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Sensing urban vitality is a useful method for understanding urban development. However, the spatio-temporal characteristics of the association between the built environment and urban vitality in Shenzhen, the youngest mega-city in China, have not yet been explored. In this paper, we examined the effects of built environment indicators on urban vitality by using spatial regression models and multi-source geospatial data. The main research findings were as follows. Firstly, urban vitality displayed a consistent high–low pattern during both weekdays and weekends. Differences in the distribution of urban vitality with time between weekdays and weekends were more significant. Secondly, the effects of various built environment indicators on urban vitality exhibited significant temporal disparities. Within a day, population density, building density, bus station density, and distance to metro stations all exhibited positive effects, while distance to the central business district (CBD) exhibited negative effects, with pronounced diurnal differences. Moreover, the effects of road network density and functional mix on urban vitality were both positive and negative throughout the day. Thirdly, besides population density and building density, road network density, functional mix, bus stop density, and distance from metro stations exhibited positive and negative disparities within the study area. Overall, distance to the CBD had a negative effect on urban vitality. This concludes that planning for urban vitality should consider the spatio-temporal heterogeneity of the association between the built environment and urban vitality. [ABSTRACT FROM AUTHOR]

China has pledged that it will strive to achieve peak carbon emission by 2030 and realize carbon neutrality by 2060, which has spurred renewed interest in hydrogen for widespread decarbonization of the economy. Hydrogen energy is an important secondary clean energy with the advantage of high density, high calorific value, rich reserves, extensive sources and high conversion efficiency that can be widely used in power generation, transportation, fuel and other fields. In recent years, with the guidance of policies and the progress of technology, China's hydrogen energy industry has developed rapidly. About 42% of China's carbon emissions comes from the power system and Shenzhen has the largest urban power grid in China. Bringing the utilization of hydrogen energy into Shenzhen's power system is an important method to achieve industry transformation, achieve the "double carbon" goal and promote sustainable development. This paper outlines the domestic and international development status of hydrogen energy, introduces the characteristics of Shenzhen new power system, the industrial utilization of hydrogen energy and the challenges of further integrating hydrogen energy into Shenzhen new power system and, finally, suggests on the integration of hydrogen energy into Shenzhen new power system in different dimensions. [ABSTRACT FROM AUTHOR]

Ship exhaust gas has become an essential source of air pollution in recent years. To assess the impact of ship exhaust gas on the atmospheric environment and human health, this paper studies the problem of ship exhaust gas diffusion in the port area. According to automatic identification system (AIS) data, ship exhaust gas is estimated based on the bottom-up method, and the result of emission calculation is entered into a Gaussian puff model to calculate the superposition of the diffusion of gaseous pollutants from multiple ships. In addition, the results of a case study of the diffusion of ship exhaust gas in the western area of Shenzhen Port in China show that the distribution of the NO2 concentration in the studied area is not stable, the diffusion of exhaust gas from multiple ships mainly affects some areas near large ship berths at night, and there is a small impact on the whole study area. This lays a foundation for monitoring and treating the atmospheric environment in the port area. [ABSTRACT FROM AUTHOR]

In this paper, the complex network of the urban functions in Shenzhen of China under the lockdown of the corona virus disease 2019 (COVID-19) is studied. The location quotient is used to obtain the dominant urban functions of the districts in Shenzhen before and under the lockdown of COVID-19. By using the conditional probability, the interdependencies between the urban functions are proposed to obtain the complex networks of urban functions and their clusters. The relationships between the urban functions, and the overall and cluster characteristics of the urban functions before and under the lockdown of COVID-19 are analyzed based on the complex networks. The mean degree and mean weighted degree of the primary categories of the urban functions are obtained to discuss the classification characteristics of the urban functions before and under the lockdown of COVID-19. Then, the differences and changes of the urban functions before and under the lockdown of COVID-19 are compared, and the corresponding policy implications under the lockdown of COVID-19 are presented. The results show that under the lockdown of COVID-19, the correlation of the urban functions is stronger than that before the lockdown; the common urban functions are more useful and essential, and finance, fine food and medical treatment are important; public service and government departments have the most positive relationship with other urban functions, and finance service has the highest spatial agglomeration distribution trend; and the cluster characteristics of urban functions are more related to people's livelihood, and the urban functions show incomplete and cannot be operated for long term. [ABSTRACT FROM AUTHOR]

Since bus prioritization policies can help mitigate urban traffic jams, the planning of bus lanes has drawn considerable attention. Existing methods suffer from a common limitation, which is that the limited spatial adaptability resulting from certain road condition information cannot be directly specified. Many bus GPS trajectories have been accumulated and can be contiguously gathered if needed. This paper proposes a trajectory-based bus lane planning method. First, we formulize the bus lane planning problem as a multiobjective optimization problem in which the road conditions, traffic flow, connectivity of bus lanes, and construction cost are organized as four constraints, and road utilization and bus punctuality are modeled as two objectives. Then, an evolutionary algorithm-based method is presented to solve the problem. We tested the model in the Nanshan District, Shenzhen City, China. Through a comparison with existing survey-based methods, the parameters associated with road conditions in this method are directly extracted from GPS trajectories, and this method is more effectively deployed than other methods. Since GPS trajectories can cover a wide area if needed, and because the proposed method can be effectively executed, this method can be adapted to large urban scales. [ABSTRACT FROM AUTHOR]

Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework. [ABSTRACT FROM AUTHOR]

Heavy mental contamination in urban land is a serious environmental issue for large cities. Visible and near-infrared spectroscopy has been rapidly developed as a new method for estimating copper (Cu) levels, which is one of the heavy metals. Spectral pretreatment is essential for reducing noise and enhancing analysis. In the traditional method, the entire spectrum is uniformly pretreated. However, in reality, the influence of pretreatment on the spectrum may vary depending on the wavelengths. Limited research has been conducted on breaking down the entire spectrum into distinct parts for individualized pretreatment, an innovative method called piecewise pretreatment. This study gathered 250 topsoil samples (0–20 cm) in Shenzhen City, southwest China, and obtained their vis-NIR spectra (350–2500 nm) in the laboratory. This study divided the spectrum into three parts, each processed by six commonly used spectral pretreatments. The number of pretreated parts varied from 1 to 3, resulting in 342 PLSR models being built. Compared to the traditional method, piecewise pretreatment showed an increase in mean residual predictive deviation (RPD) from 1.55 to 1.71 and an increase in the percentage of positive outcomes in ∆RPD from 33.33% to 55.56%. Thus, we concluded that piecewise pretreatment generally outperforms the traditional method. Furthermore, piecewise pretreatment aims to choose the most effective pretreatment method for each part to optimize the Cu estimation model. [ABSTRACT FROM AUTHOR]

Land use transitions play a critical role in ecological environmental restoration, but they are also plagued by ecological environmental problems caused by excessive land resource development. In this study, we propose a methodological framework for unveiling the nexus profile of land use/cover change (LUCC) and eco-environmental effects. This study explored the spatiotemporal evolution patterns of LUCC over a long time series based on high-precision land use data from 1990 to 2020. Then, the ecological values (EVs) of various cities were calculated to obtain the ecological contribution rate of different land use types in the process of change. Finally, the future development trends of land use and ecological environmental quality were predicted under multiple scenarios using the cellular automata–Markov model, and scientific policy recommendations were proposed. The results showed that the expansion trajectory of the construction land in the urban agglomeration mainly expanded inwards along the mouth of the Pearl River, and the conversion of cultivated land to construction land was the most significant type of land use change. The overall ecological environmental quality of the study area showed a downwards trend, with Shenzhen exhibiting the largest decrease in EVs. Cultivated land contributed significantly to improving regional ecological environmental quality, while the land use transition types with relatively large contributions to environmental quality deterioration were conversions to construction land. Under the scenario of coordinated protection, the degree of cultivated land area reduction was significantly reduced, and the area of forestland showed a positive growth trend, with the expansion trend of construction land being reversed. These research findings can enrich the theoretical research on the sustainable development of urban agglomerations and provide reliable data support for policy-making. [ABSTRACT FROM AUTHOR]

Between 7 December 2022 and 28 February 2023, China experienced a new wave of COVID-19 that swept across the entire country and resulted in an increasing amount of respiratory infections and hospitalizations. The purpose of this study is to reveal the intensity and composition of coinfecting microbial agents. In total, 196 inpatients were recruited from The Third People's Hospital of Shenzhen, and 169 respiratory and 73 blood samples were collected for metagenomic next-generation sequencing. The total "Infectome" was characterized and compared across different groups defined by the SARS-CoV-2 detection status, age groups, and severity of disease. Our results revealed a total of 22 species of pathogenic microbes (4 viruses, 13 bacteria, and 5 fungi), and more were discovered in the respiratory tract than in blood. The diversity of the total infectome was highly distinguished between respiratory and blood samples, and it was generally higher in patients that were SARS-CoV-2-positive, older in age, and with more severe disease. At the individual pathogen level, HSV-1 seemed to be the major contributor to these differences observed in the overall comparisons. Collectively, this study reveals the highly complex respiratory infectome and high-intensity coinfection in patients admitted to the hospital during the period of the 2023 COVID-19 pandemic in China. [ABSTRACT FROM AUTHOR]

Our findings shed light on existing theoretical investigations and the sustainable development of urban villages and the policy recommendations are proposed for the sustainable development of urban villages. It is widely acknowledged that public spaces in urban villages play an important role in residents' everyday lives, and the nighttime activities of most pedestrians are confined to the main streets in urban villages. Main streets are an important public space in urban villages at night and are a focus for comprehensive renovation in China. There has not been adequate research on main streets at nighttime, and such research is necessary to improve the quality of urban spaces and achieve high-quality development. This study used methods such as literature analysis, on-site research, and semi-structured interviews to understand the spatial characteristics of the main streets of urban villages at night and summarize the demands of pedestrians. This study used the Kano model to adjust the structure of the analytic hierarchy process (AHP) model to form the AHP–Kano model and construct an evaluation system based on the evaluation dimension of pedestrian needs. We conducted a questionnaire-based survey of pedestrians on the main streets of Pingshan Village and Nantou Ancient City in Shenzhen, and obtained the dominant categories of pedestrian demands and the results of satisfaction evaluations from the main street spaces of the two urban villages at night. According to on-site research, we analyzed the problems in the main street spaces of the two urban villages at night, and propose targeted renovation suggestions, which have clear value for improving everyday public spaces in urban villages at nighttime. [ABSTRACT FROM AUTHOR]

Influenza is prevalent globally, leading to severe morbidity and mortality. During the pandemic, knowledge, attitude, and practice (KAP) towards influenza virus and vaccination were less investigated among southern Chinese older adults. A cross-sectional study was conducted through the structured questionnaire among community healthcare centers in selected districts in Shenzhen, southern China from September to October 2021. KAP towards influenza virus and vaccination were analyzed. A multivariable logistic regression model was used to identify associated factors. Among 975 participants, 55.6% were reported to have received influenza vaccination ever, and 46.6% had taken influenza vaccination in 2020 during the pandemic. Only one-fifth of participants knew severe comorbidities happen among severe influenza cases. A total of 88.3% thought older adults should have influenza vaccination. COVID-19 vaccination history was associated with receiving influenza vaccination (OR 1.92, 95% CI 1.32–2.80). People with a high-level income had better KAP towards influenza virus and vaccination. COVID-19 vaccination history was associated with the positive actions of influenza vaccination during the pandemic. Efforts should be made to promote the free influenza vaccination program widely and launch health education events on influenza and its vaccination regularly to improve KAP among older adults. [ABSTRACT FROM AUTHOR]

Recently, carbon price forecasting has become critical for financial markets and environmental protection. Due to their dynamic, nonlinear, and high noise characteristics, predicting carbon prices is difficult. Machine learning forecasting often uses stacked ensemble algorithms. As a result, common stacking has many limitations when applied to time series data, as its cross-validation process disrupts the temporal sequentiality of the data. Using a double sliding window scheme, we proposed an improved stacking ensemble algorithm that avoided overfitting risks and maintained temporal sequentiality. We replaced cross-validation with walk-forward validation. Our empirical experiment involved the design of two dynamic forecasting frameworks utilizing the improved algorithm. This incorporated forecasting models from different domains as base learners. We used three popular machine learning models as the meta-model to integrate the predictions of each base learner, further narrowing the gap between the final predictions and the observations. The empirical part of this study used the return of carbon prices from the Shenzhen carbon market in China as the prediction target. This verified the enhanced accuracy of the modified stacking algorithm through the use of five statistical metrics and the model confidence set (MCS). Furthermore, we constructed a portfolio to examine the practical usefulness of the improved stacking algorithm. Empirical results showed that the improved stacking algorithm could significantly and robustly improve model prediction accuracy. Support vector machines (SVR) aggregated results better than the other two meta-models (Random forest and XGBoost) in the aggregation step. In different volatility states, the modified stacking algorithm performed differently. We also found that aggressive investment strategies can help investors achieve higher investment returns with carbon option assets. [ABSTRACT FROM AUTHOR]

Urban waterfront renewal, especially public space improvement, is important for regaining waterfront space vitality. However, existing studies constrained by sparse and hard-to-access data are hard to explore how changes in spatial elements during waterfront renewal would affect space vitality. Waterfront space vitality comprises social vitality represented by public behaviors and economic vitality represented by urban functional facilities. Taking the Maozhou River renewal project in China as an example, we collect spatial elements and vitality on corresponding periods in 2018 and 2020 (before and after the renewal construction) and use multiple linear regression models to assess the relationships. We find that the functional diversity (e.g., commercial and cultural facilities) and design quality (e.g., path density and the shoreline's proximity to the water) are the two most influential spatial elements affecting space vitality during waterfront renewal. Overall, the use of two-time datasets has generated strong evidence for measuring waterfront revitalization. [ABSTRACT FROM AUTHOR]

No research has been conducted to explore the variables associated with healthcare providers' (HCPs) knowledge and attitudes toward the human papillomavirus vaccine (HPV) since the vaccine was approved for free use in some Chinese cities. In Shenzhen, southern China, a convenience sample strategy was used to distribute questionnaires to HCPs involved in the government's HPV vaccination program from Shenzhen. There were 828 questionnaires collected in total, with 770 used in the analysis. The mean HPV and HPV vaccine knowledge score was 12.0 among HCPs involved in the government HPV vaccination program (with a total score of 15). the average scores for HPV and HPV vaccine knowledge varied among different types of medical institutions. District hospitals had the highest mean score of 12.4, while private hospitals ranked fourth with a mean score of 10.9. Multivariate logistic regression results revealed significant disparities in the type of license and after-tax annual income across HCPs (p < 0.05). The future education and training for HCPs should focus on private community health centers (CHCs), HCPs whose license type is other than a doctor, and HCPs with low after-tax annual income. [ABSTRACT FROM AUTHOR]

Fractal dimension curves of urban growth can be modeled with sigmoid functions, including logistic function and quadratic logistic function. Different types of logistic functions indicate different spatial dynamics. The fractal dimension curves of urban growth in Western countries follow the common logistic function, while the fractal dimension growth curves of cities in northern China follow the quadratic logistic function. Now, we want to investigate whether other Chinese cities, especially cities in South China, follow the same rules of urban evolution and attempt to analyze the reasons. This paper is devoted to exploring the fractals and fractal dimension properties of the city of Shenzhen in southern China. The urban region is divided into four subareas using ArcGIS technology, the box-counting method is adopted to extract spatial datasets, and the least squares regression method is employed to estimate fractal parameters. The results show that (1) the urban form of Shenzhen city has a clear fractal structure, but fractal dimension values of different subareas are different; (2) the fractal dimension growth curves of all the four study areas can only be modeled by the common logistic function, and the goodness of fit increases over time; (3) the peak of urban growth in Shenzhen had passed before 1986 and the fractal dimension growth is approaching its maximum capacity. It can be concluded that the urban form of Shenzhen bears characteristics of multifractals and the fractal structure has been becoming better, gradually, through self-organization, but its land resources are reaching the limits of growth. The fractal dimension curves of Shenzhen's urban growth are similar to those of European and American cities but differ from those of cities in northern China. This suggests that there are subtle different dynamic mechanisms of city development between northern and southern China. [ABSTRACT FROM AUTHOR]

China is considering to offer COVID-19 vaccination for children aged 6–35 months. This study investigated the changes in COVID-19 vaccine acceptability and associated factors among parents with children aged 6–35 months in 2020 and 2021. Two rounds of cross-sectional online surveys were conducted among adult factory workers in Shenzhen, China. A subset of 208 (first round) and 229 (second round) parents with at least one child aged 6–35 months was included in the study. Parental acceptability of COVID-19 vaccination increased significantly from 66.8% in the first round to 79.5% in the second round (p = 0.01). Positive attitudes, perceived subjective norm, and perceived behavioral control were associated with higher parental acceptability in both rounds of surveys (p values ranged from <0.001 to 0.003). A negative association of negative attitudes with parental acceptability was observed in the second round (p = 0.02). No significant associations of exposure to information related to COVID-19 vaccination on social media with parental acceptability was found in either round of survey. Expanding the existing COVID-19 vaccination programs to cover children aged 6–35 months is necessary in China. Future programs should focus on modifying perceptions among parents to promote COVID-19 vaccination for children in this age group. [ABSTRACT FROM AUTHOR]

Analyzing micro-level firm data from the Chinese manufacturing sector, this study provides compelling evidence that firms in emerging markets that invest in research and development (R&D) for product differentiation significantly increase firm performance as measured by market power, profitability, and earning quality. Privately held (non-state-owned), mid-size, Shenzhen exchange-listed firms experience the largest boost to firm performance when they invest in research and development. However, analyzing the contribution of R&D investment to firm market value, we reveal that while R&D investments are valued by institutional investors, the potential for investment in R&D to boost firm performance is not recognized by individual investors, who dominate Chinese financial markets. This finding suggests that managers may under-invest in R&D if equity compensation comprises a large share of the overall compensation package. [ABSTRACT FROM AUTHOR]

In this paper, we expand the value-added intellectual coefficient (VAIC) model by constructing a comprehensive financial capital (FC) component. Human capital efficiency is subdivided into executive (EHCE) and nonexecutive human capital efficiency (NHCE). We have sampled listed agriculture companies (LAC) in China's Shanghai and Shenzhen A-share markets from 2009 to 2018 and categorized them as high-tech (HTAC) and non-high-tech agriculture companies (NHTAC). We find that capital employed efficiency (CEE) and EHCE have a significant positive effect on corporate sustainable growth (CSG) of HTAC but no significant effect on CSG of NHTAC, while FC has a significant positive effect on both. These results suggest that companies, especially HTAC, should invest in human capital, and their executives and policymakers should develop effective knowledge management tools and begin accumulating the necessary intellectual capital to allow adaptation to their changing environment. In the spirit of the intellectual agriculture concept, we present some new ways to study the performance of agricultural companies using intellectual capital and offer suggestions that can help to modernize the industry. [ABSTRACT FROM AUTHOR]

Frequent outbreaks of marine disasters in the context of global warming pose a serious threat to the sustainable development of coastal areas and the construction of global maritime capitals. Implementing integrated marine and coastal management and assessing and enhancing cities' resilience to marine disasters are of practical importance. Based on the capital perspective, this study innovatively constructed a framework for the Coastal Marine Disaster Resilience Index (CMDRI) for the coastal city level, considering the main marine disaster characteristics of Chinese coastal areas. Eight coastal cities in China proposed to build global maritime capitals were used as research objects. The random forest model, which can handle complex nonlinear systems and feature importance, was applied for the first time to resilience assessment and key factor identification in marine disasters. The results show that the overall level of CMDRI of each city is steadily increasing, with Shenzhen having the highest marine disaster resilience grade for each year and Zhoushan having the lowest. Economic and human capitals accounted for a more significant proportion of key factors, followed by physical and social capitals, and environmental capital accounted for a minor proportion. The comparison results of model performance show that the random forest model has better fitting accuracy and stability in assessing CMDRI and can be further applied to other disaster resilience and sustainability areas. [ABSTRACT FROM AUTHOR]

The planning techniques and strategies for optimizing the urban wind and heat environment are important means for cities to adapt to climate change at the source. This study used Shenzhen, a sub-tropical megacity in southern China, as an example for evaluating the climate environment, heat island intensity, and urban form, and then for analyzing the relationships between them. The results revealed a high-quality climate area located southeast of Shenzhen that can provide a high wind speed and low temperature. Low-quality climate areas were located in the central and western regions and were less comfortable. The relationship between surface ventilation potential and urban form was analyzed using linear regression and the Pearson correlation coefficient, showing that there was a significant correlation between a surface urban heat island (SUHI) and building density (BD) as well as the sky view factor (SVF), and that there was also a correlation between the ventilation potential coefficient (VPC) and other factors, such as the surface's roughness length (RL) and building height (BH). The results showed that ventilation capacity deteriorated as BH and RL increased. An environmentally sensitive thermal area was identified from the surface urban heat island intensity, which was always in a strong heat island (SHI) or sub-strong heat island (SSHI) year-round. It was recommended that seven level one corridors and nine level two corridors be formed. Additionally, thermal and wind environment optimization strategies and protective suggestions were proposed for the city's overall development. [ABSTRACT FROM AUTHOR]

China started to implement COVID-19 vaccination programs for children in July 2021. This study investigated the changes in parents' COVID-19 vaccine hesitancy for children before and after the vaccination program rollout. Repeated cross-sectional online surveys among full-time adult factory workers were conducted in Shenzhen, China. This analysis was based on 844 (first round) and 1213 parents (second round) who had at least one child aged 3–17 years. The prevalence of vaccine hesitancy for children aged 3–11 years dropped from 25.9% (first round) to 17.4% (second round), while such a prevalence for children aged 12–17 years dropped from 26.0% (first round) to 3.5% (second round) (p < 0.001). Positive attitudes, a perceived subjective norm, and perceived behavioral control related to children's COVID-19 vaccination were associated with lower vaccine hesitancy in both rounds. In the second round and among parents with children aged 3–11 years, negative attitudes and exposure to information on SARS-CoV-2 infection after receiving a primary vaccine series were associated with higher vaccine hesitancy, while exposure to experiences shared by vaccine recipients and infectiousness of variants of concern were associated with lower vaccine hesitancy. Regular monitoring of vaccine hesitancy and its associated factors among parents should be conducted to guide health promotion. [ABSTRACT FROM AUTHOR]

An urban flood simulation model based on TELEMAC-2D was constructed, and the inundation data of two measured rainstorms (7 June 2018 and 16 September 2018) were selected to validate the model. Flooding processes were simulated under 12 designed rainfall scenarios with rainfall return periods of 20, 50 and 100 years and rainfall peak coefficients of 0.2, 0.4, 0.6 and 0.8, respectively. The hazard-vulnerability (H-V) method was used for urban flood risk assessment. The selected hazard factors included inundation depth, flood velocity, elevation and slope. The vulnerability factors included land use type, population density and property distribution. The analytic hierarchy process (AHP) method was used to calculate the weight values of each indicator factor, and ArcGIS software was used for overlay calculation. The results of the analysis show that as the rainfall peak coefficient factor increases, the area of each risk zone increases to varying degrees. The larger the rainfall peak coefficient factor, the more serious the flooding. As the rainfall return period increases, the effect of the rainfall peak coefficient factor of the change in the area of the highest risk zone diminishes. The highest risk zone is the largest within Luohu District (LHD), accounting for 46.38%, 60.92% and 45.54% of the total highest risk area, respectively. As the return period increases, the area of the highest risk zone within Futian District (FTD) increases, but its proportion has a decreasing trend, and the proportion of the highest risk area within Longgang District (LGD) has an increasing trend. The risk zoning map can better reflect the risk distribution of the basin and provide a scientific basis for early warning of flood prevention and drainage in the Shenzhen River basin. [ABSTRACT FROM AUTHOR]

In order to allocate the raw water of the complex water supply system in Shenzhen reasonably, this paper studied the complex network relationship of this large-scale urban water supply system, which consists of 46 reservoirs, 67 waterworks, 2 external diversion water sources, 14 pumping stations and 9 gates, and described each component of the system with the concepts of point, line and plane. Using the topological analysis technology and graph theory, a generalized model of the network topological structure of the urban water allocation system was established. On this basis, combined with the water demand prediction and allocation model of waterworks, a water resources allocation model was established, aiming at satisfying the guaranteed rate of the water supply. The decomposition and coordination principle of the large-scale system and the dynamic simulation technology of the supply-demand balance were adopted to solve the model. The forward calculation mode of controlling waterworks and pumps, and the reverse calculation mode of controlling reservoirs and waterworks were designed in solving the model, and a double-layer feedback mechanism was formed, which took the reverse calculation mode as outer feedback and the reservoir water level constraint or pipeline capacity constraint as inner feedback. Through the verification calculation of the case study, it was found that the proposed model can deal well with the raw water allocation of a large-scale complex water supply system, which had an important application value and a practical significance. [ABSTRACT FROM AUTHOR]

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields. [ABSTRACT FROM AUTHOR]

Station-free bike sharing systems (BSSs) are a new type of public bike system that has been widely deployed in China since 2017. However, rapid growth has vastly outpaced the immediate demand and overwhelmed many cities around the world. This paper proposes a heuristic bike optimization algorithm (HBOA) to determine the optimal supply and distribution of bikes considering the effect of bicycle cycling. In this approach, the different bike trips with separate bikes can be connected in space and time and converted into a continuous trip chain for a single bike. To improve this cycling efficiency, it is important to properly design the bicycle distribution. Taking Shenzhen as an example, we implement the algorithm with OD matrix data from Mobike and Ofo, the two large bike sharing companies which account for 80% of the shared bike market in Shenzhen, over two days. The HBOA results are as follows. 1) Only one-fifth of the bike supply is needed to meet the current usage demand if the bikes are used efficiently, which means a large number of shared bikes in Shenzhen remain in an idle state for long periods. 2) Although the cycling demand is high in many areas, it does not mean that large numbers of bikes are needed because the continuous inflow caused by the cycling effect of bikes will meet most of the demand by itself. 3) The areas with the highest demands for optimal bikes are residential, followed by industrial, public transportation, official and commercial areas, on both working and non-working days. This algorithm can be an objective basis for city related departments to manage station-free BSSs and be applied to design the layout of bikes in small-scale spatial units to help station-free BSSs operate efficiently and minimize the need to relocate the bikes without reducing the level of user satisfaction. [ABSTRACT FROM AUTHOR]

Debate on the shift from a monocentric to polycentric urban structure has been extensive. Polycentricity generally refers to the co-existence of several centers in a city. Existing studies regarding China have mainly focused on the morphological characteristics of urban centers, but few recent studies have focused on functional dimensions of urban centers. Emerging big data sources provide new opportunities to explore the morphological and functional perspectives of urban spatial structure. This study uses mobile phone signaling data and develops a new methodology to measure urban centers' functional centrality. The study area focuses on Shenzhen City, which has rapidly transformed from a village into a metropolitan city in the past few decades. As the first economic special zone in China, Shenzhen has adopted a polycentric urban plan since the beginning of the urbanization process. This study explores the spatial employment structure of the city from the morphological and function dimensions. Based on the findings, this study discusses the role of urban planning in forming an urban spatial structure and provides implications for future planning. [ABSTRACT FROM AUTHOR]

This study explored how corporate social responsibility (CSR) risk, social networks, and firm performance interacted in light of resource dependence theory and information asymmetry theory to bridge the literature gap between CSR risk and firm performance under the conditions of China's network. We used data from Shanghai and Shenzhen A-share listed firms in China from 2010 to 2019 to conduct a social network analysis and random-effects GLS regression analysis. The study revealed the following: (1) CSR risk hurts financial performance, while structural holes and network density attenuate this effect; (2) CSR risk positively impacts capital performance, which is amplified by closeness centrality; (3) CSR risk harms innovation performance, while betweenness centrality and network density mitigate this effect. Despite CSR risk bringing short-term benefits, this effect is not sustained. Generally, CSR risks are more detrimental to firms than beneficial. In this study, we strengthen the basis of the research on CSR risk and firm performance, along with research on social networks, advising firms to avoid CSR risks and utilize their networks to mitigate such risks and achieve a better performance. [ABSTRACT FROM AUTHOR]

Reliable and fine-resolution electric power consumption (EPC) is essential for effective urban electricity allocation and planning. Currently, EPC data exists mainly as statistics with low resolution. Many studies estimate fine-resolution EPC based on the positive correction between stable nighttime light and EPC distribution. However, EPC is related to various factors other than nighttime light and is spatially non-stationary. Yet this has been ignored in current research. This study developed a novel method to estimate EPC at 500 m resolution by considering spatially non-stationary through fusing geospatial data and high-resolution satellite images. Deep transfer learning and statistical methods were used to extract socio-economic, population density, and landscape features to describe EPC distribution from multi-source geospatial data. Finally, a random forest regression (RFR) model with features and EPC statistics is established to estimate fine-resolution EPC. A study area of Shenzhen city, China, is employed to evaluate the proposed method. The R 2 between predicted EPC and statistical EPC is 0.82 at sub-district level in 2013, which is higher than an existing EPC product (Shi's product) with R 2 = 0.46 , illustrating the effectiveness of the proposed method. Moreover, the EPC distribution for Shenzhen from 2013 to 2019 was estimated. Furthermore, the spatiotemporal dynamic of EPC was analyzed at the pixel and sub-district levels. [ABSTRACT FROM AUTHOR]

Analysis of sea-level observations demonstrates that Typhoons Mawar (2017) and Mangkhut (2018) induced seiches in both Dapeng Bay and Daya Bay near Shenzhen of China, with periods varying from about 3.5 to 4.0 h. Typhoon Mawar (2017) also generated seiches with a period of about 1.2 h. Seiches with such periods in the two bays have not been reported in the past. In this study, we investigate the main processes affecting seiches over these coastal waters using a nested-grid ocean circulation modeling system. The modelled results of typhoon-induced seiches agree well with observations, which indicates that the seiches after the passage of typhoons are dynamically free waves generated by the storm-induced accumulation of water bodies in the two bays. Model sensitivity experiments show that wind directions have an important influence on the type and characteristics of seiches. When the wind stress causes the water body to accumulate in a cross-bay direction, seiches in a closed water body are generated. When the wind stress causes the water body to accumulate in an along-bay direction, seiches in a semi-closed water body are produced. Because of the irregularity of the bathymetry and coastline and variability of wind directions, these two types of seiches can exist simultaneously in the two bays. [ABSTRACT FROM AUTHOR]

Financial derivatives have been increasingly used by firms to hedge against financial risks. However, it is still not clear what factors at the firm level lead to firms' derivative use and whether derivative use can generate performance improvement, especially in the context of firms operating in emerging economies. Using the unbalanced panel data consisting of 2529 listed firms from China covering an 11-year period from 2005 to 2015, this study examines these two questions regarding firms' use of financial derivatives. Based on results from the empirical analysis, this study identified operational cash flow, tax shield, R&D investment, and the possibility of bankruptcy, as the firm-level factors that enable firms' decision to invest in financial derivatives. More importantly, empirical findings from this study suggest that a firm's derivative use tends to negatively affect firm performance, rather than improve firm performance. The negative effect of derivative use on firm performance is not consistent between the two groups of the better performer and poorer performer firms. While the poorly performed firms are more likely to use financial derivatives for the purpose of performance improvement, their derivative use tends to further damage, rather than improve, performance. These research findings have theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Shenzhen, the first special economic zone of China, has witnessed earth-shaking changes since the late 1980s. In the past 35 years, about 80 km2 of land has been reclaimed from the sea in Shenzhen. In order to investigate coastal vertical land motions associated with land reclamation, we proposed an elaborated Point Target (PT) based Small Baseline Subset InSAR (SBAS-InSAR) strategy to process an ENVISAT ASAR ascending and descending orbits dataset both acquired from 2007 to 2010. This new strategy can not only select high density PTs but also generate a reliable InSAR measurement with full spatial resolution. The inter-comparison between InSAR-derived deformation velocities from different orbits shows a good self-consistency of the results extracted by the elaborated PT-based SBAS-InSAR strategy. The InSAR measurements show that the reclaimed land is undergoing remarkable coastal subsidence (up to 25 mm/year), especially at the Shenzhen Airport, Baóan Center, Qianhai Bay and Shenzhen Bay. By analyzing the results together with land reclamation evolution, we conclude that the ground deformation is expected to continue in the near future, which will amplify the regional sea level rise. [ABSTRACT FROM AUTHOR]

The 16th Universiade, an international multi-sport event, was hosted in Shenzhen, China from 12 to 23 August 2011. During this time, officials instituted the Pearl River Delta action plan in order to enhance the air quality of Shenzhen. To determine the effect of these controls, the current study examined the trace elements, water-soluble ions, and stable lead isotopic ratios in atmospheric particulate matter (PM) collected during the controlled (when the restrictions were in place) and uncontrolled periods. Fine particles (PM2.5) were collected at two sampling sites in Shenzhen: "LG"--a residential building in the Longgang District, with significant point sources around it and "PU"--Peking University Shenzhen Graduate School in the Nanshan District, with no significant point sources. Results from this study showed a significant increase in the concentrations of elements during the uncontrolled periods. For instance, samples at the LG site showed (controlled to uncontrolled periods) concentrations (in ng·m-3) of: Fe (152 to 290), As (3.65 to 8.38), Pb (9.52 to 70.8), and Zn (98.6 to 286). Similarly, samples at the PU site showed elemental concentrations (in ng·m-3) of: Fe (114 to 301), As (0.634 to 8.36), Pb (4.86 to 58.1), and Zn (29.5 to 259). Soluble Fe ranged from 7%-15% for the total measured Fe, indicating an urban source of Fe. Ambient PM2.5 collected at the PU site has an average 206Pb/204Pb ratio of 18.257 and 18.260 during controlled and uncontrolled periods, respectively. The LG site has an average 206Pb/204Pb ratio of 18.183 and 18.030 during controlled and uncontrolled periods, respectively. The 206Pb/204Pb ratios at the PU and the LG sites during the controlled and uncontrolled periods were similar, indicating a common Pb source. To characterize the sources of trace elements, principal component analysis was applied to the elements and ions. Although the relative importance of each component varied, the major sources for both sites were identified as residual oil combustion, secondary inorganic aerosols, sea spray, and combustion. The PM2.5 levels were severely decreased during the controlled period, but it is unclear if this was a result of the controls or change in meteorology. [ABSTRACT FROM AUTHOR]

The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies. [ABSTRACT FROM AUTHOR]

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) cause environmental problems worldwide. Continuous monitoring and forecasting of harmful algal blooms are necessary for marine resources managers to detect the intensity and spatial extent of HABs and provide early warnings to the public. In this study, we introduce an integrated web-based system for the monitoring and forecasting of coastal HABs. The system is named the Harmful Algal Blooms Monitoring and Forecasting System (HMFS). HMFS integrates in situ observations, a remote-sensing-based model, hydrodynamic and water quality model and Web-Based Geographic Information System (GIS) techniques into one environment. The in situ sensors and remote sensing model provide automatic and continuous monitoring of the coastal water conditions. The numerical models provide short-term prediction and early warning of HAB of up to 5 days. The overall forecast accuracy is more than or equal to 50% for the major coastal areas of Shenzhen in 2018. By leveraging a web-based GIS technique and Service-Oriented Architecture (SOA), the web portal of HMFS provides a graphic interface for users and mangers to view real-time in situ measurements and remote sensing maps, explore numerical model forecasts and get early warning information. HMFS was applied to Shenzhen, which is a rising megacity in Southern China. The application study demonstrated the applicability and effectiveness of HMFS for monitoring and predicting HABs. [ABSTRACT FROM AUTHOR]

In recent years, the enormous losses caused by urban surface deformation have received more and more attention. Traditional geodetic techniques are point-based measurements, which have limitations in using traditional geodetic techniques to detect and monitor in areas where geological disasters occur. Therefore, we chose Interferometric Synthetic Aperture Radar (InSAR) technology to study the surface deformation in urban areas. In this research, we discovered the land subsidence phenomenon using InSAR and Global Navigation Satellite System (GNSS) technology. Two different kinds of time-series InSAR (TS-InSAR) methods: Small BAseline Subset (SBAS) and the Permanent Scatterer InSAR (PSI) process were executed on a dataset with 31 Sentinel-1A Synthetic Aperture Radar (SAR) images. We generated the surface deformation field of Shenzhen, China and Hong Kong Special Administrative Region (HKSAR). The time series of the 3d variation of the reference station network located in the HKSAR was generated at the same time. We compare the characteristics and advantages of PSI, SBAS, and GNSS in the study area. We mainly focus on the variety along the coastline area. From the results generated by SBAS and PSI techniques, we discovered the occurrence of significant subsidence phenomenon in the land reclamation area, especially in the metro construction area and the buildings with a shallow foundation located in the land reclamation area. [ABSTRACT FROM AUTHOR]

This study describes the integration and analysis of travel smart card data (SCD) with points of interest (POIs) from social media for a case study in Shenzhen, China. SCD ticket price with tap-in and tap-out times was used to identify different groups of travellers. The study examines the temporal variations in mobility, identifies different groups of users and characterises their trip purpose and identifies sub-groups of users with different travel patterns. Different groups were identified based on their travel times and trip costs. The trip purpose associated with different groups was evaluated by constructing zones around metro station locations and identifying the POIs in each zone. Each POI was allocated to one of six land use types, and each zone was allocated a set of land use weights based on the number of POI check-ins for the POIs in that zone. Trip purpose was then inferred from trip time linked to the land use at the origin and destination zones using a novel "land use change rate" measure. A cluster analysis was used to identify sub-groups of users based on individual temporal travel patterns, which were used to generate a novel "boarding time profile". The results show how different groups of users can be identified and the differences in trip times and trip purpose quantified between and within groups. Limitations of the study are discussed and a number of areas for further work identified, including linking to socioeconomic data and a deeper consideration of the timestamps of POI check-ins to support the inference of dynamic and multiple land uses at one location. The methods and metrics developed by this research use social media POI data to semantically contextualise information derived from the SCD and to overcome the drawbacks and limitations of traditional travel survey data. They are novel and generalizable to other studies. They quantify spatiotemporal mobility patterns for different groups of travellers and infer how their purposes of their journeys change through the day. In so doing, they support a more nuanced and detailed view of who, where, when and why people use city spaces. [ABSTRACT FROM AUTHOR]