight accessibility models are proposed to measure the service accessibility of logistics land use stakeholders while accounting for their different logistics demands. The proposed method is capable of measuring the spatiotemporal impacts of logistics suburbanization on the service accessibility of ULINs and the corresponding stakeholders. We use the real data of Beijing as a testbed and our results reveal that (1) there is considerable variation in the service accessibility of ULINs and logistics firms' service accessibility with respect to different logistics facility locations and (2) choosing logistics facilities' locations requires careful consideration of the differences in ULINs' service accessibility to mitigate uneven service availability on the receiving side. The paper highlights the potential for freight accessibility in urban freight planning and provides effective quantitative evaluation approaches to support better practices in logistics land use and transport planning. • Logistics service accessibility has been conceptualized from two views of infrastructure network and land-use stakeholders. • Infrastructure-based freight accessibility model has been proposed to measure the service performance of ULINs. • The impacts of logistics suburbanization on the service accessibility of ULINs, goods suppliers and receivers are examined. • Policy suggestions for planning attractive logistics facility locations to encourage logistics clusters are provided. [ABSTRACT FROM AUTHOR]

Despite the rapid development of China's digital economy and many studies involving the development of digital economy, there is no unified standard on the measurement of digital economy, and the existing accounting methods all have certain shortcomings. In view of this, this paper constructs a set of accounting system to measure the scale of digital economy industry, from the perspective of digital industrialization and industrial digitization, based on input-output tables. We separate digital economy from traditional industries by splitting and merging IO tables using data such as the Economic Census Yearbook and SAT to calculate the scale of digital economy industry. In this paper, data from Beijing, Tianjin and Hebei provinces in 2017 were used as an example for the calculation. The accounting results show that there is still a large gap in digital economy industry development between Beijing, Tianjin and Hebei, with the total digital economy industry scale, industry digitization scale, digital industrialization scale and the proportion of digital economy value added to GDP in Beijing being much higher than those in Hebei and Tianjin. In addition, due to the characteristics of the respective industrial structures in Beijing, Tianjin and Hebei, the percentages of the four major categories in digital industrialization also show different performance. The share of digital technology application industry in Beijing is much higher than that in Tianjin and Hebei, while the share of digital product manufacturing in Hebei and Tianjin is high. [ABSTRACT FROM AUTHOR]

• The geographical factors cannot be ignored in the site selection of WTE plant. • SVNSs makes up for the weakness of traditional fuzzy sets in information description. • The weight calculation method in SVNSs background is proposed. • EDAS can reduce the adverse impact of decision makers' own reasons on the results. • The combination of GIS and MCDM can improve the precision of site selection. Waste-to-energy (WtE) plant has been attached great importance by more and more countries because of the harmless, energy reducing and recycling characteristics, and has become an important part of the city's normal operation and energy structure. Whereas, the reasonable site selection of the WtE plant is the key to ensure its sustainable development. This paper is designed to provide a two-stage model for the site selection of WtE plants. Firstly, a comprehensive assessment criteria system including environmental, economic, technical and social factors is established by selecting the reasonable influence criterias from the current relevant literature. Considering the fuzziness of the criteria and the bounded knowledge of decision makers, the single-valued neutrosophic sets (SVNSs) are introduced to describe the fuzzy information, and creatively combined with decision-making and trial evaluation laboratory-analytical network process (DANP) method to determine the influence grades of criterias and calculate the weights. After that, the relevant geographic information layers are collected and the preliminary alternatives are obtained by Geographic information system (GIS) from an overall perspective, then the extend evaluation based on distance from average solution (EDAS) method is utilized to rank the alternatives under the SVNSs environment. Finally, the model is tested to select the optimal alternative for siting the WtE plants in Beijing as an example. It is verified that the model is of great stability and feasibility by sensitivity analysis and comparative analysis. The result shows that this paper can provide a new theoretical basis for the planning of WtE plants. [ABSTRACT FROM AUTHOR]

Once an urban rail transit (URT) system breaks down or is deliberately damaged, it will cause tremendous pressure on the whole urban transportation system. Hence, its ability to deal with incidents has become an important research field. Existing studies seldom consider the entire cycle of an incident and ignore changes in passenger travel behavior during emergency events. In this paper, resilience is defined as the ability of absorbing, resisting and recovering when the URT network fails. We propose a multi-dimensional evaluation model for measuring the resilience by calculating multiple abilities with consideration of multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc. Finally, we present a case study based on real-world data from the Beijing Subway network to illustrate the effectiveness and applicability of the proposed model. Through the resilience analysis, we recognize the critical stations, measure emergency recovery ability and provide necessary information support for reducing the risk of URT incidents. • The proposed multi-dimensional model evaluates the network resilience from three aspects: absorption ability, resistance ability and recovery ability. • Multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc., are considered in the proposed evaluation model. • A real-world large-scale case study of the Beijing Subway network is analyzed in the paper. [ABSTRACT FROM AUTHOR]

• Provide a social cost assessment model for municipal solid waste management. • The social cost of incineration treatment accounted for 81.9%. • Municipal solid waste management in Beijing has great potential for saving social costs. • Recommend subsidies for waste utilization based on social cost assessment. • Provide information for the whole society through social cost assessment. The increased municipal solid waste generation poses ponderous pressure on the economy, environment, and public health. The current waste treatment process has multiple limitations. To inform policymakers on the best practices and feasibility, we develop a more comprehensive social costing model to assess the impacts of municipal solid waste management throughout its life cycle. The prominent findings show that the life cycle social cost of municipal solid waste in Beijing in 2021 is 12.4 billion yuan. Incineration has the highest social cost, totaling 10.172 billion yuan. The social cost per unit of waste incineration is 2,045 yuan/t, which is higher than that of landfill (1,288 yuan/t), composting (1,132 yuan/t), anaerobic digestion (1,057 yuan/t), and recyclables resource utilization (-344 yuan/t). The life cycle assessment results show that economic costs, including collection, transportation, and treatment costs, account for about 61%, and health loss costs account for about 37%. The scenario analysis suggests a significant potential for social cost savings from food waste and recyclables utilization. Ideally, a social cost reduction of almost 38% could be achieved. Error analysis examines the influence of variation in uncertain parameters on the evaluation results. This paper provides scientific strategies for optimal investment and decision-making on the comprehensive municipal solid waste management. These findings could provide an essential reference for policymakers and stakeholders in municipal solid waste management, replicated in different cities and other emerging economies. [ABSTRACT FROM AUTHOR]

When building new airports various strategies for initiating new air routes prominently affect the overall system performance. From a perspective of disruption management, however, very little is known regarding the impacts of different strategies exerted to the resilience of airline networks in a multi-layer system configuration. In this paper, we established a multi-layer China airline network (CAN) using route data from 46 airlines in mainland China and proposed four optimization strategies for adding new routes based on service-oriented perspectives. The geographical traits of the multi-layer CAN was first explored and, taking the Daxing International Airport (a new-built airport in Beijing, China) as a demonstrative example, we then conducted both qualitative and quantitative analyses to investigate the impact of the proposed strategies and their effectiveness of improving network resilience. It is revealed that those strategies have heterogeneous effects on the network performance in terms of meeting passenger demands and balancing overall connectivity at a national strategic level, and the overall resilience of the CAN could be significantly improved under the selected strategy by 59.8% on average. Based on the findings, several managerial and policy implications are put forward and discussed. This paper sheds new lights on resilience-building strategies for service carriers and airport managers and is also of value to regional governments for offering incentives for expanding connections to new destinations. • A comprehensive multi-layer airline network is constructed. • The network consists of 46 layers of sub-networks from mainland China. • Practical strategies are proposed and tested for improving network resilience. • Optimized strategies improve the overall resilience of the multi-layer network by 59.8% on average. • Implications for managing a growing multi-layer airline network are provided. [ABSTRACT FROM AUTHOR]

• This paper takes Beijing as a case to study regional economic resilience. • We systematically explore the effects of COVID-19 on industrial dynamics. • Individual-owned firms located in high-risk regions are more vulnerable. • The epidemic shock leads to cascading effects among the local industrial network. The COVID-19 epidemic has altered the economic geographies of cities. However, the academic focus on urban industries and their structural dynamics is far from enough. In this paper, we integrate literature from economic geography, business studies and network sciences, and design an adaptation framework to theorize urban economic resilience. Taking the COVID-19 outbreak in Xinfadi, Beijing during the summer of 2020 as a case study, we use Logit regressions and DID estimations to identify the impacts of the epidemic on local industrial dynamics. Our results support that the COVID-19 outbreak significantly increases the number of business exits. The negative impacts are concentrated in high-risk regions, pointing to specific industries and individual-owned firms. Furthermore, we use a machine learning method to visualize the dynamics of industry networks in a small-scale urban area, and then simulate the transmission paths of industry-failure. In doing so, we contribute to current economic resilience literature that firm-level adaptation and industrial dynamics complement regional adaptation theory, while the cascading effects in the industry network underpin the micro foundations of resilience formation. [ABSTRACT FROM AUTHOR]

• Substantial surveys identify that bus queuing, merging, and repeated opening doors can significantly affect bus dwell time. • A Markov chain is designed to characterize the effect of vehicles in adjacent lane and passengers on bus merging. • A comprehensive model is proposed to estimate bus dwell time and bus queuing time. • The actual performance of the comprehensive model is validated in real cases in three cities. • The probability of bus overtaking is highly correlated with bus service time and average stop service time. The precise estimation of bus dwell time (BDT) is immensely important as it directly impacts the reliability of bus schedules and the accuracy of arrival time predictions. Various factors—such as the behavior of buses merging into the main traffic flow, uneven distribution of passengers on board, and frequent bus queues—jointly lead to a high variability of the BDT. To achieve an accurate estimation of BDT across various stop types, a comprehensive BDT model is developed in this paper. First, based on berth occupancy, probabilistic models are constructed for different bus arrival scenarios, which are then used to study corresponding bus queuing times. Second, the Bureau of Public Roads function is employed to characterize the obstruction effect occupants on the bus impose on boarding passengers. Combined with this function, a bus service time model is proposed considering the bus opening doors more than once and in-vehicle crowding. Third, the bus merging process is discretized to cope with the volatility of the time interval required for a bus to leave the bus bay. A Markov chain model is designed to calculate the time buses have to wait before merging. Case studies conducted in Qingdao, Chongqing and Beijing, China, demonstrate the effectiveness of the comprehensive model both during off-peak and peak periods. Furthermore, this paper incorporates overtaking rules into the comprehensive model to examine the influence of bus overtaking willingness and various overtaking policies on bus queuing times. Through numerical analysis, the key link is between willingness to overtake, service time and the likelihood of bus overtaking is disclosed. [ABSTRACT FROM AUTHOR]

In many densely populated mega-cities around the world, metro systems are becoming overly saturated by the ever-increasing travel demand, which results in overcrowded stations, excessive delay, and unsafe anxieties among riders. The concept of extra-long trains (i.e., trains longer than station platforms) was recently proposed in the literature as a promising way to increase a metro line's capacity without additional infrastructure construction. This paper develops a general modeling framework to optimize train block configuration and operation protocols of a metro line so that extra-long trains can be used under varying demand distribution and infrastructure setting. The design problem is formulated as the integration of two coupled vehicle routing problems which simultaneously optimizes the train block configuration plan, door opening strategy, stop skipping strategy, and train dispatch schedule. This paper proposes two customized solution methods, including an adapted savings heuristic and a destroy-and-repair algorithm. A series of hypothetical examples are tested to demonstrate how the proposed solution approaches outperform an existing commercial solver even for small to moderate problem instances. Moreover, two real-world case studies, with very different demand patterns from two continents, are presented to test the effectiveness of using extra-long trains. Our results show that a metro line with extra-long train operations is capable of serving up to 20%–30% more passenger demand as compared to that with only regular trains, while at the same time producing a similar or lower passenger average travel time. • Design of extra-long trains (i.e., longer than station platforms) in a general setting. • Mixed-integer program to optimize train block configuration and operation protocols. • Customized solution methods (adapted savings and destroy-and-repair). • Extensive experiments with hypothetical and real-world data (from Beijing and Santiago). • Results show significantly increase in metro line capacity without infrastructure construction. [ABSTRACT FROM AUTHOR]

A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative study of the tensile performance of plug-pin scaffold joints under different temperatures. The current research objects are mostly for the traditional low carbon steel scaffolding, and lack of research on the tensile performance of scaffolding joints in low temperature conditions. In this paper, based on the temporary seating facilities of the Beijing Winter Olympic Games complex, a new type of sorbite stainless steel was used. The tensile bearing capacity, damage mode and strain distribution patterns of the sorbite stainless steel joints and traditional Q355 joints under different temperature conditions were studied with finite element model and parametric analysis. The results indicated that for traditional Q355 joints, damage primarily occurs in the form of wedge bending deformation, and that decreasing temperature leads to increased tensile bearing and yield capacity. In contrast, for sorbite stainless steel joints, higher tensile bearing capacity was observed at +20 °C, but at −20 °C and − 40 °C conditions, the bend of the C-type card becomes more prone to crack. The accuracy of the ABAQUS finite element model is verified by comparing it with test results. Increasing the thickness of the U-type and C-type cards can slightly increase the tensile bearing capacity of the joint and a minimum wall thickness of 2.7 mm is recommended. Two suggestions are proposed for improving the joint design using sorbite stainless steel. The research provides a premise for further studies on the tensile performance of plug-pin scaffold joints. • A low-temperature loading test device and method for joints are proposed. It offers a test environment of −70 °C ∼ 150 °C. • The material properties of sorbite stainless steel and Q355 at −40 °C, −20 °C and room temperature are studied. • The mechanical properties of plug-pin scaffold joints using sorbite stainless steel and Q355 at low temperatures are studied. • The tensile properties of plug-pin scaffold joints at different low temperatures are investigated. • Theoretical load-displacement modeling of plug-pin scaffold joints is developed. [ABSTRACT FROM AUTHOR]

Electrochromic (EC) glazing provides a promising solution to actively adjusting the sunlight and radiant heat through windows in response to building visual and thermal environmental requirements, and control strategies play a critical role in the application of EC technology in buildings. However, there is a conflict between the visual and thermal environmental requirements for EC regulation during a cooling season. To solve this problem, this paper developed a predictive control strategy for EC glazing. It was achieved based on two theoretical models which could predict the energy performance of EC glazing at different tint states, including the solar radiation heat transmitted through the glazing and lighting energy consumption. The optimum glazing state was determined based on real-time minimized energy consumption. Study results showed that the predictive control strategy outperformed the rule-based control strategy, with percentage energy saving of an office room in Beijing, China from 8.04% to 12.78% over an entire cooling season. In addition, the paper evaluated the energy-saving of using EC glazing under the predictive control strategy in 28 Chinese cities. The results indicated that EC glazing was highly applicable in most of cities with energy saving from 2.35 kWh/m2 to 5.04 kWh/m2 over an entire cooling season. • A predictive control strategy for EC glazing was developed. • Theoretical models were established to predict the EC glazing energy performances. • Real-time minimized energy consumption determined the optimum EC glazing state. • Balance between the visual and thermal environment requirements was achieved. • The energy-savings of using EC glazing in 28 Chinese cities were assessed. [ABSTRACT FROM AUTHOR]

• analyze the energy efficiency of Beijing's industrial sectors from 2002 to 2018. • model industry heterogeneity of Beijing's industrial sectors accurately. • low energy efficiency of Beijing's industrial sectors due to management inefficiency. • the energy efficiency of heavy industry is higher than that of light industry. • explore the influencing factors of energy efficiency of Beijing's industrial sectors. Improving energy efficiency is one of the effective means to achieve China's carbon peaking and carbon neutrality goals. As the capital of China, Beijing should improve energy efficiency and has a primary responsibility in achieving the goals. Considering the heterogeneity of industrial sectors, this paper proposes a data envelopment analysis (DEA) approach to analyze the total factor energy efficiency and its influencing factors of 31 industrial sectors in Beijing from 2002 to 2018. We show that the low energy efficiency of Beijing's industrial sectors, mainly due to management inefficiency, is not conducive to the realization of the goals. Besides, the energy efficiency of heavy industry is higher than that of light industry. The expansion of enterprise scale has a positive impact on energy efficiency, while the property right structure, foreign trade and energy consumption structure have a negative impact on energy efficiency. Finally, we put forward some policy recommendations. [ABSTRACT FROM AUTHOR]

A Trombe wall with thermal catalytic blinds (TC-Blind/TW) for indoor heating/cooling, air purification and shading was proposed. In this paper, we has fully explored the advancement and feasibility of the system from thermal catalytic material preparation and performance testing, system construction and system steady state experiment testing, system heat-mass transfer model establishment and verification, and finally to the analysis of system operation strategy. The main conclusions are as follows: Firstly, thermal catalytic material MnO x -CeO 2 behaved excellent heating and purification performance, the formaldehyde degradation followed L-H model. Secondly, the thermal efficiency and formaldehyde single-through conversion of system during the daytime were between 35%-55% and 32%–42%, respectively. Thirdly, the established heat-mass transfer model had the high accuracy, with the maximum RSMD of 0.8%. Finally, the blinds turning angle has a great influence on the system performance, then the optimal annual operation angles in four cities were suggested. Take Beijing as an example, the annual generated clean air was 11355.6 m3/m2, the reduced heating and cooling loads were 341.2 and 318.3 MJ/m2, respectively. [ABSTRACT FROM AUTHOR]

This paper about the high-quality development level of Beijing-Tianjin-Hebei region is of vital importance to promote high-quality development of China. An evaluation index system that can comprehensively describe the high-quality development of the Beijing-Tianjin-Hebei region is designed in this paper. There are 27 indexes selected to evaluate the high-quality development of the cities in Beijing, Tianjin and Hebei province from three dimensions: target level, standard level and index level. By applying the SOM (Self-Organizing Map) algorithm to cluster analysis of high-quality development levels. The results of the study showed that there are variations in high-quality development of Beijing-Tianjin-Hebei city cluster. Beijing has the highest quality development level, followed by Tianjin and Hebei, and a significant gap exists between Beijing and Hebei in terms of the level of quality development. In addition, the high-quality development level of cities in Hebei Province are not balanced. Therefore, Beijing-Tianjin-Hebei city cluster should break down administrative barriers, making overall plans and promoting coordinated development among regions. [ABSTRACT FROM AUTHOR]

Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions. • The regional control strategy prevents the emergence of warning communities. • A bi-level model is developed to solve the traffic diversion of mixed traffic. • A variable neighborhood search algorithm is designed to solve the model. • The control strategies under different critical variables scenarios are analyzed. [ABSTRACT FROM AUTHOR]

With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

• The MSW generation is related to social index, economic index and population index. • The Grey Relational Analysis method can improve the prediction performance accuracy. • The GRA-LSTM model precisely and accurately predict the MSW generation in Beijing. • Advice for urban development are proposed from the MSW source and the MSW disposal. Achieving accurate prediction of the Municipal Solid Waste (MSW) generation is essential for the sustainable development of the city. This paper selects Beijing as the research object, building a neural network model based on Grey Relational Analysis and Long and Short-Term Memory (GRA-LSTM), and choosing 14 influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then this study obtains the landfill area in Beijing by using the aforementioned prediction results and the calculation formula of the landfill. Firstly, the GRA method is used to sort the influencing factors of the MSW generation for obtain the key influencing indexes. Secondly, the LSTM model is used to learn features of the key influencing indexes. Finally, the area of Beijing landfill is estimated by the calculation formula of landfill area. The results show that, first of all, the MAPE value of the GRA-LSTM combined model established in this paper is 7.3, and the prediction performance of this model is better than the other seven structural methods. Secondly, the area demand for landfills in Beijing shows an upward trend. At last, this paper put forward relevant suggestions to achieve sustainable urban development and deal with the increase in the MSW generation and the demand for landfills. [ABSTRACT FROM AUTHOR]

• We investigate driving forces of different land-use types to BSD. • Diversified machine learnings are proposed for spatial interpolation fusion. • Hotspot detection establishes sampling points for reducing the MAUP effect. • Fusion models improve the interpolation performance: accuracy and stability. • Word clouds identifies predominant facility names in each land-use type. Land use plays a crucial role in promoting the bike-sharing demand. Traditionally, studies on bike-sharing demand (BSD) are mainly focused on its prediction through regression methods, but the influence of MAUP (modifiable areal unit problem) in modeling is ignored. This paper aims to model spatial BSD distribution and prove the driving forces of different land use types to BSD through a machine-learning-based multiple interpolation fusion method. The hotspot detection model is employed to establish sample points covering different land use types in urban areas. In order to capture the differences in adaptations among different urban regions and for different data sizes, six machine learning methods are applied and evaluated to improve BSD estimation by fusing five spatial interpolation algorithms, including Inverse Distance Weight, Spline, Kriging, Natural Neighborhood and Trend. The methodological verification of Beijing City shows that the fusion models improve the estimation performance compared with individual interpolation algorithms, and that GRNN (generalized regression neural network) method is superior to all the others. According to fitting results of all POIs based on the GRNN fusion model, we identify which types of facilities correspond to customers that will have a stronger preference for bike-sharing and demonstrate which facility names are more prominent in each land use type. The conclusions presented here enrich our understanding relationships between land-use and BSD, which provide a valuable foundation for the bike-sharing development. Compared with implementing regression in an analysis zone or a square grid, troubles caused by the MAUP are effectively solved through this method. [ABSTRACT FROM AUTHOR]

• A series of semi-quantitative analytical procedures are proposed for the characterization of organic additives in ancient lime mortars and can be a preliminary screening method for further lab analysis. • The use of a microplate spectrophotometer reduced the detection limit by two orders of magnitude and decreased the sample consumption to 0.2 g for the detection of seven organic substances. • Twelve samples of ancient mortar from the Forbidden City in Beijing were analysed using the test procedure. To improve the performance of lime mortar, ancient craftsmen often added various additives to mortars, including inorganic and organic materials. The analysis of mortar additives plays an important role in the conservation and restoration of historical buildings. However, it is quite difficult to accurately identify the species of additives. This paper proposed a series of semi-quantitative analytical procedures to identify the organic additives in ancient lime mortars. By using reagents such as phenolphthalein, periodic acid, acetylacetone, anthrone, bicinchoninic acid (BCA), iodine-potassium iodide, chloramine T, etc., organic additives such as triglyceride, soluble sugar, starch, and protein (including blood, gelatine, and other unknown proteins) remaining in ancient lime mortar were accurately detected, respectively. The linear relationship of the standard curve was significant, and the detection limits for the target additives were below 7 µg/mL. The process is easy to operate within 6 h, and it is suitable for rapid in-situ testing. [ABSTRACT FROM AUTHOR]

• The shared and non-shared collection service systems are evaluated and compared. • The spatiotemporal characteristics of multiple stakeholders are considered. • A mixed-integer optimization model is established. • The shared service system is superior in economic and environmental implication. • The expansion of storage space is required to meet the future processing demand. One of the impending consequences of the rapid penetration of electric vehicles (EVs) is that a substantial amount of expired EV batteries will present an increasing waste collection and management problem, particularly in the urban context. Motivated by a lack of research on this issue, this paper comprehensively evaluates the relative benefits of shared versus non-shared collection systems, where the service outlets are not exclusive to specified automakers. Using a mixed-integer optimization model, the analysis features spatiotemporal and multiple stakeholder complexities. Based on the historical monthly EV sales data from 2016 to 2021, a representative case study of Beijing, China is conducted, including 16 district centers, 32 major automobile manufacturers, 153 collection service outlets and 4 disposal centers. The results show that a shared collection service system leads to higher profitability, higher collection rates, increased environmental benefits and improved facility utilization. Consequently, this research contributes to supply chain liberalization to foster the efficient waste management of EV batteries. With a further model extension, it can also provide decision support for the policy-making of more countries. [ABSTRACT FROM AUTHOR]

• An integrated optimization approach of the flow control and train scheduling considering skip-stop patterns is established. • Joint strategy that can reasonably allocate arriving passengers and control train occupancy is obtained. • The optimization strategy can increase the average boarding rate from 88.09% to 94.69%. • The average travel time of passengers can be reduced by 7.22% by the proposed approach. • The proposed strategies provide more explicit and accurate guidance for metro managers. In many special situations (epidemic, emergencies, large-scale events, etc.) of the metro, it is a considerably real issue on how to control passenger flow density and improve operational efficiency synchronously under the control requirements of specific the load factor. This paper aims to propose an integrated optimization approach for jointly researching passenger flow control and train scheduling with skip-stop patterns. Besides, the passenger dynamic demand and train occupancy rate are considered. A mixed integer programming model is formulated to minimize the unused capacity of trains and the total number of waiting passengers at platforms and outside stations. Through the comprehensive analysis of the relationship between passengers and trains, the numerical experiments are implemented in Beijing Metro Line 1 to illustrate the validity of the proposed method. Finally, discussion and comparison of the performance of integration optimization strategies with diverse scenarios are given. The results show that, under the optimized combination strategy, the average boarding rate of all stations can increase from 88.09% to 94.69% by the proposed approach, the number of waiting behaviors of passengers can be declined by 92.65%, and the average travel time of passengers can be reduced by 7.22%. [ABSTRACT FROM AUTHOR]

Flammable gas leaked from pipelines is easy to cause explosion accident. The consequences of accident are amplified in strongly constrained environments near the pipelines. In this paper, a gas leakage and explosion accident in Beijing is analyzed using field investigation and relevant experimental analysis techniques, and the complete investigation process of accident cause based on physical evidence is displayed. Some theoretical analysis models are introduced to explore the causes of this type of accident from a system perspective. The laws of explosion damage of building structures in strongly constrained environments are also summarized. The results indicate that the gas leaked from the end of the hose was excited by an electric spark in the electrical box causing this explosion and a gas explosion with a TNT equivalent of 2.73–4.28 kg is sufficient to damage a large number of walls of civilian buildings. The pressure difference between the two sides of structures and the duration are decisive factors in the degree of damage. Cause analysis shows that human error needs to be avoided through education and supervision. The study can provide theoretical references for the investigation of explosion accidents, as well as improvement of energy management system. [ABSTRACT FROM AUTHOR]

• Integrated train dwell time regulation and train speed profile generation for automatic train operations is considered. • A coupled model of passenger flow dynamics, train timetable dynamics and train speed profile generation is introduced. • A distributed optimal control method based on alternating direction method of multipliers is designed to satisfy the real-time performance. • The proposed method improves headway regularity and meanwhile reduces the total energy consumptions. The wide-spread application of automatic train operation (ATO) system on metro lines allows short service headways, high-density operations and high operation efficiency. This paper addresses real-time train control for ATO when faced with disturbances or disruptions in its operations. More specifically, the paper focuses on the design of integrated train dwell time regulation and speed profile generation in real-time and in response to dynamic changes in the operation environment. A nonlinear optimal control model is formulated in a rolling horizon scheme that incorporates three key operating elements: train timetable, passenger load and train speed profile. The objective is to simultaneously improve headway regularity and reduce the total energy consumptions. To satisfy the real-time control requirement for ATO system, a decomposition method based on the alternating direction method of multipliers (ADMM) is designed to divide the original optimization problem into many sub-problems, one for each train, which can then be computed in a distributed manner. Moreover, to address the non-convexity issue, a relax-round-polish process is developed to deal with the formulated nonlinear optimal control problem with convex objective over non-convex constraints in order to find the approximate solutions quickly for the embedded applications. The combined result is an ADMM-based heuristic algorithm. The effectiveness of the proposed model and solution algorithm is demonstrated using real-world data from the Changping Line of Beijing Metro. The results show that the proposed distributed and embedded optimization algorithm is able to significantly enhance the robustness and reliability of real-time train control in automated high-density metro lines. [ABSTRACT FROM AUTHOR]

Current studies on compliment responses are largely confined to the dyadic model of communication, without taking into consideration the influence of third parties present. The most commonly used methods of data collection in compliment response research are discourse completion tasks (DCT), role play, etc., the objectivity and accuracy of which remain unconfirmed. With the method of natural conversation recording and targeted elicitation, this article collects 233 cases of female college students' compliment responses in both dyadic and triadic contexts in Beijing and analyzes the data in the model of "acceptance-mitigation-evasion-rejection". The results show that the frequency of accepting compliments among Chinese college students has increased greatly compared to the findings of Chen (1993), indicating noticeable Western influence on Chinese compliment response strategies. However, there is a significant difference between the acceptance ratio in dyadic and triadic contexts. In dyadic communication, the compliment receivers generally prefer the acceptance strategy, while in triadic contexts, they choose evasion and rejection. This finding indicates that the westernization of compliment response strategies in Chinese culture is a gradual and heterogeneous process and the Self-denigration Maxim still plays an important role in the culture at a deeper level. This study also indicates that the use of DCT tends to raise the ratio of acceptance strategy in general in Chinese compliment response research. • First, the result of this paper shows that in triadic contexts Chinese college students accept much less compliments than they do in dyadic contexts, which challenges a dominating view that under the influence of westernization, Chinese have mostly shifted from rejection to acceptance in responding to compliments. • Second, this paper takes into account the presence of a third party, which calls for and inspires reconsideration of other speech acts such as apology, invitation and so on. • Third, the findings of our study suggest that DCT as a method of data collection systematically increases the percentage of Chinese interactants' acceptance of compliments in naturally-occurring conversations. [ABSTRACT FROM AUTHOR]

• A photovoltaic-thermal road assisted ground source heat pump system is proposed. • The system could be applied to the buildings far away from heat supply network. • The system performance for building heating is studied by a 10-year simulation. • Influence of design parameters on the long-term operation performance is studied. • The matching relationship between heat exchanger length and road area is optimized. The photovoltaic-thermal (PVT)-ground source heat pump (GSHP) system has been proved to have great potential in improving building energy efficiency, but ordinary PVT collector requires a large installation area and limits the popularization of PVT. In this paper, a novel solar energy collector, called photovoltaic-thermal road (PVTR), is proposed to replace the traditional PVT collector, and a novel energy system that is PVTR-GSHP system is proposed. In order to study the system performance, taking a typical expressway service accommodation building in Beijing as an example, a 10-year simulation is carried out to analyze the heating operation characteristics of the proposed system. Moreover, the effects of the PVTR area, the length and layout of ground heat exchanger (GHE) on the system performance are also discussed. Results show that, when the length of GHE is 5000 m, and the number of boreholes is 25 and the borehole depth is 100 m, the minimum area of PVTR satisfying the soil heat balance and meeting the system power consumption are 187.2 m2 and 216 m2, respectively. When the PVTR area is 216 m2, the minimum GHE length satisfying the soil heat balance is 4000 m. Finally, to realize system zero energy consumption and soil heat balance, the best combination of GHE length and PVTR area is 4000 m and 216 m2 under the cases of this paper. And in this case, the average COP HP and COP sys in ten years are 5.59 and 2.31 respectively. [ABSTRACT FROM AUTHOR]

The transition to alternative energy sources and the adoption of on-demand operating modes in urban bus systems are crucial steps towards reducing carbon footprints and improving public transit services. This paper presents a two-phase approach for the collaborative optimization of charging schedules and passenger services, aimed at enhancing the operation of on-demand electric bus systems. First, we propose a label-setting dynamic programming algorithm that enables the efficient generation of bus-trips for each bus line in response to passenger requests. Second, we introduce a time–space network optimization model that facilitates integrated multiple bus-trip planning for the transit network, involving multiple bus lines and charging spots. The model selects bus-trips from various time–space arcs, which represent passenger carrying, bus deployment, and bus charging activities. To validate the effectiveness of our approach, we conduct a case study using real-world data from bus lines in Beijing, China. Computational results demonstrate that our approach can handle on-demand electric bus operations within minutes of solution time, efficiently serving over 2,000 passengers. Practically, our approach achieves a notable reduction in average transit time and effectively reduces the waste of public transit resources. The proposed approach can serve as a beneficial tool for decision-makers and operators seeking to enhance the performance and environmental impact of their electric bus systems. • This paper aims at enhancing on-demand bus operational efficiency. • This paper proposes an optimization approach for electric bus operations. • This paper provides practical solutions for responsive transit systems. [ABSTRACT FROM AUTHOR]

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities. • A Potential to Kinetic Energy (PKE) framework is introduced to investigate the relationship between green space attractiveness and urban movements. • Potential energy contains five sub-concepts: size, culture, attraction, proximity and location. • Kinetic energy represents urban movement that characterizes the motion of objects resulting from potential energy conversion. • Empirical analysis of Beijing shows the significant role of relative green space attractiveness in shaping urban movement patterns. • The PKE framework supports urban planners to evaluate green space attractiveness, examine the impact of landscape change, and promote inclusive urban environments. [ABSTRACT FROM AUTHOR]

• Debates on religion in secular modernity pays inadequate attention to science and technology. • Religion and S&T are not antithetical, but co-produced. • Religion addresses the spiritual alienation caused by technological advancement. • Religion serves as an alternative source of creativity for technological innovation. • Religion assumes a "scientific" and "rationalised" façade to appeal to modern, educated people. This article attempts at a re-theorization towards the symbiosis and co-production of religion, modern science and technology, inspired by theoretical thinking within geographies of religion and science and technology studies (STS). Recent scholarship on the geographies of religion has made substantive advancements in discerning the convergence of religion and secular modernity. However, science and technology (S&T), as an essential condition and driving force of secular modernity, remain peripheral to this ongoing theoretical agenda, yet to be fully incorporated into the analytical framework about the co-constitution of religion and secular modernity, arguably because of the entrench divide between the rationalism of science and metaphysical thinking underlying religion. This paper addresses this issue by foregrounding the social and cultural constitution of science and in particular, its susceptibility to religious and spiritual sensibilities. Through an empirical case of Longquan Monastery, a Buddhist monastery in Beijing, China , this paper shows how the monastery brings S&T and Buddhism into close encounter in order to enrich both – in particular, we examine how Buddhism is appropriated to address the spiritual alienation caused by technological domination, how Buddhism acts as an alternative source of inspiration for technological creativity, and how Buddhist institutions reinvent themselves as rational and scientific to cater to a religious clientele socialized into modern scientific progress. Through the dialogue with STS, this research further contributes to addressing enduring concerns in the geographies of religion with religious spaces and practices as context-specific and spatially variable. [ABSTRACT FROM AUTHOR]

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

• This study identified four types of urban industrial lands. • A large number of urban industrial land needs to be decentralized. • Some urban industrial land is suitable to be converted into urban green space. Decentralization is essential for the sustainable socio-economic development of megacities. In the context of the decentralization of noncapital functions in Beijing, the renewal of inefficient urban industrial land (UIL) into urban green space (UGS) is an important way to improve the quality of the city. This paper analyzes the urban industrial land use efficiency (UILUE) and its urban green space suitability (UGSS) of 865 study units in Daxing District, Beijing, and combines bivariate spatial autocorrelation with cluster analysis. The results show that (1) the distribution of UIL in Daxing District is "dense in the north and sparse in the south", and UILUE shows a spatial pattern of "high in the west and low in the east", with a high-efficiency area of 985.85 ha, accounting for 12.06 % of the study area, and a low-efficiency area of 507.77 ha, accounting for 6.21 % of the study area. (2) Among the existing UIL, the high suitable area is 1106.46 ha, accounting for 13.57 % of the study area, and the low suitable area is 439 ha, accounting for 5.38 % of the study area. (3) Through bivariate spatial autocorrelation analysis, four types of UIL were identified, among which the low–high cluster area had the greatest potential for land conversion, with 175 study units and an area of 1335.22 ha, accounting for 16.28 % of the study area. Finally, this paper proposes recommendations for the renewal and utilization of UIL in Daxing District to provide guidance and suggestions for the decentralization of other megacities in the world. [ABSTRACT FROM AUTHOR]

[Display omitted] • 2010-2018: Ecological-economic coordination was highest in Hebei and lowest in Tianjin. • The ecological-economic coordination degree of various regions has obvious spatial spillover effects. • Improved functional and development tactics and policy reforms can strengthen ecological-economic development relations. The Beijing-Tianjin-Hebei region is an integral part of the ecology and economy of northern China. Unique geographical factors closely interlink the regions; however, limited attention has been paid to urban clusters' ecological and economic spatial linkages. Therefore, this paper introduces Ecosystem service value and Ecological-economic coordination degree, considering extensive spatial spillover effects within the region under study. This paper adopts spatial autocorrelation and the spatial Durbin model to detect the coordination relationship. Moreover, examine the influence of population growth, sales of consumer goods, health facilities, green areas, and Tertiary Industry on Ecological-economic coordination. The relationship is tested economic growth, using the internal and external Thus, it determines that the change of coordination degree shows a "V"-shaped development in time and space and gradually realizes the overall coordinated development. It affirmed the judgment that the Ecological-economic coordination degree of each region has an obvious spatial spillover effect. Policy reforms, improved functions, and development mechanisms can enable the balanced and healthy development of the region's ecological and economic systems. [ABSTRACT FROM AUTHOR]

Pumping wells (PWs) lie at the core of agricultural irrigation infrastructure, and their spatial configuration is of great significance for the scientific management of regional agriculture and rational use of water resources. To solve the problems of unreasonable and unscientific spatial allocation of regional agricultural irrigation PWs, this paper proposes a new strategy for updating agricultural irrigation PWs based on a spatial decision model, using Songzhuang Town, Tongzhou District, Beijing as a case study. The model utilizes constraints based on local factors such as actual land use, and proposes a novel feasible solution strategy that combines random local search and abrupt jumps to increase the probability that feasible solutions will be found, addressing the performance issues of simulated annealing algorithms, influence of initial values, and parameter sensitivity. Finally, we used the proposed method to perform updated decision analysis on the spatial configuration of PWs in the study area. The results showed that most of the crops in the study area lack irrigation, and the currently available PWs cannot meet the needs for irrigation. The new PWs added based on this decision method can be more evenly distributed within the predefined optimization unit. The proposed number of PWs to be added is 166, the density of PWs is 0.04/ha, and the crop area covered by the PWs is 3350.43 ha. In contrast to the previous PW layout, the optimized PW configuration can meet the irrigation needs of residents, indicating that the new method proposed in this paper has good potential for extension and application. [Display omitted] • A new decision-making method for renewal of agricultural irrigation PWs was proposed. • The maximum unit crop area can be covered when the buffer zone radius was set to 166 m. • The optimal spacing of agricultural irrigation wells was 332 m. • The scheme alleviated the problem of insufficient water for agricultural irrigation. • The method has popularization potential for regional agricultural water management. [ABSTRACT FROM AUTHOR]

• Accurate risk path forecasting in rail transit systems is a significant and challenging task. • A probabilistic deep learning framework that can process multisource data for risk path forecasting. • The model can effectively handle safety issues such as cascade failures. Failure-related urban rail events can disrupt transit system operations and traffic flow and lead to serious safety problems worldwide. A domino effect can occur if potential cascading events of major failures are not effectively mitigated and controlled. Therefore, accurate risk path forecasting in rail transit systems is a significant and challenging task. Because of the limitations of traditional models in terms of computational power and feature extraction capabilities, this paper proposes a probabilistic deep learning framework that can process multisource data for risk path forecasting in urban rail transit. This paper first proposes a method for constructing a large-scale risk path ground truth dataset when fault events occur. Then, the framework uses a graph-based feature mapping method to model social media, passenger flow, and station failure information. Finally, we proposed a spatiotemporal feature extractor and a dynamic difference weighting loss function to extract features and optimize parameters. We apply real-world data from 2018 to 2019 from the Beijing urban rail transit system for experimental analysis. The analyzed results demonstrate that the proposed model exceeds the baseline models by at least 2.9% in terms of F 1 value, fusing multi-source data exceeds using single-source data by at least 14% in terms of F 1 value and the proposed attention mechanism and dynamic loss function weights can effectively improve the forecasting performance of the model. Furthermore, the results of the different steps ahead demonstrate that the above results are robust. The application of the model can effectively handle safety issues such as cascade failures. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

Road disruptions frequently occur in practice, resulting in a productivity loss of bus services and widespread passenger delays. These negative impacts are significant, especially when multiple routes are influenced at the same time. In order to mitigate these impacts, this paper proposes a multi-route coordination approach that collaboratively adjusts multiple bus routes and optimizes bus timetables to provide effective alternative bus services for passengers. Three adjusting strategies are adopted for bus routes with varying passenger demand: detouring, short-running, and cancellation. To address the multi-route coordination problem, a column-generation-based two-stage framework is developed. Concretely, a column generation technique is utilized in the first stage to iteratively generate candidate adaptive bus routes and passenger paths. Following that, an integrated integer programming model is built in the second stage to simultaneously determine bus timetables and the combinations of those adaptive bus routes. After realizing the low computational efficiency for large-scale problems, this paper designs a customized decomposition algorithm based on set partitioning to solve the presented model and obtain near-optimal solutions efficiently. Finally, the proposed methodology is applied to an illustrative Sioux Falls network and a real-world bus network in Beijing to verify its validity and effectiveness. Three comparative analyses are conducted to discuss the advantages of the three adjusting strategies, to investigate the benefits of coordinating bus timetables, and to explore the applicability of different adjusting strategies, respectively. • A multi-route coordination problem for bus systems in response to road disruptions. • A column-generation-based two-stage framework to address the problem. • A customized decomposition algorithm based on set partitioning for model solving. • The framework and algorithm are applied to a real-world bus network in Beijing. [ABSTRACT FROM AUTHOR]

With the rapid increase in residents in megacities, the passenger demand of metro systems is rising sharply and steadily, bringing immense pressure to train operations. To improve the service quality, this paper discusses systematically investigating a joint optimization of the robust passenger flow control strategy and train timetable on a congested metro line. A deterministic model for train timetabling and passenger flow control at each station is first developed to make a trade-off between operation efficiency and service fairness. Then, the uncertain passenger demand is further considered at each station, and three integer linear programming models are formulated to derive the robust passenger flow control strategies. The first two models are related to the technique of Light Robustness , in which the uncertainty is handled by inserting expected protection levels at stations or on trains. In addition, with a stochastic scenario set that characterizes the uncertain passenger information, the last model aims to find a solution that is feasible for all involved scenarios, and thus, reduces the impact of the uncertainty in metro systems. To improve the computational efficiency of large-scale instances, a customized decomposition-based algorithm is developed. Finally, some real-world case studies based on the operation data of the Beijing metro Batong line are conducted to verify the performance and effectiveness of the proposed approaches. • A model for the passenger flow control and train timetabling problem is formulated. • Three robust optimization models are further formulated to deal with uncertainty. • An effective customized decomposition-based algorithm is developed. • Real-world case studies are conducted to verify the effectiveness of the approaches. [ABSTRACT FROM AUTHOR]

The turbulent standard deviations and the turbulent third-order and fourth-order moments are the key turbulence dispersion parameters in Lagrangian dispersion models. However, the characteristics of these parameters under heavy haze conditions in urban areas have not been fully investigated, and the commonly used similarity relations of these parameters in models were based on observations in highly flat and sparsely populated areas. In this paper, the vertical profiles of these parameters and their local similarity relations under heavy haze conditions in the wintertime of Beijing have been analyzed by using data collected at a 325-m meteorological tower. The heavy haze process has been divided into three stages: transport stage (TS), cumulative stage (CS), and dispersion stage (DS). Results show that the turbulent dispersion parameters behave differently during three stages. In the TS and DS, the maxima appear in the profiles of the turbulent standard deviations above the urban canopy; in the CS, the turbulent standard deviation are almost constant with height. The analysis of the third and fourth order moments shows that the wind velocities above the urban canopy in the TS deviate from the Gaussian distribution more significantly than those in the CS and DS. The local similarity relations of the turbulent dispersion parameters in the TS, especially for the longitudinal wind components, are normally different from those in the CS and DS. Thus, different from the common assumptions in Lagrangian models, the turbulence dispersion in horizontal directions is anisotropic and should be parameterized by multiple similarity relations under heavy haze conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

• This paper contributes to the understanding of the taste heterogeneity of travelers to pre-peak discount pricing strategy. • Naturalistic data from smart card data users in Beijing before and after a policy intervention is used in this regard. • Four groups of travelers with heterogeneous characteristics are classified by constructing a latent class model. • Different targeted policy recommendations for each group of travelers are proposed in order to improve the policy effect. Studies of travelers' response behavior to transportation demand management is receiving substantial attention among researchers and transport operators in recent years. While previous studies in this area have generally assumed that the sensitivity of travelers to different factors is homogeneous and relies on survey responses, which may be prone to self-reporting errors and/or subject to behavioral incongruence. Relying on naturalistic data, this paper aims to investigate the behavioral response to pre-peak discount pricing strategy in the context of the Beijing subway with a special focus on the heterogeneity among the travelers. Anonymous smart card data from 5946 travelers before and after the introduction of a peak avoidance policy in Beijing are used to construct a latent class choice model to capture the sensitivity to different factors and the associated taste heterogeneity of travelers. Given the passive nature of the data, the model can offer more realistic outputs. The results indicate that there is substantial heterogeneity in travelers' responses to the peak avoidance policy, and that they can be probabilistically allocated to four latent classes. For all classes of travelers, the decision to shift their departure to off-peak is affected by the monetary saving, the required change in departure time and the frequency of travel, but in different magnitudes. In particular, only two classes of travelers (who exhibit lower standard-deviation in pre-intervention departure time) show significant sensitivity to price changes indicating that the discount policies are more likely to be effective for these groups. The rest of travelers are largely price insensitive – warranting the need for non-monetary incentives as opposed to fare discounts. To the best of our knowledge, this study is the first to innovatively apply the LCC framework to analyze travelers' heterogeneous behavior using large-scale smart card data without socio-demographic information. The findings can provide guidance to the subway authority in devising differential peak avoidance policies targeted for different groups of users, which are likely to be more effective than the current 'one size fits all' approach. [ABSTRACT FROM AUTHOR]

Atmospheric oxidizing capacity (AOC) is an essential driving force of troposphere chemistry and self-cleaning, but the definition of AOC and its quantitative representation remain uncertain. Driven by national demand for air pollution control in recent years, Chinese scholars have carried out studies on theories of atmospheric chemistry and have made considerable progress in AOC research. This paper will give a brief review of these developments. First, AOC indexes were established that represent apparent atmospheric oxidizing ability (AOIe) and potential atmospheric oxidizing ability (AOIp) based on aspects of macrothermodynamics and microdynamics, respectively. A closed study refined the quantitative contributions of heterogeneous chemistry to AOC in Beijing, and these AOC methods were further applied in Beijing-Tianjin-Hebei and key areas across the country. In addition, the detection of ground or vertical profiles for atmospheric OH·, HO 2 ·, NO 3 · radicals and reservoir molecules can now be obtained with domestic instruments in diverse environments. Moreover, laboratory smoke chamber simulations revealed heterogeneous processes involving reactions of O 3 and NO 2 , which are typical oxidants in the surface/interface atmosphere, and the evolutionary and budgetary implications of atmospheric oxidants reacting under multispecies, multiphase and multi-interface conditions were obtained. Finally, based on the GRAPES-CUACE adjoint model improved by Chinese scholars, simulations of key substances affecting atmospheric oxidation and secondary organic and inorganic aerosol formation have been optimized. Normalized numerical simulations of AOIe and AOIp were performed, and regional coordination of AOC was adjusted. An optimized plan for controlling O 3 and PM 2.5 was analyzed by scenario simulation. [Display omitted] [ABSTRACT FROM AUTHOR]