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• A choice experiment was used to assess the preferences and willingness to pay for tangible and intangible industrial heritage. • The case study is the Baosteel steel factory, an industrial brownfield in Shanghai planned to be transformed into an arts and entertainment hub. • The results show that individuals tend to support selective rather than extensive preservation of physical attributes of the industrial sites. • Findings show there exist a demand for preserving and transmitting the intangible heritage of industrial culture along with adaptive reuse of the industrial sites. Industrial heritage has in recent years become an important component in land use and urban development policies. Yet, assessing the economic value associated with the preservation and reuse of industrial complexes remains an open question. In this paper, we apply a discrete choice experiment (DCE) method to assess the economic valuation of the Baosteel steel factory, an industrial brownfield in Shanghai planned to be transformed into an arts and entertainment hub. By eliciting the preferences of locals for the preservation and reuse of distinct attributes of the site, our findings suggest that the preservation of significant landmarks and the intangible component of the industrial site are the two most relevant attributes worth protection. Moreover, we add evidence on the relationship between respondents' economic preferences and their attitude toward the cultural value conveyed by industrial heritage by showing how distinct groups of respondents express different preferences for single attributes of the industrial heritage. The paper contributes to the heritage valuation literature by illustrating the implications and challenges of assessing the demand for industrial heritage rehabilitation projects. [ABSTRACT FROM AUTHOR]

This study focuses on investigating the shear behavior of the Shanghai clay-concrete interface under cyclic loading using a large-scale multifunctional shear apparatus. Considering that soil-structure interfaces are typically found in shallow soil layers or even above the ground surface, the interface zone is unsaturated in its natural state, the shear experiments were therefore conducted on the tested soil with different water contents. The corresponding matric suction of the tested soil under different water content and initial dry densities was determined by the filter paper method. The experimental findings indicate a substantial influence of matric suction on the interfacial strength, revealing a pronounced non-linear relationship. The interface exhibits the highest shear strength during the initial shear, which gradually decreases with an increasing number of shear cycles. This reduction can be attributed to the weakening of the bonding effect at the interface caused by cyclic shear. On the other hand, the matric suction decreases with water content increases, leading to a decrease in interface shear strength. Additionally, the vertical cumulative displacement of the soil increases with higher water content. • Cyclic shear experiments were conducted on the interface between unsaturated Shanghai clay and concrete. • The interface strength exhibits a non-linear relationship with the matric suction. • The contribution of matric suction to the interface strength is significantly influenced by the number of shear cycles. [ABSTRACT FROM AUTHOR]

The majority of modal disparity studies focus on accessibility to jobs and to non-work activities with little attention paid to actual commuting behavior. How modal differences in commuting behavior change when set against the minimum and maximum commutes possible within a fixed urban structure is much less known. In commuting efficiency studies, the mode of commuting flows has not been intersected with socio-economic characteristics. Using the 2015 1 % National Population Sample Survey (NPSS) in Shanghai, we apply the excess commuting framework to fill this research gap by measuring the modal disparity between public transport and cars, in commuting efficiency by education level of workers. Results show that public transport users are less efficient commuters than car users, indicating great capacity exists for optimizing public transportation. However, the modal disparity decreases with increasing educational attainment, because car commuting efficiency is decreasing at a faster rate than for public transport. Based on these findings, urban transportation policy should focus on improving jobs-housing balance of public transport, and building a friendly green travel environment to rectify car-oriented bias in urban spatial structure and narrow the difference between public transport users and car users, especially for the poorly-educated. • This paper is the result of an international collaboration between three authors who have worked extensively on topics related to commuting. • The paper examines aspects of the demographics and transportation interactions in Shanghai. • A very notable finding is the overwhelming mode share in public transport for the least educated workers. • The paper shows the importance of heterogeneity of socioeconomic characteristics in estimating excess commuting. [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 definition of water sustainability is further expanded. • A water sustainability assessment model regarding quantity-quality-benefit is innovatively built. • The impact of coupled coordination relationships between the three subsystems is considered. • Water sustainability is evaluated from the single and three-dimensional perspective simultaneously. • Three-dimensional water sustainability assessment is more objective and scientific. An integrated water sustainability assessment is conducive to realizing the optimal allocation and efficient water utilization, and alleviating the water crisis. Existing research mainly appraises water quantity and water quality as unidimensional or in combination, without considering the water benefit, and few studies have assessed the regional water sustainability based on an integrated evaluation multidimensionally. Therefore, this paper innovatively introduces water benefit to construct a three-dimensional quantity-quality-benefit model (QQBM) to evaluate water sustainability in line with the water footprint (WF) theory and water resources balance sheet (WRBS). The impact of the coupled coordination relationship among water quantity, water quality, and water benefit on the water sustainability evaluation is further analyzed by introducing the coupled coordination degree model (CCDM). This paper takes the Yangtze River Economic Belt (YREB) as an example and makes a spatio-temporal analysis of the water sustainability in its 11 provincial capitals during 2010-2021 from a single dimension and a three-dimensional perspective, respectively. The results found that: (1) From a single dimension, the water sustainability in the 11 provincial capitals in the YREB during 2010-2021 is at a medium level, with better water benefit sustainability, moderate water quantity sustainability, and horrendous water quality sustainability. Among them, the water sustainability is the best in Guiyang and Hangzhou, while the worst in Shanghai. (2) From a three-dimensional perspective, the water sustainability in the 11 provincial capitals from 2010 to 2021 is horrific. Among them, the water sustainability is the best in Kunming, Changsha, Nanjing and Shanghai, while the worst in Wuhan and Hefei. (3) The degree of coupling coordination between water quantity, water quality and water benefit significantly influence the water sustainability in the 11 provincial capitals of the YREB. The three-dimensional assessment model QQBM constructed in this study is more comprehensive and objective than the single perspective assessment. The findings in this paper can help other regions to conduct similar water sustainability assessments and inform policy recommendations. [ABSTRACT FROM AUTHOR]

• This paper studies a vehicle deployment and dispatch problem. • A two-stage model is proposed to consider the uncertainties. • A variable neighborhood search method is designed to solve the model. • Managerial implications are derived on the basis of sensitivity analyses. To manage traffic emergencies, cities require multiple types of traffic rescue vehicles, which need to be dispatched from various rescue stations dispersed throughout the city. A reasonable way of deploying the rescue vehicles must be determined given that the times and locations at which the traffic emergencies occur are uncertain. In this paper, we propose a two-stage stochastic programming approach to deploying multiple types of emergency vehicles in response to traffic accidents in the context of uncertainty. The first stage of the proposed model concerns decisions on the quantities of the different types of vehicles to be stocked at each rescue station. In the second stage, when the locations and accident rescue demands are realized in each scenario, the decision-making involves dispatch of the emergency vehicles to the traffic accidents. To solve the proposed model, we suggest a variable neighborhood search method. Using the road network of Jiading District, Shanghai, as an example, we perform numerical experiments to investigate the efficiency of the proposed method and the model validity. Some managerial implications are also outlined in the sensitivity analysis. [ABSTRACT FROM AUTHOR]

An anomalous trajectory detection method based on ship trajectory clustering and prediction is proposed. The method consists of two modules, namely, trajectory clustering based on improved DBSCAN and Trajectory prediction by ProbSparse Attention-based Transformer. we propose the concept of ship behavior similarity for the first time in this paper, by decomposing the velocity according to the ship's heading to get 'velocity coordinates' to replace latitude and longitude coordinates, on the basis of which the ship behavior trajectories are clustered and the core trajectories are extracted. By comparing the behavioral similarity between the predicted trajectory and the core trajectory with the preset threshold, it is judged whether the abnormal trajectory or the ship has abnormal operation. AF-DP is used in this method for compression of trajectory data, and Fast-DTW is used to calculate the behavioral similarity between trajectories. Notably, this is the first application of ProbSparse Attention-based Transformer in the maritime domain. The proposed method is applicable to inland river traffic near Wusongkou, Shanghai. The final experimental results show that the method we proposed can effectively identify the abnormal behaviors in the trajectory, and can provide technical support for the analysis of ship track data and the risk management of maritime transport in this region. • In this paper, we reduce the algorithmic complexity of Transformer using ProbSparse Attention. • A new trajectory representation is proposed for the detection of anomalous trajectories. • Ship behavioral trajectories were clustered using improved DBSCAN. [ABSTRACT FROM AUTHOR]

In the context of prominent energy crisis, photovoltaic power (PV) generation has received increasing attention, then accurate PV generation forecasting is crucial for ensuring the smooth operation of power stations. However, existing research is insufficient in comprehensively analyzing the impact of PV generation volatility. To fill the gaps and enhance the prediction accuracy, this paper proposes a new hybrid forecasting method. We first introduce the Locally Weighted Scatterplot Smoothing (LOWESS) method to process the data and enhance the data stability, and use Pearson correlation coefficient (PCC) and Random Forests (RF) for feature selection to improve the quality of input data. Then we use Attention mechanism and Convolutional Neural Network (CNN) layer to optimize Bi-directional Gate Recurrent Unit (BiGRU) model and form a new hybrid model. Finally, based on the Bagging algorithm, we use ensemble learning to further optimize the hybrid BiGRU model to enhance the depth and performance. The proposed method is validated through case analysis results from two different locations, Xuhui District in Shanghai, China and the DKASC area in Alice Springs, Australia. The results demonstrate that, compared with other models, the developed method exhibits exceptional prediction performance and effectively enhances the accuracy of PV generation forecasting. Keywords : photovoltaic power generation; Locally Weighted Scatterplot Smoothing; feature selection; ensemble learning; Bi-directional Gate Recurrent Unit. • LOWESS smoothing technology is used to reduce the variability of PV generation data. • Pearson correlation coefficient and Random Forest are used to for extract feature. • Attention mechanism and CNN layer are used to optimize BiGRU model. • Two BiGRU layers are stacked to improve the depth and prediction performance. • Ensemble learning is introduced to further optimize the hybrid BiGRU model. [ABSTRACT FROM AUTHOR]

Previous experimental tests on clays have confirmed the non-coincidence of principal strain increments with principal stress axes even though the stress rate is colinear with the current stress. To simulate the non-coaxial behavior of saturated clay subjected to monotonic proportional or non-proportional loading with constant principal stress rate directions, an anisotropic hypoplastic model is presented in this paper. The model is proposed by incorporating an improved anisotropic asymptotic state boundary surface (ASBS) and a non-coaxial asymptotic strain rate direction into the original explicit-ASBS hypoplastic model. The non-coaxial flow results from a non-coaxial stress rate defined by a Gram-Schmidt orthogonalization process based on a reference stress tensor. The capability of the proposed model is demonstrated by simulating the test data performed in hollow cylindrical apparatus (HCA) on Wenzhou clay and Shanghai clay with different drainage conditions and initial consolidation states. In addition, a series of numerical stress probing tests have been conducted to gain further insights into the properties of the proposed model. [ABSTRACT FROM AUTHOR]

The advancement of low-cost, non-manually labeled big data technologies for fault detection in wind turbines is crucial to guarantee their safe and efficient operation. In this context, Normal Behavior Modelling (NBM) is a prevalent approach. However, existing NBM methods for wind turbine fault detection inadequately utilize complex spatial–temporal correlations within the Supervisory Control and Data Acquisition (SCADA) during the normal behavior prediction stage. To cope with this limitation, this paper propose a hierarchical spatial-temporal autocorrelation graph neural network method for online wind turbine pitch system fault detection. This approach integrates a dynamic graph network with an adaptive structure for spatial correlation learning and a transformer model with autocorrelation attention to identify periodic time-distance correlations. Furthermore, a hierarchical neural network, specifically designed for wind turbine physical structure, enhances local information extraction through seven latent graph neural networks and a probsparse attention mechanism. The proposed method demonstrates significant effectiveness in real-world applications, as evidenced by data from a Shanghai wind farm. [ABSTRACT FROM AUTHOR]

Colour is an important factor in the expression of recognizability and cultural identity of regional cultural and creative design. At present, the colour recognition of regional characteristic and the colour association of regional culture mainly rely on the designer's subjective perception. To obtain the target colour resources with reference for regional cultural and design need, this study proposes a scientific method of colour extraction and strong colour association matching of Shanghai style Chinese paintings by machine learning, and applies the related results to the colour design of cultural and creative products. Firstly, using the SLIC superpixel algorithm and Mean shift algorithm to realize the overall dimensionality reduction of the image features and the colour aggregation gradually, so as to extract the characteristic colours of Shanghai style Chinese paintings; Secondly, we introduce a data mining algorithm (Apriori) to mine out the association rules from multiple characteristics colours and filter out strongly associated colour combinations; Finally, we apply the colour combinations and colour tones to the colour design of the creative products. In order to verify the scientificity of the colour extraction and colour matching method proposed in this paper, we selected another painter's paintings in the same school as the algorithm experimental validation sample, similar results were obtained. In addition, we measured user satisfaction using the degree of awakening to Shanghai style culture and the propensity to make consumer decisions as the evaluation dimensions, which proves that the method of this study is effective. [ABSTRACT FROM AUTHOR]

Accurate numerical analysis in geotechnical engineering heavily relies on the constitutive model and its parameters. The advanced constitutive model can describe the complex mechanical behaviors of soil that may involve a number of parameters. However, determining the values of constitutive parameters always relies on manual trial-and-error, which can be a time-consuming process and not conducive to widespread application. This paper presents an identification method that combines machine learning with search algorithm based on the laboratory and in-situ testing. Initially, the sensitivity of constitutive parameters was analyzed by investigating the effects of variations in soil overconsolidation and structural parameters on the results of triaxial and pressuremeter tests. Subsequently, the initial state parameter values and material control parameter ranges of the soil can be identified from the triaxial tests, this is achieved by using the neural network model. In order to accurately determine the parameters value, the numerical model was established based on in-situ pressuremeter test, and traversal algorithm was implemented to search for the optimal fit values within the range of material control parameters. Finally, the proposed identification method was applied to layers 3–5 of Shanghai clay, and the inverted parameters exhibited a good fit with the outcomes of triaxial tests and pressuremeter tests. The combination of laboratory and in-situ testing can enhance the reliability of obtaining constitutive parameters, and this method provides an insight into the parameters identification for advanced constitutive models. [ABSTRACT FROM AUTHOR]

Advanced synchrotron radiation sources are major facilities for analyzing the formation and evolution of material structure in multidisciplinary research, owing to their high luminosity, low emittance, wide energy bandwidth and other characteristics. In China, several advanced synchrotron radiation sources are under construction, including the HEPS (High Energy Photon Source) and SHINE (Shanghai high repetition rate X-ray Free Electron Laser and extreme light facility). These facilities are poised to become major multidisciplinary research platforms in China. To meet the requirements of pixel detectors in these facilities, dedicated pixel readout chips are being developed, including HEPS-BPIX and HYLITE. This paper presents a high-speed serializer circuit developed in a commercial 130-nm CMOS technology for the full-scale (128 × 128) pixel readout chip of HYLITE, which requires a data rate of 4 Gbps currently. The serializer consists of a 16-to-1 binary-tree multiplexer, a ring-oscillating-based phase-locked loop (RO-PLL) and a current-mode logic driving stage with a multi-stage pre-amplifier. Test results demonstrate its expected functionality within a wide operating range from 0.52 Gbps to 5.5 Gbps. At 5.12 Gbps, the measured jitter values are about 2 ps for random jitter, 20 ps for deterministic jitter, and 47 ps for total jitter with a 10−12 bit error rate. Additionally, the horizontal and vertical eye openings are 0.84 UI and 72%, respectively. These positive results indicate that the serializer meets the requirements of HYLITE. [ABSTRACT FROM AUTHOR]

Inadequate parking planning for heavy commercial vehicles (HCV) exacerbates urban road congestion. As an effective means of parking management, geofencing that identifies the virtual boundary for geographic areas is essential to ensure these vehicles do not impede traffic and urban spaces. However, geofenced areas must be rationally designed to prevent mismatches between parking areas and real parking needs. This paper presents a data-driven approach that integrates the Spatial-temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) methods and a Gaussian mixture model for identifying and predicting potential parking areas for HCVs. Leveraging the HCV trajectory data and land use data in Shanghai, China, we characterize the spatial distribution of parking demand and create a probabilistic model to predict active HCV traffic patterns and the spatial confidence regions under varying land use conditions. The results show that clusters of HCV parking demand tend to congregate near ports, comprehensive transportation hubs, logistics centers, and commercial hubs. These clusters correspond to five distinct parking demand patterns (i.e., day-long HCV stops, morning peak time HCV stops, daytime HCV stops, afternoon peak time HCV stops, and nighttime HCV stops), each reflecting specific spatiotemporal characteristics. The geofenced spatial domain was found to be very sensitive to the timing of parking, emphasizing the importance of using advanced geofencing technologies. The methodological framework introduced in this study holds significant value for policymakers and HCV operators as it aids in determining parking at strategic levels, offering valuable insights and tools to enhance the effectiveness of parking management. [ABSTRACT FROM AUTHOR]

The Corona Virus Disease 2019 (COVID-19) pandemic had a pernicious influence on the whole world, so that the international community implemented various travel policies to inhibit the viral spread. With the COVID-19 pandemic under control, the overly lenient measures allowed the new variant to take advantage. Also, it is difficult to determine the geographical scope of transport policy that has the least impact on work resumption. Therefore, it is necessary to find policies to promote work resumption while preventing and controlling the pandemic in a geographical perspective. In this paper, a framework based on mobile phone data is proposed for promoting work resumption during the pandemic. First, human mobility networks are constructed through the origin-destination flow extracted from mobile phone data. Second, the singular value decomposition (SVD) algorithm is utilized to recognize the mobility patterns and explore the urban mobility dynamics of work-resumption-related travel during the pandemic. Third, the nonoverlapping community detection algorithm is used to generate policy zoning for work-resumption-related travel. Finally, the policy zoning is validated by public health data based on the overlay analysis and spatial statistics method, and policy implications are proposed. A case study of Shanghai, China is applied to verify the proposed framework. The results show that the framework can capture the mobility patterns and implement the policy zoning based on mobile phone data to balance the economic activity and public health during the pandemic, and the proposed framework can be applied to other cities with future public health events. • We propose a framework for promoting work resumption during the pandemic based on mobile phone data. • Mobility patterns are classified into regular, visitor, holiday, and commuting travel by matrix decomposition algorithm. • Geographical scope of transport policy in response to COVID-19 is determined by community detection method. • Specific policies based on policy zoning are provided to balance the economic activities and public health. [ABSTRACT FROM AUTHOR]

This paper addresses model-based approach considering online model parameters estimation to estimate the real-time freeway traffic state for inhomogeneous traffic flow. Its effectiveness is demonstrated through macroscopic simulation and the influence of detector configuration on the estimation performance is investigated. The results indicate that when a freeway is inhomogeneity, additional detectors need to be placed at the boundary of traffic flow inhomogeneity to achieve better estimation performance. Finally, it is confirmed that considering traffic flow inhomogeneity can achieve better estimation performance using real expressway data from Shanghai. [ABSTRACT FROM AUTHOR]

• A hybrid graphical network approach considering causality named AC-GGN is proposed. • It can accurately make spatio-temporal prediction of deep excavation-induced ground settlement. • It is verified in a practical excavation case to reach high accuracy for each monitoring point. • Data augmentation techniques help to strengthen the long-term prediction ability of AC-GGN. • Global sensitivity analysis is conducted to quantitatively explain prediction behavior of AC-GGN. With the increasing demand for deep and large-scale excavation pits, the deformation response during excavation has become exceedingly complex, especially located in building-intensive areas. This paper proposes a hybrid deep learning model named attention-causality-based graphical gated network (AC-GGN) to accurately make the spatio-temporal prediction about the excavation-induced ground settlement at different monitoring points during the foundation pit construction. The novelty of the AC-GGN model lies in its flexible integration of four key components, including the Granger causality (GC) test, graph convolutional network (GCN), the gated recurrent unit (GRU), and attention mechanisms, which work together to effectively capture casual relationships along with spatial and temporal dependence embedded in the observed time-series from each monitoring points and then boost the prediction performance. To validate its applicability and superiority, a case study about a metro station excavation project in the Shanghai Metro Line 14 is conducted. Results indicate that the AC-GGN model outperforms state-of-the-art algorithms, which can make precise predictions for each monitoring point. The proper data augmentation technique facilitates long-term prediction with high accuracy, thereby expanding the scope of AC-GGN application beyond short-term prediction. Moreover, the global sensitivity analysis can be used to reveal which monitoring points have the most significant impact on ground settlement prediction. It can aid in identifying key risk areas for monitoring and control. In summary, the novel architecture of AC-GGN is beneficial to dynamically capture and predict the trend of ground settlement across different areas of the construction site. Practically, the accurate prediction results generated by AC-GGN offer rich evidence to not only perceive the excavation-induced risk development but also formulate corresponding measures in advance for risk mitigation. [ABSTRACT FROM AUTHOR]

The current paper presents a data-driven hierarchical Bayesian model (HBM) for predicting maximum lateral wall deflections in deep excavations in clay. The presented approach can address a number of challenges, including handling missing input parameters, incorporating the observational method, and accounting for site uniqueness. A new database, EXCA-CLAY/11/901, comprising 302 excavation sites worldwide, is compiled to train the HBM. The trained HBM is applied to a real case study in downtown Shanghai. The HBM can be applied to predict the maximum lateral wall deflection at each excavation stage independent of measured deflections from past stages (non-observational approach) or the more typical observational approach that includes past deflection measurements. Extensive cross-validation analysis validates the performance of the HBM and compares it with three existing regression models. The results show that the regression models exhibit slightly better performance within their applicable range when compared to the non-observational HBM prediction, but are outperformed by the observational HBM. The HBM is shown to offer several advantages over the conventional regression models, including the ability to produce 95% confidence intervals, handle missing input parameters, accommodate a diverse database, and continuously refine predictions as new information becomes available. [ABSTRACT FROM AUTHOR]

• We proposed a new model shield test device capable of observing the real-time movement of muck in fine-grained soils. • Three distinct muck movement modes are identified: flow around the central axis, overall static, and local adhesion. • A real-time method is proposed for early detection of muck blockage inside actual shield chambers in this paper. This study aims to evaluate the effectiveness of the newly developed shield test device (VMS) by Tongji University in Shanghai for determining the muck state inside EPB shield chambers in fine-grained soils. Muck blockage inside shield chambers frequently results in significant stratum deformation and excessive damage to these machines. The engineering community requires real-time and effective monitoring technology to assess the muck flow state inside EPB shield chambers. Three motion modes of the muck composed of fine-grained soil near the partition have been identified: flow around the central axis, overall static and local adhesion. These modes correspond to the three characteristics of shear plate torque near the shield partition: the periodic fluctuation, the three-stage change of 'rise-fall-stability', and a noticeable amplitude difference at the top and lower sides of the chamber. Finally, a preliminarily validated wireless signalling-type shear plate device that can be assembled on the field shield and an actual measurement method was further developed to determine the muck state inside the shield chamber in real-time. [ABSTRACT FROM AUTHOR]

Linking the urban spatial structure with residential mobility is a vital yet challenging mission. With a unique dataset tracking changes in individual residential locations in Shanghai from 2014 to 2015, this paper portrays the directional trajectories of Shanghai's spatial structure and rigorously examines the motivations of residents for different spatial preferences with the approach of propensity score matching. By classifying the spatial structure into four typologies, polycentricity, sprawl, dispersed-monocentricity and compact-monocentricity, we find that relocated residents push Shanghai's spatial structure in greater favor of sprawl or compact-monocentricity. Furthermore, the movers towards sprawl, decentralized-monocentricity and compact-monocentricity mainly benefit from housing, jobs, and neighborhood amenities, respectively; and the movers towards the polycentric spatial structure can take the advantage of favorable house prices. These results shed light on the process of shaping the urban spatial structure and offer valuable insights into residents' spatial preferences. • It contributes by linking the urban spatial structure with residential mobility. • Relocated residents push Shanghai's spatial structure in greater favor of sprawl or compact-monocentricity. • The movers that contribute to the shaping of different urban spatial structures can take advantage of various advantages. [ABSTRACT FROM AUTHOR]

• Evaluating and enhancing the resilience of urban logistics systems under emergency lockdowns. • Quantifying the performance of urban logistics systems by integrating distributional equity and delivery efficiency. • Proposing a quick-response capacity reallocation strategy for resilience enhancement. • Case study on the 2022 COVID-19 outbreak in Shanghai with managerial insights. The urban logistics system serves an essential role in providing daily necessities for residents but is vulnerable to resource shortages in disasters. For instance, cluster infections and access restrictions caused by public health emergencies like COVID-19 pose a severe challenge to the resilient operations of urban logistics systems. Especially, the restriction level and the labor shortage always vary a lot on a citywide basis, resulting in a dilemma in handling distributional equity and delivery efficiency. Motivated by this issue, this paper studies the resilience assessment method and enhancement strategy for urban logistics systems under emergency lockdowns with integrated consideration of equity and efficiency. The resilience-oriented capacity reallocation models are developed for quick-response strategies in the case of two disruption types, i.e., closure of distribution centers and capacity degradation of delivery routes. Numerical studies of a real-world case in Shanghai are conducted to illustrate the feasibility and effectiveness of our methods under different disruption levels. The comparison of reallocation schemes with respect to different weights of equity and efficiency is also performed to discuss their impacts on resilience. The results indicate the importance of considering equity in logistics distribution under large-scale disruptions. If distributional equity is highly valued, the service capability can retain up to 74 % of the original level despite half of the distribution centers being shut down. In addition, different concerns on equity and efficiency can lead to variations in system resilience for the same disruption scenario, but the capacity reallocation schemes consistently align with the aim of resilience enhancement. Finally, several managerial insights are provided for decision-makers on urban logistics planning and intelligent disaster management during emergencies. [ABSTRACT FROM AUTHOR]

• Superfluid cryoplant with 1 kW@2K for SHINE test facility. • Three-stage cold compressors combines with five parallel process vacuum pumps. • Helium pressure stability than ±0.15 mbar at 31 mbar for test dewar. The Shanghai high repetition rate X-ray free electron laser (XFEL) and extreme light facility (SHINE), a quasi-continuous wave hard XFEL facility, is currently under construction. In order to test all the superconducting components, assembled cryomodules and undulators, the SHINE test facility that consists of various test benches has been built. A superfluid cryoplant with 1 kW@2K was installed, commissioned, acceptance tested and turned into operation to support the cryogenic tests. The plant consists not only of a standard compressor system and cold box but also of a process vacuum system for 2 K operation. In the 2 K cryogenic system, three-stage cold compressors are firstly used to compress helium vapor at a mass flow of up to 50 g/s from 28 mbar to about 400 mbar. Afterwards, the sub-atmospheric helium is continually pressurize to the suction pressure of the warm compressors by five parallel process vacuum pumps. During the first eight months of 2 K operations, the tuning of the cold compressor regulation has been implemented to adapt the different static and dynamic heat loads. A helium pressure stability of superconducting cavities better than ±0.15 mbar at 31 mbar was achieved inside the vertical test bench with additional heating power of 160 W. In this paper, the pump down procedure and the operation results of cryogenic system at 2 K will be described. [ABSTRACT FROM AUTHOR]

Uncertain disruptions in urban rail transit (URT) networks can inflict significant damage to their structure and functionality. Enhancing network resilience has emerged as a critical solution to mitigate these challenges. This paper introduces a two-stage approach to bolster the resilience of large and intricate URT networks by focusing on pre-failure network topology enhancement and post-failure recovery sequence optimization. The assessment of network resilience encompasses the entire resilience process from the onset of a failure to the end of the failure to the completion of the recovery, and the resilience loss triangle model was employed as a valuable tool for assessing network resilience. Meanwhile, an improved resilience evaluation metric was proposed, which uses the global efficiency ratio as the network performance indicator. Taking the Shanghai metro (SHM) system as a case study, a distance-weighted network model was constructed to validate the effectiveness of the proposed resilience enhancement method. Based on the network failure simulation, vulnerable stations in the SHM network were identified and the influence of different metro lines was evaluated. Moreover, considering the existing network topology and practical planning, the topology optimization scheme for adding an outer loop line in the SHM network was developed. The results demonstrate that the topology-optimized network can effectively enhance the network resilience and protect the vulnerable nodes. Additionally, corresponding optimal recovery sequences were proposed for the failure of a single node, multiple nodes, and multiple edges. An interesting finding is that the optimal recovery order is not dependent on the node degree but rather associated with the node vulnerability. Furthermore, it is demonstrated that optimizing the post-failure recovery sequence helps reduce network resilience loss, whereas improving the network topology prior to failure plays a more significant role in promoting resilience. Hence, the proposed methodology and research findings can serve as a valuable reference for the development planning and emergency management of public transportation systems. [ABSTRACT FROM AUTHOR]

• A multistage reliability-constrained stochastic planning model is established for the diamond distribution network(DDN). • The Markov decision process is used to model the multistage stochastic dynamic decision process. • An approximate dynamic programming(ADP) algorithm is used to solve the model. In this paper, a multistage reliability-constrained stochastic planning model is established for the diamond distribution network, a new type of distribution system grid structure, with the goal of optimizing the total cost of investment and operation. The uncertainty of load growth is simulated by generating multiple load growth scenarios, and the power supply loss caused by the reliability of components in the system is also considered. The multistage stochastic dynamic decision process is modeled by The Markov decision process. In order to solve the "curse of dimensionality" problem in multi-stage stochastic planning model, the approximate dynamic programming algorithm is used to solve it. The case study is based on the typical structure of the diamond distribution network and realistic data of an actual district in Shanghai, China. The planning results verify the effectiveness of the model and the solution algorithm, and demonstrate the reliability and economy of the diamond distribution network are superior to the traditional double-ring network. [ABSTRACT FROM AUTHOR]

Accurate assessment of the sustainable development level is crucial to providing reasonable and effective management recommendations. However, analysis with a single scale can only reflect the sustainable status at a specific level. In this study, a multi-scale analysis of sustainability was conducted via the three-dimensional Ecological Footprint (EF) model in the Yangtze River Delta (YRD). Moreover, driving factors of sustainability were analyzed using the Multi-scale Geographically Weighted Regression model (MGWR). The conclusions include: (1) the development of YRD showed an unsustainable trend from 2014 to 2021, with per capita EF decreasing from 5.769 ha/cap to 5.283 ha/cap. The provincial sustainability is ranked as follows, from being the most sustainable to the least: Anhui Province > Jiangsu Province > Zhejiang Province > Shanghai City. (2) Based on dual indicators of EF depth and size, 41 cities were categorized into nine groups. The dominant type is always Medium size-Low depth (ML) and High size-Low depth (HL) type cities from 2014 to 2021. From the perspective of city scale, the sustainability of five cities weakened, while seven cities showed improvements. (3) Output value of primary industry (OVPI) and population (POP) were always the dominant factors affecting EF, but showed positive and negative correlations respectively. OVPI changes from north-south symmetric type to central symmetric type, while POP changes from central symmetric type to north-south symmetric type. This paper aims to comprehensively assess the sustainable development status from agglomeration-province-city perspectives, and its findings can provide a reference for similar regions with unbalanced development status between ecology and production. • The sustainability of the world-class urban agglomeration was assessed via the three-dimensional ecological footprint model. • More comprehensive and accurate analysis results based on multi-scale analysis. • The four patterns of influencing mechanism are revealed based on the MGWR model. • The combination analysis of dual indicators was adopted to classify the sustainability of 41 cities. [ABSTRACT FROM AUTHOR]

Probabilistic site characterization is the cornerstone of data-centric geotechnics, which underpins the digitalization in geotechnical engineering. The essential aspect of probabilistic site characterization is to reasonably infer the probability density distributions of geotechnical parameters, based on field and laboratory data. In this paper, a clay property database, SZ-CLAY/11/5130, was established by collecting geotechnical investigation reports from 52 engineering projects in Shenzhen city. This SZ-CLAY/11/5130 database was first compared with the global clay database CLAY/10/7490 and the Shanghai clay database SH-CLAY/11/4051, in terms of their sample ranges, correlations, and scatter plots. Then, the multivariate probability distribution of eight clay parameters was established using the training records selected from SZ-CLAY/11/5130, to characterize the generic correlations between multiple clay parameters. Based on Bayesian theory, the posterior distributions of clay mechanical parameters from a construction site in Shenzhen city were updated using both the site-specific correlations and the generic correlations given by SZ-CLAY/11/5130. The constructed multivariate probability density distribution is necessary for this updating step. [ABSTRACT FROM AUTHOR]

Group travel behaviour widely exists in cities, but has not been well investigated by researchers. To fill this gap, this paper develops a co-existence-based methodological framework to systematically explore the spatiotemporal characteristics of group travel behaviour. We apply our framework to a case study of Shanghai, China, using a one-month tranche of metro smart card data. Results show that most travellers perform a small number of group trips, together with a small number of co-travellers. They usually travel in a dyad or triad group and form far more small social communities than large ones. Group travel behaviour is distinctly different from individual travel in terms of both time and space: group travellers are more likely to travel during weekends, on holidays, and in the afternoons and evenings. They also prefer to perform group behaviour near stations located in the city centre or the centres of new towns in suburban areas, and close to attractions and public facilities. The analysis we present has various potential applications such as improving the management of public events and supporting the design of group ticket policy. [ABSTRACT FROM AUTHOR]

The operational characteristics of the combined solar-ground source heat pump system (referred to as SGSHPS) are influenced by local solar resources, climate characteristics, and operating modes. This paper aims to study the operational characteristics of SGSHPS in Shanghai under different operation modes, providing a reference for engineering applications of SGSHPS. Three operation modes were selected for this study: operation of the soil source heat pump, tandem operation mode 1, and tandem operation mode 2. The tandem operation mode 1 represents the heat pump source-side outlet fluid passing first through the buried pipe and then through the thermal storage tank, The tandem operation mode 2 represents the heat pump source-side outlet fluid passing first through the thermal storage tank and then through the buried pipe. The highest system COP was experimentally measured for tandem mode 1. TRNSYS software was used to construct the three operation modes and obtain parameters such as unit COP, system COP, and buried pipe heat exchange. By comparing and analyzing the winter typical january month average system COP values obtained from experiments and simulations for each operation mode, The maximum error between the experimentally derived conclusions and the simulated conclusions is 9.9 %. The winter typical month january average system COP for the operation of the soil source heat pump was 3.26, while for tandem mode 1 it was 3.5, and for tandem mode 2 it was 3.44. Tandem mode 1 exhibited a 7 % higher system COP compared to the operation of the soil source heat pump, and a 1.7 % higher system COP compared to tandem mode 2. This indicates that the tandem operation of the solar-ground source heat pump is more energy-efficient than the operation of the soil source heat pump system. Therefore, tandem mode 1 is considered the optimal operating mode for SGSHPS in Shanghai during winter. [ABSTRACT FROM AUTHOR]