Search

• 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]

As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks. • The paper constructed a comparative analysis on the evolution of China's urban networks during the last decade. • Employing machine learning to investigate the potential determinants. • The study examined the distinct attributes of the evolution and structure of high-speed rail and airline networks. • The study contrasts the determinants influencing the evolution of high-speed rail and airline networks at the city level. [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]

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]

Efficient road and building footprint extraction from satellite images are predominant in many remote sensing applications. However, precise segmentation map extraction is quite challenging due to the diverse building structures camouflaged by trees, similar spectral responses between the roads and buildings, and occlusions by heterogeneous traffic over the roads. Existing convolutional neural network (CNN)-based methods focus on either enriched spatial semantics learning for the building extraction or the fine-grained road topology extraction. The profound semantic information loss due to the traditional pooling mechanisms in CNN generates fragmented and disconnected road maps and poorly segmented boundaries for the densely spaced small buildings in complex surroundings. In this paper, we propose a novel attention-aware segmentation framework, Multi-Scale Supervised Dilated Multiple-Path Attention Network (MSSDMPA-Net), equipped with two new modules Dynamic Attention Map Guided Index Pooling (DAMIP) and Dynamic Attention Map Guided Spatial and Channel Attention (DAMSCA) to precisely extract the building footprints and road maps from remotely sensed images. DAMIP mines the salient features by employing a novel index pooling mechanism to retain important geometric information. On the other hand, DAMSCA simultaneously extracts the multi-scale spatial and spectral features. Besides, using dilated convolution and multi-scale deep supervision in optimizing MSSDMPA-Net helps achieve stellar performance. Experimental results over the seven benchmark building and road extraction datasets namely, Porto, Shanghai, Massachusetts Road, Massachusetts Building, Synthinel-1, WHU Satellite I and WHU aerial Imagery dataset, ensures MSSDMPA-Net as the state-of-the-art (SOTA) method for building and road extraction as our method beats the next best alternatives by 5.94%, 2.55%, 3.97%, 11.64%, 6.86%, 6.92%, 2.57% IOU and 3.98%, 1.90%, 2.43%, 7.17%, 3.98%, 4.99%, 1.37% F1 score, respectively, over the mentioned datasets. The code has been released in: https://github.com/shirshabose/MSSDMPA-Net. • We propose a novel attention-guided pooling mechanism to alleviate information loss. • Prior generated probability map guided novel attention for multi-scale geometry-aware feature aggregation. • Multi-scale optimized with deep supervision to generate prior probability maps. • Dilated convolutions used to extract features of objects of varied shapes. • Multi-scale feature aggregator network for building and road segmentation. [ABSTRACT FROM AUTHOR]

• Industrial robots can significantly promote firm productivity. • The main mechanism comes from the improvement of human capital structure and innovation of firms. • Industrial robots are more likely to increase the total factor productivity of non-state owned and small-scale firm. • Industrial robots are more likely to increase the total factor productivity in firms that have political affiliations and low market concentration. • Industrial robots are more likely to increase the total factor productivity in firms that low financialization, and high managerial competence. Industrial robotics has become a driving force in the development of high-quality manufacturing enterprises. This paper investigates the impact of industrial robotics on the total factor productivity of enterprises and its mechanism of action through the use of data from A-shares of Chinese companies listed on the Shanghai and Shenzhen exchanges from 2007 to 2019. We found that the use of industrial robots significantly increases the total factor productivity of enterprises, findings that still hold when Bartik instrumental variables are constructed. The mechanism of action suggests that industrial robot application improves both the human capital structure and the innovation capability of enterprises, thus increasing total factor productivity. Heterogeneity analysis shows that industrial robot applications are more likely to increase total factor productivity in firms that are non-state-owned and small scale and have political affiliations, low market concentration, low financialization, and high managerial competence. The findings of this study will help to accurately assess the productivity effects of industrial robotics, and the government should continue to promote the integration of industrial robotics and manufacturing development and actively cultivate independent innovation capabilities and professional skills in the field of industrial robotics in order to increase total factor productivity in enterprises. [ABSTRACT FROM AUTHOR]

Understanding consumers' purchase behaviors is fundamental to the success of the dairy industry. With its economic importance, the Chinese market is critical to dairy producers in most countries around the world. However, understanding consumers in this market is particularly challenging, as these consumers often have a different relationship with dairy products than consumers elsewhere in the world, given the country's historical dairy-related scandals. This special relationship can be characterized by what consumer behavior researchers call "high involvement," indicating that Chinese dairy consumers often attempt to reduce the level of risk associated with buying dairy products. Surprisingly, although this relationship affects several important aspects of purchase behavior, examination of the concept of involvement in the dairy sector has not been widely considered. Of note, there is no understanding of how Chinese consumers vary in their involvement levels and their implications on their dairy purchase behaviors. Nor have there been involvement-based insights provided on how dairy companies can position their marketing strategy to suit the needs of these consumers better. Thus, this study proposes a new approach to understanding Chinese consumer dairy decisions by introducing "consumer involvement" as a segmentation tool through which individuals' behaviors can be predicated according to their involvement profile. Based on an online survey administered in Shanghai using 1,073 dairy consumers, principal component analysis confirmed involvement with dairy is a multidimensional construct with the following 4 factors: pleasure value, symbolic value, risk importance, and risk probability. A 2-step cluster analysis identified 4 consumer clusters based on their involvement profile: face-concerned dairy lover, carefree dairy consumer, cautious dairy lover, and confused dairy consumer. According to a one-way ANOVA test and cross-tabulation with χ2 test, these consumer segments behave differently in relation to the extensiveness of decision making, cue utilization, trust of information sources, and consumption behavior. The outcomes in this paper further explain why efforts to restore consumer trust for dairy products do not work among some consumers, as individuals may exhibit diverse attitudes toward such information due to their heterogeneous involvement levels. The study also provides suggestions for market practitioners and organizations to develop effective target market strategies and policies according to different consumer clusters. [ABSTRACT FROM AUTHOR]

This paper studies the problem of the polygonal mapping of buildings by tackling the issue of mask reversibility, which leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments, and high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on four public benchmarks, including the AICrowd, Open Cities, Shanghai, and Inria datasets. On the AICrowd, Open Cities, and Shanghai datasets, our proposed method obtains unanimous improvements on the metrics of AP, AP boundary and PoLiS by large margins. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU/HiSup. [ABSTRACT FROM AUTHOR]

Currently, China is at a critical stage of economic transformation and pollution control. The environmental pollution control efficiency (PCE) deserves more in-depth study. This paper adopts a three-stage global data envelopment analysis (DEA) model and a global Malmquist index model to measure the PCE and the sources of its changes in China from 1991 to 2019. Study shows that: (1) the overall level of PCE has remained low over the past 29 years, and it has a slowly fluctuating upward trend, with an average annual growth rate of only 0.92 %; (2) the lower pure technical efficiency hinders the effectiveness of environmental pollution control, which is mainly affected by the slow technological progress; and (3) at the provincial level, the overall efficiency of Shanghai and Guangxi has long been above 0.9, while the efficiency of other provinces has been below 0.9 for quite a long time. At the regional level, the Yangtze River Delta is the most effective area, while the Yellow River Basin is the most ineffective area. In consideration of this, we provide specific strategies for improving PCE of each province, from three aspects of increasing the capital input, optimizing the scale structure, and upgrading technology level. [Display omitted] [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]

• A deep freight-on-rail-transit strategy is proposed to integrate urban transport. • Drivers and barriers to deep FoRT strategy are identified. • Suitability of MRT-based integrated logistics system adoption in China is assessed. • Influencing factors of project adoption and policy implications are suggested. • Government support and public–private partnerships are critical to project success. Integrating goods movement into public/mass rapid transit (MRT) is an emerging initiative to improve urban freight transport services and sustainability. This paper explores new prospects to achieve extensive non-road city logistics based on a deep freight-on-rail-transit (FoRT) strategy. To begin, the strengths, weaknesses, opportunities, and threats (SWOT) of deep FoRT are analyzed in view of the development status quo of urban China. Next, a multi-criteria assessment model driven by real-world data and 11 quantified metrics is proposed to judge the suitability for developing the MRT-based integrated logistics system (MILS) in 16 Chinese cities. Finally, critical factors influencing MILS project adoption are explicated, and the possible supportive policies are discussed from aspects of planning, regulation, funding, marketization, and innovations. Results show that the alignment with national development goals, rich social-environmental benefits, and stakeholder interest are the primary drivers of deep FoRT strategy, whereas poor planning and decision-making, governance and management deficiencies, and high investment could be the main hurdles. Priority of MILS project adoption in the selected cities is divided into four tiers, where Shanghai, Beijing, and Shenzhen are recognized as the three best candidates. Strong and coordinated policies are needed to integrate the strategy into urban planning. [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]

Community micro-renewal is an important aspect of urban regeneration. Residents, as the primary users of the community, have become a vital component of community micro-renewals. With the advent of participatory design, designers are no longer the dominant constructors of communities. Residents' participation is essential to current community micro-renewals, but the existing resident involvement still reflects a lack of initiative and an underutilization of potential. Instead, co-creation, in which residents can be deeply involved in the community micro-renewals, is becoming the mainstream. This paper categorizes co-creation models in community micro-renewals into three types according to the degree of residents' participation: the co-creation model based on common ideation, the co-creation model for resident participation in the design process, and the co-creation model of resident-led community regeneration. Three corresponding cases in Shanghai, China are analyzed in relation to specific scenarios and typologies. Each case study provides a contextualized interpretation of the characteristics associated with different types of co-creation. Increased residents' participation in the process of co-creation has been found to positively impact residents' identification with community micro-renewals and their intrinsic commitment to sustain them. Furthermore, highly participatory co-creation empowers residents to assume a higher level of responsibility for the project and mitigates the risk of uniformity. The contribution of this paper is to provide a theoretical basis for subsequent practices. Specifically, this research aims to facilitate a comprehensive integration of participatory design methodologies with community micro-renewal initiatives. [ABSTRACT FROM AUTHOR]

• MCCA model improves the accuracy of land use structure simulation in YRD. • The index of land ecological risk assessment is expanded and optimized based on the regional characteristics of the YRD. • The ecological risk levels in 2020, 2025 and 2035 are evaluated considering ecological and economic benefits. • The spatial and temporal changes and aggregation patterns of ecological risk levels are discussed from both city-scale and patch-scale. • Several suggestions are provided for formulating more scientific and effective land use management strategies. Since land resources are essential to human survival, the over-exploitation and unreasonable utilization of land threatens the sustainability of human society. This paper uses the well-developed Yangtze River Delta (YRD) region as a case study, and adopts an improved Mixed-cell Cellular Automata Model (MCCA) to simulate the potential utilization of land resources in 2025 and 2035. Then, the paper attempts to construct a comprehensive assessment system that integrates multiple indicators, and evaluates regional risk changes at a micro patch-cell level and a macro city. The results suggest that (1) The utilization of land resources varies greatly in the YRD region from 2020 to 2035. While Shanghai will continue to make use of available built up land resources, the majority of land resources in Jiangsu Province will shift away from being agricultural in nature. Since forests make up a majority of the land resources in Zhejiang Province, and forests and agricultural land make up a majority of land resources in Anhui Province, the change in land utilization in these two provinces will be more gradual. (2) Changes in land utilization are also likely to cause ecological risks. The risk level in Shanghai is the highest, it is relatively high in Zhejiang but is likely to decrease, relatively low in Jiangsu but likely to increase, and Anhui has the lowest risk level. (3) The K-means clustering method obtained three ecological risk zones of the YRD, and the overall risk level is the highest in the central-eastern zoning ③, followed by the southern zoning ②, and the lowest in the northern zoning ①. Furthermore, the agglomeration pattern of the three zones transforms from L-L to H-H. (4) This paper puts forward corresponding suggestions and countermeasures for four aspects, including the strengthening of agricultural land protection, the regulation of built-up land use expansion, the coordination of an all-rounded approach of urban development, and the fostering of regional sustainable development, in hopes of optimizing land usage and alleviating ecological risk levels. Scientific estimations of potential ecological risks are critical to avoiding future ecological crises thus contributing to more sustainability. [ABSTRACT FROM AUTHOR]

Plastic pollution is currently one of the environmental pollution issues of global concern, and microplastics (plastics with a size ≤ 5 mm) have also attracted much attention due to their large specific area, higher pollutant adsorption capacity, and higher migration performance. As the population increases and more chemical products are manufactured, the level of microplastic pollution continues to increase. Microplastics discharged from sewage treatment systems will enter the ocean and rivers, so estuary ecosystems are vital to controlling marine microplastics. As the estuary of China's most significant river entering the East China Sea, the Changjiang River Estuary has become one of the typical areas for microplastic research in estuaries. This paper systematically reviews the sources, hazards, and impacts of microplastics in the Changjiang Estuary. Firstly, the research on microplastics in the Changjiang Estuary was analyzed, and we found that the bioaccumulation, removal, and interaction with microorganisms of microplastics have become a new research hotspot. At the same time, the current abundance of microplastics in the surface water of the Changjiang River Estuary is 1.71 ± 3.12 MP/L, which is lower than that of the Changjiang River area and the river farmland in Shanghai, but still higher than the 0.22 ± 0.27 MP/L in the area adjacent to the East China Sea. Microplastics smaller than 1000 μm accounted for 68% of the total, while transparent, white, blue, and black were the primary colors of microplastics in the Changjiang Estuary. In addition, polyethylene and polypropylene are the primary materials of microplastics in the central Changjiang River Estuary and related water bodies, and fibers are the main shape of microplastics. Industry, agriculture, domestic sewage, and air deposition may be the primary sources of microplastics in the Changjiang Estuary. The numerous lakes and water conservancy facilities in the Changjiang River Basin can reduce the number of microplastics reaching the Changjiang Estuary to a certain extent. At the same time, seasonal rainfall and ocean currents will affect the abundance of microplastics in the surface waters of the Changjiang River Estuary. Finally, this paper puts forward relevant suggestions based on the actual situation of the Changjiang River Estuary and the developed microplastic treatment technologies. This article will provide a structured reference for future research on microplastics in the Changjiang Estuary and assist in assessing microplastics' impact on the ecological environment. The Fate of Microplastic Pollution in the Changjiang River Estuary. [Display omitted] • Microplastics' sources, hazards, and impacts in the Yangtze Estuary were reviewed. • Bioaccumulation, removal, and interaction with microorganisms are the hotspots. • The microplastics in the water of the Changjiang mainly exists in the form of fibers. • Source control and establishment of MPs barriers will help to solve MPs pollution. [ABSTRACT FROM AUTHOR]

Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. [ABSTRACT FROM AUTHOR]

Accessibility equity is a critical factor in developing equitable and sustainable communities. Although numerous studies have analyzed accessibility inequity, few of them examined inter-group and intra-group inequity and made comparisons between private cars and public transit. In this paper, a Gaussian-based Two-step Floating Catchment Area method was applied to evaluate the spatial accessibility of multitier healthcare services among different income groups in Shanghai, China. The results show that high-income households have better access to healthcare services than low-income households. The gap between low- and high-income groups increases as the level of healthcare services rises. The Theil index was used to assess inequity between groups and within groups, as well as overall inequity. The inter-group inequity for public transit is greater than that for private cars, suggesting that accessibility by public transit is more unequally distributed across groups of people compared with private cars. Inter-group inequity increases with the tier of healthcare services. The bivariate local Moran index shows that underserved areas are found primarily in low-income neighborhoods. Overall, the research findings indicate the quality of public transit services might have a more significant impact on accessibility inequity than the spatial distribution of health services. This study provides valuable information for designing targeted policy provisions to improve equity and efficiency in healthcare access. • This study examines inter-group and intra-group inequity, as well as total inequity. • Travel time estimation from an online Maps API was applied to assess accessibility. • The gap in accessibility between low- and high-income groups increases as the level of healthcare services rises. • The inter-group inequity in accessibility by public transit is more severe than that by private cars. [ABSTRACT FROM AUTHOR]

• CO 2 air conditioning systems driven by compressed air engines are designed innovatively. • CO 2 air conditioning system driven by compressed air engine can significantly reduce running costs. • Combination of air conditioning system and compressed air storage is promising. Traditional building air conditioning uses electricity or thermal energy to drive the refrigeration cycle. The energy released by the expansion of compressed air can also be used for driving the refrigeration cycle. Moreover, the expanded low-temperature air can be used in the air conditioning system too. In this paper, three forms of CO 2 air conditioning systems driven by novel reciprocating piston compressed air engines were designed, namely EAGC (Exhaust Air for Gas Cooler), EAS (Exhaust Air for Sub-cooler), and EASC (Exhaust Air for Space Cooling), to analyze the performance and economics of CO 2 air conditioning systems combined with compressed air energy storage technology. Twenty-seven typical cities were selected in different climate regions in China to evaluate the systems' performance and running cost under outdoor temperature changes and different electricity prices. The results show that compared with the electricity-driven CO 2 air conditioning system, the three systems can greatly improve the system COP (Coefficient of Performance) but still cannot reach the performance level of the conventional freon refrigeration systems. However, in cities like Beijing and Shanghai with the large peak-to-valley electricity price difference, EASC system compared to the electricity-driven R410A air conditioning system running costs are lower, the average running cost per hour can be saved 11.96% and 16.35%, respectively. This means that the CO 2 air conditioning system with compressed air energy storage technology has a certain economic application value and can be popularized on reasonable occasions. This innovative system design can help solve the contradiction between supply and demand of building energy, realize leisure energy consumption, and reduce the economic cost of energy consumption on the demand side. [ABSTRACT FROM AUTHOR]

• This paper reveals properties of ride-sourcing customer order cancellation behavior from a real dataset. • This paper models customer order cancellation behavior in coupled ride-sourcing and taxi markets to explain the observed phenomena. • This paper illustrates the importance of appropriately designed penalty/compensation strategies to both platform profit and social welfare. In today's world, massive on-demand mobility requests are dispatched every hour on ride-sourcing platforms, however, customers later cancel quite a number of these confirmed orders. This paper makes the first attempt to look into this customer confirmed-order cancellation behaviour based on a two-month, hourly average dataset of Didi Express in Shanghai provided by Didi Chuxing. The mean ride distance and pick-up distance of cancelled orders are observed to be obviously longer than those of completed orders of the same time period, reflecting an obvious impact of travel cost on customer decisions of order cancellation. However, the correlation between the mean customer confirmed-order cancellation rate (COCR) and the mean customer waiting time for pick-up of cancelled orders is significantly negative. Shanghai, like many other cities around the world, has coupled ride-sourcing and taxi markets, and as such, this counter-intuitive phenomenon can be explained as an outcome of the lower (higher) chance of meeting vacant taxis while waiting for pick-up during peak (non-peak) hours. We formulate COCR as a function of customer waiting time for ride-sourcing vehicles and cruising taxis, the penalty strategy by the platform for cancellation of confirmed orders, and customers' own characteristics (i.e., ride distance, value of time, perceived psychological cost of order cancellation, additional safety concern over ride-sourcing), and propose a system of nonlinear equations to depict the complex interactions between the ride-sourcing and taxi markets considering the probability of ride-sourcing cancellation after order confirmation. With the proposed model, we replicate the observed lower COCR under a higher demand rate and longer waiting time for pick-up through numerical examples, and highlight the potential improvement of platform profit that can be achieved by appropriately designed penalty/compensation strategies. [ABSTRACT FROM AUTHOR]

In many countries, national governments deploy city-regionalism not simply as a domestic policy tool but also as a geopolitical device enabling the internationalization of state territory and economy. Focusing upon the evolution of Yangtze River Delta (YRD) region in China, this paper offers new insights into the close associations between Chinese city-regionalism and the geopolitical orchestration of national development. Rather than conceived as an inevitable outcome of contemporary globalization processes, the development of YRD region is instead examined against the changing geopolitical dynamics and national objectives of the Chinese Communist Party (CCP) from 1949 to the present day. In the early phase of CCP rule, the development of regional urban centres, such as Shanghai in YRD region, was an instrument of national territorial integration and class unity. Following economic reforms in the 1980s, the formal designation of YRD as a city-region reflected the Chinese state's aspiration for accelerated economic growth and the internationalization of the domestic economy. Now confronted with widening regional inequalities, the Chinese state has greatly expanded YRD region to incorporate peripheral cities and provinces for the sake of regionally coordinated development. Even as YRD transforms into a global city-region, Shanghai seemingly separates itself functionally and discursively from the rest of the region. In departing from contemporary post-Westphalian or hyper-globalist perspectives on the rise of global city-regions in a stateless world, the paper provides a new interpretation of Chinese city-regionalism as a case of countervailing geopolitical processes at work within and beyond national state borders. [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]

Cascade hydropower plants which have good regulation performance and are managed by the dispatching center of regional power grids are usually required to simultaneously shave the peak load for multiple provincial power grids, which is an important way of relieving the growing peak shaving pressure on power grids in central and eastern China. This paper establishes an optimization model for the short-term generation scheduling of cascade hydropower plants in regional power grids. In this model, minimizing the peak-valley load difference of multiple power grids is adopted as the objective function. In addition to conventional hydraulic constraints, the operation constraints of individual hydropower units, electrical constraints and head effect on the power generation are all considered to obtain precise scheduling. The original nonlinear and non-convex model is converted into a standard mixed-integer linear programming (MILP) formulation through several linearization strategies. The results for the real-world case study indicate that: 1) the proposed model is computationally efficient with a calculation time of 326 s; 2) the peak-valley differences of the Shanghai Power Grid and Zhejiang Power Grid decreased by 9.71% and 2.29%, respectively; 3) compared with actual operation, the proposed model shows better performance in peak shaving for multiple provincial power grids. • Daily operation of cascade hydropower plants shaving peak load for multiple power grids is studied. • Operation constraints of hydropower units and head effect on power generation are considered. • An MILP based model is developed through several linearization strategies. • The proposed model has better peak shaving performance compared with actual operation. [ABSTRACT FROM AUTHOR]

Efficient passenger flow assignment is essential for ensuring the safe and stable operation of the urban rail transit (URT). This paper proposes a new passenger flow assignment approach to address this challenge. The approach generates two agents: connected vehicle and unconnected passenger, to simulate vehicle operation and passenger travel for the URT network. It derives the passenger trajectory by the passenger route evolution mechanism to achieve the passenger flow assignment for the whole network. Furthermore, it provides statistics on platform passenger flow when obtaining the overall station passenger flow. Meanwhile, the approach employed Automatic Fare Collection (AFC) data as input to analyze the passenger flow features of the Shanghai Metro. The passenger flow assignment results can reveal the start and end of morning and evening peak hours at a time granularity of seconds. That is, 7:16:20 and 9:33:00 for the morning peak and 16:49:00 and 19:22:50 for the evening peak, respectively. The results also reveal the route choice pattern of passengers. Passengers have preferences for each type of route, in descending order: the shortest route, the minimum number of stations route, and the minimum number of transfer stations route, when they have a choice of these three routes. Moreover, the results can accurately identify large passenger flow stations and provide data support for station passenger flow management strategies. • This paper proposes a new passenger flow assignment approach. • The approach simulates connected vehicles and unconnected passengers. • Count platform passenger flow when obtaining the overall station flow. • Analyzing passenger route choice preferences and large passenger flow stations. [ABSTRACT FROM AUTHOR]

This research explores the pattern of institutional change and its underlying logic in China's recent land development governance. China's central government has been issuing national policies since 2012 to promote urban regeneration to improve land use efficiency and sustainability. Through the lens of historical institutionalism, this paper analyses the making and implementation of Shanghai's new urban regeneration policies since 2014, which was a response to the central state's mandate. Results show that the redevelopment mode encouraged by the municipal governments is financially unattractive to district governments and market entities. Pro-growth development governance maintains strong inertia since it has been embedded in interlocking institutional arrangements. However, the top-down campaign temporarily changed the power distribution among different levels of government and cultivated incremental institutional layering. A small range of redevelopment projects were implemented through political mobilisation of the state-owned enterprises' financial resources and the local cadre's attention. This paper concludes that the transformation of China's land development governance is an inevitably arduous top-down process, characterised by constant rescaling of the distribution of power and constant adjustments to reconcile the pro-growth inertia and the state mandate toward sustainability. • Adopting historical institutionalism, the paper focuses on power in urban governance. • Sustainable land development modes contradict China's pro-growth inertia. • Top-down political mobilisation drives the paradigm shift in urban redevelopment. • Political mobilisations temporarily alter power distribution and trigger institutional changes. • Arduous incremental institutional layering in urban governance is inevitable. [ABSTRACT FROM AUTHOR]

With the continuous development of the urban underground space, the depth of the underground structures is also increasing. As a typical soft soil area, the geological environment in Shanghai is very complex and rich in groundwater. Unlike traditional support measures, the artificial ground freezing method can be applied to various complex geological conditions with its unique advantages. In this paper, the distribution of the freezing temperature field and the frost heave deformation for a deep foundation pit constructed by the pile freezing method were studied by the numerical simulation. The distribution of the freezing temperature field in the single freezing hole, the double freezing holes and the single row holes were analyzed, respectively. After the excavation of the deep foundation pit, the temperature fields of straight and corner sections were also studied. The effect of the piles on the distribution of the freezing temperature field and the frost heave deformation of the ground was compared. The research results of this paper can provide a design basis and a reference for the construction of deep foundation pits by the artificial ground freezing. • AGF with row piles was conducted to deep foundation pits. • Freezing temperature field and frost heave of deep foundation pit were studied. • Effect of row piles on temperature field and deformation were analyzed. [ABSTRACT FROM AUTHOR]

• This paper examines the integrated optimization of train stopping plan and seat allocation. • The integrated optimization problem is formulated as a mixed-integer mathematical model. • The integrated optimization approach improves efficiency against separate optimization of stopping plan and seat allocation. • Numerical studies were conducted on Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors. This paper examines the integrated optimization of train stopping plan and seat allocation scheme in railway systems, where equilibrium passenger travel choice under elastic demand is considered. The integrated optimization problem is formulated as a non-concave and non-linear mixed-integer mathematical model, where the objective is to maximize the system net benefit considering ticket revenue, consumer surplus, and cost associated with train stoppings. The integrated optimization model can be reformulated into a mixed-integer linear programming (MILP) model based on a series of linearization, relaxation, and outer-approximation techniques, which can then be solved by commercial MILP solvers (e.g., GUROBI). We also compare the integrated optimization approach with that when the train stopping plan and seat allocation are optimized separately and identify the potential benefits. Numerical studies have been conducted on a small-scale example, the Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors to illustrate the proposed model and solution approach. [ABSTRACT FROM AUTHOR]

Pneumococcal vaccines have been developed to protect infants and young children from pneumococcal diseases. Vaccination coverage studies are important in determining a population's vaccination status and strategically adjusting national immunization programs (NIP). In this paper, we aim to describe the coverage of pneumococcal conjugate vaccines (PCVs) immunization for birth cohorts from 2012 to 2020 and discussed the factors influencing the coverage. Vaccination data were collected via the vaccination information database in Shanghai, China, for children born from 2012 to 2020. The population data used in this study were collected from each community from 2012 to 2020. The coverage of initial immunization (1st dose), basic immunization (three doses) and full immunization (3 + 1 doses) for PCVs was calculated according to the number of doses received. As vaccination coverage was assessed each year, Annual Growth Rate (AGR) was used to describe the variation trend of vaccination coverage. Immunization time and completeness of different PCVs were also analyzed. The total number of births from 2012 to 2020 was 38,268 in Huangpu District, Shanghai, China. The initial immunization coverage of PCVs increased from 12.26% in 2012 to 49.65% in 2020, and the highest coverage was 50.61% in 2019. The cumulative vaccination coverage of PCVs was 19.4% for initial immunization and 16.8% for basic immunization from 2012 to 2020. And cumulative full immunization coverage of PCVs was 12.3% from 2012 to 2019. The PCVs coverage of most vaccination statuses showed an obvious upward trend from 2017 to 2020. Despite an upward trend in vaccination coverage of PCVs, the vaccination coverage of initial, basic and full immunization among children is still low. And given the heavy burden of Streptococcus pneumoniae (Sp) among children in China and the fact that the current vaccination coverage cannot effectively protect children, it is recommended that the government include PCVs into the NIP as soon as possible. [ABSTRACT FROM AUTHOR]

Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is also extremely time-consuming, and the performance of CNNs degrades significantly when there is a domain gap between the training and test images. In this paper, to address this problem, a global-local adversarial learning (GOAL) framework is proposed for cross-domain road detection. On the one hand, considering the spatial information similarities between the source and target domains, feature space driven adversarial learning is applied to explore the shared features across domains. On the other hand, the complex background of VHR remote sensing images, such as the occlusions and shadows of trees and buildings, makes some roads easy to recognize, while others are much more difficult. However, the traditional global adversarial learning approach cannot guarantee local semantic consistency. Therefore, a local alignment operation is introduced, which adaptively adjusts the weight of the adversarial loss according to the road recognition difficulty. Extensive experiments were conducted on different road datasets, including two public competition road datasets—SpaceNet and DeepGlobe—and our own large-scale annotated images from four cities: Boston, Birmingham, Shanghai, and Wuhan. The experimental results show that the proposed GOAL framework can clearly improve the cross-domain road detection performance, without any annotation of the target domain images. For instance, taking SpaceNet road dataset as the source domain, compared with the no adaptation method, the IOU performance of GOAL framework is increased by 14.36%, 5.49%, 4.51%, 5.63% and 15.14% on DeepGlobe, Boston, Birmingham, Shanghai, and Wuhan images, respectively, which demonstrates its strong generalization capability. [ABSTRACT FROM AUTHOR]

• Station-based one-way carsharing benefits in a developing country. • The results in Shanghai are compared with the relevant research results in different cities in the world. • Operation data and questionnaire survey are combined to evaluate the carsharing's impact on vehicle ownership. • Characteristics of users affected by carsharing are analyzed and the key factors are identified. • Shanghai carsharing mainly suppresses vehicle growth by reducing individual willingness to buy a car. Carsharing is considered an important alternative to the private car. Based on the studies in Europe and North America, it is agreed that carsharing can reduce private car ownership. However, due to the differences in the development stages and characteristics of cities and transportation, it is unknown whether carsharing can also decrease car ownership in China, which results in a gap in the evaluation of carsharing in China. This paper takes the carsharing project EVCARD in Shanghai as an example. Based on survey data on vehicle ownership from users and actual operations data, the impacts of carsharing on selling cars, giving up and delaying purchasing a car in Shanghai are quantitatively analyzed. The results in Shanghai are compared with the relevant research results in different cities around the world. The results show that: (1) the carsharing project in Shanghai also has the benefit of reducing private car ownership. Each carsharing vehicle can replace 4.56 private cars. However, unlike in European and North American cities, Shanghai carsharing mainly suppresses vehicle growth by reducing individual willingness to buy a car, because the proportion of cars sold owing to carsharing is obviously lower than in Europe and North America. (2) For giving up car purchases, carsharing mainly suppresses the first car in the family. (3) Good public transport and carsharing services can substantially improve the substitution benefits of carsharing for private cars. If carsharing can reach the service level of "whenever and wherever carsharing is available", the proportion of users who sell, give up and delay buying cars will increase from 0.78%, 30% and 31% to 12%, 40% and 47% respectively. The findings in the study provide a quantitative basis for assessing the carsharing benefits on the reduction of vehicle ownership in China, and also provide a useful reference for evaluating carsharing benefits in other developing countries. [ABSTRACT FROM AUTHOR]

In recent years, natural disasters happening over the world pose severe challenges to emergency evacuation systems, especially for highly-populated urban community areas. Underground spaces not systematically utilized yet provide a new development direction for the construction of emergency shelters. Emergency response for mitigating negative impacts of disasters calls for well-designed shelter system and effective management of pedestrian evacuation. In this paper, we study the location selection of underground emergency shelters in urban community areas, along with pedestrian evacuation planning once disaster happens. The aim is to effectively use the existing underground space and sidewalk network to satisfy pedestrian evacuation demand as much as possible. We formulate the problem as a network flow model, and develop a minimum-cost-maximum-flow solution approach. A real-world case study based on the East Nanjing Road community area in central Shanghai is conducted. The results show that sidewalks are the bottleneck of the pedestrian evacuation network due to their limited capacity. Reserving vehicle lanes, implementing one-way evacuation, and opening local small-scale underground shelters, are shown to be effective measures for improving the pedestrian evacuation performance. Overall, the proposed methodology and managerial insights based on the case study in Shanghai show a great potential of employing underground space as shelters for pedestrian evacuation system design in urban community areas. [ABSTRACT FROM AUTHOR]

Under the context of Pilot Free Trade Zones (PFTZs), ports are confronted with new opportunities to realize high-quality development. This paper first applies the Super-SBM model to evaluate and analyze the overall technical efficiency (OTE), pure technical efficiency (PTE), and scale efficiency (SE) of the ports in six typical China's PFTZs from 2010 to 2017 to reveal their development status. By further constructing an OTE-cargo throughput matrix and comparing OTE with and without considering undesirable outputs in 2017, the intensive and green development degree of ports are analyzed. Second, Tobit regression model is employed to identify the influencing factors from both external and internal aspects including the PFTZ factor together with economic and operational indicators. The main findings are as follows. (i) The ports' OTE in the six PFTZs is stable and high, with PTE on the rise and SE somewhat differentiated. Except for the mean of SE, sequences of the port efficiency in terms of standard deviations and other means roughly accords with the time sequence of PFTZs' establishment. (ii) There's heterogeneity in both intensive and green development degree of the ports, and Shanghai Port is the benchmark with the best performance. (iii) The PFTZ factor exerts a positive role in promoting port efficiency, while other significant influencing factors namely economic status, openness and population of hinterland, ownership structure, human capital, as well as operation scale of ports are also detected. Finally, suggestions on high-quality development and efficiency improvement of ports in the PFTZs are proposed. • Ports' OTE, PTE, and SE in six typical China's PFTZs are evaluated. • Evaluation results accord with the time sequence of PFTZs' establishment. • Heterogeneity are in both green and intensive development degree of the ports. • The effectiveness of PFTZ policy in promoting port efficiency is verified. • External and internal influencing factors are identified. [ABSTRACT FROM AUTHOR]