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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]

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]

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]

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]

• 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]

• 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]

• 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]

• 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]

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]

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]

• 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]

• 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]

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]

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]

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]

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]

• 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]

• 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]

Accurately predicting passengers' origin and destination (OD) demand and analyzing their spatiotemporal characteristics are the key to efficient operation and management of urban rail transit. While obtaining these spatiotemporal characteristics and thus making a short-term OD prediction are a big challenge for a model due to its high dimensionality and uncertainty. This paper proposes a pattern match algorithm based on t -distribution stochastic neighbor embedding and K nearest neighbors (TSNE-KNN) to promote the prediction performance and introduces similarity indicators to explore these features of OD flow and their relationship with the forecasting performance. Analysis of automatic fare collection data of Beijing rail transit shows that the TSNE-KNN model is superior to other state-of-the-art approaches, even including deep neural network models, and the similarity, which is affected by the functional attributes of station, the surrounding land use attributes and the degree of development of the road network, can be a universal indicator to indirectly reflect the time–space properties of the OD flow. It is found that as the similarity of daily OD flows decreases, the performance of short-term OD prediction of decreases, and rail transit stations are gradually shifting from the periphery to the center of the city. [ABSTRACT FROM AUTHOR]

• Scalable thermoplastic polyurethane films containing nanoparticles are recently developed. • Innovative advanced films present superior optical performance. • New films contribute to reduction in daytime overheat. Radiative cooling, which does not entail any energy consumption, has received considerable attention as a promising, practical, and sustainable solution to heatwaves. This study investigated single layer nanoparticles–polymer films that reflected sunlight and emitted radiation through atmospheric windows. In this paper, the fundamental principles of Mie theory, the cooling power model, and the thermal balance model of metamaterial films were first introduced. Then, the influence of fabrication parameters (nanoparticle materials, mass fractions, and thicknesses) to the reflectivity in solar band was experimentally explained. An increase in the mass fraction enhanced the reflectivity corresponding to the stretching vibration regions of C H, N H, and O H, while an increase in the thickness improved the reflectivity at non-absorption bands. Laboratory measurements indicated that the combination of TiO 2 , a fluorescent brightener, and a far-infrared ceramic powder on 100 μm thermoplastic polyurethane film achieved excellent solar reflectivity of 92.2 % and infrared emissivity of 96.1 %. Experimental testing and Fluent simulation showed that the film achieved a substantial cooling capacity in both Beijing and Tianjin. Under a transparent atmospheric window, it exhibited a subambient temperature of 3.7 °C when the air temperature reached its peak. The maximum daytime and night-time subambient temperature were 13.4 °C and 14 °C respectively. [ABSTRACT FROM AUTHOR]

With more and more interchange stations in a large-scale metro network, passengers tend to transfer between different metro lines from origination to destination, sometimes even more than once. Passenger waiting time is one of the critical standards for measuring the quality of urban public transport services. To support high service quality, this paper proposes a mixed integer nonlinear programming (MINLP) model for the train timetable generation problem of a metro network that minimizes the transfer waiting times and access passenger waiting times. In the mathematical formulation of the model, the transfer walking times at the interchange stations between two connected lines are treated as uncertain parameters. The robust train timetable generation model is formulated to optimize timetables by adjusting arrival and departure times of each train in the metro network to reduce access and transfer passenger waiting times. A robust counterpart is further derived that transforms the formulated robust model into a deterministic one. Moreover, a generalized Benders decomposition technique based approach is developed to decompose the robust counterpart into a subproblem and a master problem. The subproblem is a convex quadratic programming problem that can be solved efficiently. Finally, two sets of numerical examples, consisting of a small case and a large-scale case based on a real-world portion of the Beijing metro network, are performed to demonstrate the validity and practicability of the proposed model and solution approach. • Train timetable coordination with uncertain transfer walking time is considered. • A mixed integer nonlinear programming model is formulated for train timetable problem. • A generalized Benders decomposition algorithm is designed. • The proposed method reduces the passenger waiting times for metro networks. [ABSTRACT FROM AUTHOR]

Corrosion caused component rupture and structural collapse accidents occur frequently. Therefore, it is important to assess the fracture risk of corroded in-service steel structures. Corrosion morphology is found to be a significant factor deteriorating the ductility of the steel. Therefore, fracture properties of a tensile corroded Q235structural steel, which has been in-service in the outdoor environment in Beijing for 25 years with protection from the original anti-corrosion coating, are investigated in the paper. To this end, a series of experiments on the steel, including monotonic tension and surface scanning tests on raw samples, tension and shear tests on non-corrosion samples fabricated with the core material of the rusted steel, as well as electronic microscope scanning (EMS) on fracture surfaces of samples, are conducted. Sensitivity analyses of morphology parameters on fracture properties of raw samples are performed. Further, Finite element (FE) models are developed to replicate the experimental results. Additionally, in order to predict the probability of crack failure and assess the residual performance of corroded steel components, both Void Growth Model (VGM) and Stress-Weighted Damage Model (SWDM) and a micromechanical fracture model (known as LOU) which are more suitable for complex stress states, are employed to evaluate the accuracy through comparisons with test and FE results. Finally, fracture modes of corroded steel samples are analyzed. Research results in this study indicate that, although the aggressiveness level of the Q235 steel with only initial coating and served outdoor for 25 years in Beijing is roughly mild, the ductility of the tensile sample could be reduced by up to about 36.811 %. Several fractures may initiate almost simultaneously at both the rough surface and core of seriously rusted samples. Fracture mechanism of the sample is mixed microvoid coalescence and quasi cleavage. It can also be concluded that, compared to VGM micromechanical fracture model, SWDM and LOU model are more appropriate for fracture prediction of corroded steel components. • Tensile and EMS tests are conducted on outdoor Q235 steel in-service for 25 years in Beijing. • Effect of morphology parameters on fracture properties of samples is quantitatively analyzed. • Micromechanical models more suitable for complex stress states are employed for fracture prediction of rust steel. • The fracture mechanism of corroded steel samples is clarified. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles has become pivotal in attaining emissions reduction objectives, and the rationale behind designing and situating charging stations determines the unfurling of electric vehicles. This scholarly work fully mines the geographical situational particulars underlying the functioning statistics of charging stations in Beijing during the period from January to March 2016. By incorporating the urban road network, we construct a charging stations accessibility index to investigate the influence of stations accessibility on utilization rates. The findings reveal a substantial positive relationship between the overall accessibility of charging stations and their utilization rates. However, the limited number of charging stations hinders the agglomeration effect from significantly boosting utilization rates. Moreover, the impact of charging station accessibility on utilization rates varies significantly across regions with different levels of development, indicating pronounced heterogeneity. The research conclusions of this paper provide useful suggestions for promoting the rational layout of charging stations. [Display omitted] • Charging station accessibility depends on driving distance to important city areas. • The accessibility of charging stations significantly impacts their utilization rate. • Charging station accessibility's impact on utilization rate varies significantly across regions. • Insufficient charging stations limit agglomeration's impact on utilization rates. [ABSTRACT FROM AUTHOR]

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network. [ABSTRACT FROM AUTHOR]

The solar furnace is key equipment for the thermochemical reaction research which uses focused solar energy to drive the reactor, and the solar flux distribution on the reactor surface plays a decisive role in the regulation of the thermochemical reaction process and formation of target products. In this paper, a reflective solar furnace with a thermal power of 30 kWth in Yanqing, Beijing, is taken as the research objective. Firstly, the Monte Carlo Ray Tracing (MCRT) method is used to establish the mathematical model for the optical calculation, and the distribution characteristics of solar flux are deeply analyzed. The accuracy of the model is verified through the comparison with the SolTrace software and experimental measurements respectively. Secondly, an improved four-quadrant and adjustable focusing solar furnace experimental equipment is developed, and a new solar flux distribution regulation strategy is proposed by using the inward crossover and outward expansion offset of the four-quadrant focusing points coupled with the heat-absorbing platform movement. The results show that the non-uniform factor and the peak value of the solar flux distribution in the set target area can be optimized by selecting an appropriate inward crossover offset and movement of the heat-absorbing platform, respectively. Moreover, when the DNI varies between 480 W/m2 and 800 W/m2, through the optimized control strategy, a non-uniformity factor with the value of less than 0.10 can be achieved in the target area of a square reactor surface with a side length of 10 cm, and the peak flux can also be controlled in the range from 3000 kW/m2 to 3300 kW/m2 with a fluctuation range of less than 10.0% as set by the thermochemical reaction. The optimized control strategy can effectively meet the uniformity and stability requirements of solar flux distribution in solar thermochemical reaction and finally achieve the goal of regulating the target products. • An improved four-quadrant adjustable focusing solar furnace is developed. • An innovative solar flux distribution regulation strategy is proposed. • A non-uniformity factor with the value of less than 0.10 can be achieved. • The peak flux can be controlled in the range from 3000 kW/m2 to 3300 kW/m2. [ABSTRACT FROM AUTHOR]

The prediction and evaluation of environmental vibrations generated by traffic loads have been playing more and more important role during the planning and designing of high-quality traffic system. From the traffic source to sensitive building, the subsoil is the one of key propagation media, so the determination of soil dynamic parameters is very crucial in studying the transmitting and attenuation of environmental vibration. Due to the spatial variability of natural soil layers, the soil dynamic parameters are actually not certain but probabilistic with stochastic characteristics. However, most current inversion methods of soil parameters cannot consider the uncertainty. In this paper, an efficient inversion method is proposed to obtain the random distribution of soil dynamic parameters based on the combination of field experiment, forward soil modelling, and Bayesian theory. Take the shear modulus and damping ratio of subsoil as the typical parameters, the experimental dispersion curve and spatial attenuation curve can be obtained by field experiment, while the corresponding theoretical ones can be calculated by the thin-layered soil model with perfectly matched layer. The combining of experimental and theoretical curves generates the likelihood function. A prior probability distribution model is firstly established, and subsequently the posterior probability distribution model is deduced by genetic algorithm, Monte Carlo-Markov chain algorithm, and Bayesian theory. In order to verify the proposed inversion method, an on-site experiment based on Multichannel Analysis of Surface Waves(MASW) was conducted in an open field in Beijing. The results show the mean values of inversed soil parameters were close to the determined parameters provided by geological exploration. Compared to the traditional determined inversion, the proposed stochastic inversion method can consider the variety of soil parameters within small depth by providing probability distribution, mean and root-mean-square error, which has greater applicability in the further probabilistic prediction of environmental vibration. • Bayesian theory and Monte Carlo Markov chain algorithm can be well applied to soil parameter inversion. • The proposed inversion method can efficiently reflect the random variation of soil parameters. • The Metropolis Hastings algorithm is introduced to improve the computational efficiency. • The method is based on non-intrusive data and has more significant engineering value than traditional geological survey. [ABSTRACT FROM AUTHOR]

Currently, ozone pollution is one of the key environmental concerns, and to provide more effective strategies for ozone control, this paper proposes the use of the Geostationary Environmental Monitoring Spectrometer (GEMS) in conjunction with MAX-DOAS for observations and ozone sensitivity analysis. Compared to traditional polar-orbiting satellites, GEMS, launched by South Korea in 2019, offers superior spatiotemporal resolution. However, there is a lack of ground validation for GEMS data related to ozone and its precursors. In this study, we conducted validation in the Jing-Jin-Ji (Beijing-Tianjin-Hebei) region using two typical MAX-DOAS sites and the China National Environmental Monitoring Center (CNEMC) sites, confirming the accuracy of GEMS data. Analysis of the seasonal horizontal distribution results from GEMS and vertical distribution results from MAX-DOAS in 2022 reveals that the concentrations of NO 2 , HCHO, O 3 , and ozone sensitivity not only display seasonality and regional variations but also exhibit distinct characteristics at different altitudes and times, influenced by local climatic conditions, human activities, and topography. When formulating ozone control policies, it becomes imperative to consider factors such as time, regional characteristics, and seasonality. Comparisons between daily results from TROPOMI and GEMS indicate that polar-orbiting satellites may overlook high-pollution periods within a single day, and their measurements may not provide a comprehensive representation of an entire day's data. Consequently, relying solely on polar-orbiting satellite data for shaping ozone prevention and control policies may inadvertently encourage ozone formation during specific time periods, rendering these policies ineffective. Therefore, leveraging GEMS is essential for the development of comprehensive, around-the-clock ozone control policies. Finally, we used monitoring data from Beijing on October 6, 2022, to illustrate how the combination of GEMS and MAX-DOAS enables three-dimensional monitoring of pollutant transport processes on an hourly scale. [Display omitted] • Validation of the accuracy of ozone and its precursor data from GEMS. • Analysis of hourly ozone sensitivity level distribution using GEMS. • Limitations of polar orbit satellites in O 3 sensitivity analysis compared to GEMS. • Observation of the transport of pollutants combining GEMS and MAX-DOAS. [ABSTRACT FROM AUTHOR]

Integrating emerging shared mobility with traditional fixed-line public transport is a promising solution to the mismatch between supply and demand in urban transportation systems. The advent of modular vehicles (MVs) provides opportunities for more flexible and seamless intermodal transit. The MVs, which have been implemented, are comprised of automated modular units (MUs), and can dynamically change the number of MUs comprising them at different times and stops. However, this innovative intermodal urban transit brings with it a new level of dynamism and uncertainty. In this paper, we study the problem of jointly optimizing the timetable and the vehicle schedule within an intermodal urban transit network utilizing MVs within the context of distributionally robust optimization (DRO), which allows MVs to dynamically (de)couple at each stop and permits flexible circulations of MUs across different transportation modes. We propose a DRO formulation to explore the trade-off between operators and passengers, with the objective of minimizing the worst-case expectation of the weighted sum of passengers' and operating costs. Furthermore, to address the computational intractability of the proposed DRO model, we design a discrepancy-based ambiguity set to reformulate it into a mixed-integer linear programming model. In order to obtain high-quality solutionss of realistic instances, we develop a customized decomposition-based algorithm. Extensive numerical experiments demonstrate the effectiveness of the proposed approach. The computational results of real-world case studies based on the operational data of Beijing Bus Line illustrate that the proposed integrated timetabling and vehicle scheduling method reduces the expected value of passengers' and operating costs by about 6% in comparison with the practical timetable and fixed-capacity vehicles typically used in the Beijing bus system. • We address the timetabling and vehicle scheduling problem concerning modular units. • We formulate a distributionally robust model for the investigated problem. • We develop an algorithm combining ALNS and GUROBI to generate high-quality solutions. • We verify the effectiveness of our proposed approaches by numerical experiments. [ABSTRACT FROM AUTHOR]

This paper examines the distributional effects of a multi-school districting reform, involving lottery property allocation, on families' school choice behavior in Beijing, China. Employing a competitive school district housing market model, we investigate the reform's implications on housing prices and school enrollment patterns. Using a unique dataset that combines second-hand housing transaction records and school enrollment data, we document this notable consequence: a substantial 4.57% decrease in housing prices in Haidian District post-reform, resulting in enhanced access to high-quality schools for middle-income families. However, the impact on school choice behavior exhibits structural disparities. While the reform creates opportunities for middle-income families, lower-income families may encounter challenges in accessing public primary schools, where housing prices with improved educational quality have risen by 1.21%. Conversely, affluent families opt for private schools to secure high-quality educational resources, evident in a 4.25% increase in corresponding rent. Additionally, Chinese families prefer proximity to schools to minimize commuting expenses, as reflected in a 0.56% rise in rents near public primary schools. These findings emphasize the importance of addressing both "excessive school choice competition" and the diverse educational preferences of families when implementing school assignment rule reforms in developing countries. Our research expands the literature on primary education and extends concepts for education reform in other developing countries. • Study constructs a competitive school district housing market model. • District-wide housing prices drop by 4.57%, helps middle-income families get good educations. • Housing prices increase with improved educational quality (1.21%) and especially rent price increase near private schools (6.42%). • Poor families challenged to get housing near good schools. • Wealthy families may opt to send their children to private primary schools in order to ensure access to high-quality educational resources. [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]

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]