Health impacts of bike sharing system – A case study of Shanghai

Background: Bike sharing systems have been promoted in many countries. Bike sharing can alleviate urban air pollution and reduce road congestion during peak hours in the morning and evening. In addition, using shared bicycles as a daily commuting tool can help users increase their daily exercise volume. This study evaluates the health effects of shared bicycle use. The evaluation of health is prospective, and we utilize current data to evaluate and analyze the health of future users. The primary health considerations for users include physical activity, PM2.5 levels, and collision rates. Physical exercise might be hindered by high concentrations of PM2.5. Thus, while riding in conditions of very high PM2.5 concentration, the pollutants taken by the traveler will hurt the body and counteract the advantages of physical exercise. This research demonstrates that cycling during periods of low or moderate PM2.5 concentrations should lead to an overall reduction premature mortality. / Data and methods: We perform a health assessment study to quantify the health risks and benefits of car trip substitution by bike trip. We collected the cycling data from Mobike shared bicycles operator in Shanghai established in August 2016. From August 1st to August 31st, 2018, there were 1,023,603 orders and 3,036,936 cycling users. During the computational analysis, we examined three factors: physical activity, PM2.5 pollution, and bicycle collision rate, and then summed the results to determine the cyclist's risk of early death. Three scenarios are created to estimate the annual expected number of deaths (increasing or reduced) due to physical activity, road traffic fatalities, and air pollution. / Results: Air pollution exposure was assessed using variations in the background fine particulate matter (PM2.5) concentration, which was 45 μg/m3 on average in August 2016 in Shanghai. Cycling under these settings, the advantages of physical exercise exceeded the hazards posed by pollution. When PM2.5 concentrations exceed 45 μg/m3, seven to eight people will avert early mortality for every 306,936 users. It means 23–26 per million cyclists would avoid premature death. When PM2.5 concentrations exceed 68 μg/m3, 1 to 2 people will be significantly harmed by air pollution and 4–7 out of every million cyclists are negatively affected by high PM2.5 concentrations. / Conclusions: These results demonstrate that shared cycling can avoid premature mortality. In addition, from the perspective of urban pollution, commuters choosing bicycles instead of cars to travel can reduce urban air pollution, improve air quality, and reduce traffic jams in the morning and evening peaks. Further research on the co-benefits of shared bicycles would be helpful to planners

Using a Linear Regression Approach to Sequential Interindustry Model for Time-Lagged Economic Impact Analysis

The input-output (IO) model is a powerful economic tool with many extended applications. However, one of the widely criticized drawbacks is its rather lengthy time lag in data preparation, making it impossible to apply IO in high-resolution time-series analysis. The conventional IO model is thus unfortunately unsuited for time-series analysis. In this study, we present an innovative algorithm that integrates linear regression techniques into a derivative of the IO method, the Sequential Interindustry Model (SIM), to overcome the inherent shortcomings of statistical lags in conventional IO studies. The regressed relationship can thus be used to predict, in the short term, the accumulated chronological impacts induced by fluctuations in sectorial economic demands under disequilibrium conditions. A simulated calculation is presented to serve as an illustration and verification of the new method. In the future, this application can be extended beyond economic studies to broader problems of system analysis

Tracing the Uncertain Chinese Mercury Footprint within the Global Supply Chain Using a Stochastic, Nested Input-Output Model

A detailed understanding of the mercury footprint at subnational entity levels can facilitate the implementation of the "Minamata Convention on Mercury", especially for China, the largest mercury emitter worldwide. Some provinces of China have more than 100 million people, with economic activities and energy consumption levels comparable to those of smaller G7 countries. We constructed a stochastic, nested multiregion input-output (MRIO) model, which regionalized the China block in the EXIOBASE global-scale MRIO table, to model the mercury footprint associated with global supply chains spanning China's regions and other countries. The results show that Tianjin, Shanghai, and Ningxia had the highest per capita mercury footprint in China, which was comparable to the footprint of Australia and Norway and exceeded the footprint of most other countries. Some developed regions in China (e.g., Guangdong, Jiangsu) had higher mercury final product-based inventories (FBI) and consumption-based inventories (CBI) than production-based inventories (PBI), emphasizing the role of these regions as centers of both consumption and economic control. Uncertainties of Chinese provincial mercury footprint varied from 8% to 34%. Our research also revealed that international and inter-regional final product and intermediate product trades reshape the mercury emissions of Chinese provinces and other countries to a certain extent

The Polarizing Trend of Regional CO2 Emissions in China and Its Implications

CO2 emissions are unevenly distributed both globally and regionally within nation-states. Given China's entrance into the new stage of economic development, an updated study on the largest CO2 emitter's domestic emission distribution is needed for effective and coordinated global CO2 mitigation planning. We discovered that domestic CO2 emissions in China are increasingly polarized for the 2007-2017 period. Specifically, the domestically exported CO2 emissions from the less developed and more polluting northwest region to the rest of China has drastically increased from 165 Mt in 2007 to 230 Mt in 2017. We attribute the polarizing trend to the simultaneous industrial upgrading of all regions and the persistent disparity in the development and emission decoupling of China's regions. We also noted that CO2 emissions exported from China to the rest of the world has decreased by 41% from 2007 to 2017, with other developing countries filling up the vacancy. As this trend is set to intensify, we intend to send an alarm message to policy makers to devise and initiate actions and avoid the continuation of pollution migration

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Code-Aware Cross-Program Transfer Hyperparameter Optimization

Hyperparameter tuning is an essential task in automatic machine learning and big data management. To accelerate tuning, many recent studies focus on augmenting BO, the primary hyperparameter tuning strategy, by transferring information from other tuning tasks. However, existing studies ignore program similarities in their transfer mechanism, thus they are sub-optimal in cross-program transfer when tuning tasks involve different programs. This paper proposes CaTHPO, a code-aware cross-program transfer hyperparameter optimization framework, which makes three improvements. (1) It learns code-aware program representation in a self-supervised manner to give an off-the-shelf estimate of program similarities. (2) It adjusts the surrogate and AF in BO based on program similarities, thus the hyperparameter search is guided by accumulated information across similar programs. (3) It presents a safe controller to dynamically prune undesirable sample points based on tuning experiences of similar programs. Extensive experiments on tuning various recommendation models and Spark applications have demonstrated that CatHPO can steadily obtain better and more robust hyperparameter performances within fewer samples than state-of-the-art competitors

Edge Restoration of a 3D Building Model Based on Oblique Photography

Unmanned aerial vehicle (UAV) oblique photography technology is widely used in a variety of fields because of its excellent efficiency, realism, and low cost of manufacturing. However, due to the influence of lighting, occlusion, weak textures, and other factors in aerial images, the modeling results can have the problem of an incorrect structure that is inconsistent with the real scene. The edge line of a building is the main external expression of its structure. Whether the edge line is straight or not will directly affect the realism of the building, so the restoration of the edge line can improve the realism of the building. In this study, we proposed and developed a method for the restoration of the edge line of a 3D building model based on triangular mesh cutting. Firstly, the feature line of the edge line was drawn using human–computer interaction, and axis-aligned bounding box (AABB) collision detection was carried out around the feature line to determine the triangular patches to be cut. Then, the triangular cutting algorithm was used to cut the triangular patches projected onto the plane. Finally, the structure and texture of the 3D building model were reconstructed. This method allowed us to actualize the physical separation of continuous triangulation; the triangulation around the edge line was cut, and the plane was fitted. This method was able to improve cutting accuracy and edge flatness and enhance the edge features of buildings and the rendering quality of models. The experimental results showed that the edge restoration method proposed in this paper is reliable and that it can effectively improve the building rendering effect of a 3D building model based on UAV oblique photography and can also enhance the realism of the model

Policy and newly confirmed cases universally shape the human mobility during COVID-19

Understanding how human mobility pattern changes during the COVID-19 is of great importance in controlling the transmission of the pandemic. This pattern seems unpredictable due to the complex social contexts, individual behaviors, and limited data. We analyze the human mobility data of over 10 million smart devices in three major cities in China from January 2020 to March 2021. We find that the human mobility across multi-waves of epidemics presents a surprisingly similar pattern in these three cities, despite their significant gaps in geographic environments and epidemic intensities. In particular, we reveal that the COVID-19 policies and statistics (i.e., confirmed cases) dominate human mobility during the pandemic. Thus, we propose a universal conditional generative adversarial network based framework to estimate human mobility, integrating COVID-19 statistics and policies via a gating fusion module. Extensive numerical experiments demonstrate that our model is generalizable for estimating human mobility dynamics accurately across three cities with multi-waves of COVID-19. Beyond, our model also allows policymakers to better evaluate the potential influences of various policies on human mobility and mitigate the unprecedented and disruptive pandemic

Adapting to new policy environment – past pattern and future trend in us-sino waste plastic trade flow

<p>Plastics are one of the most used materials in human activities, where consumer consumption and industrial production together has imposed vast rise in demand for this material in last century. While plastic is ideally derived from crude oil as a primary source from manufacturers’ perspective, varying crude oil prices are driving manufacturers economically to seek for alternative sources for plastics production. Waste plastic recovered from obsolete consumer products thus becomes an economic substitution for virgin plastics, which is further intensified with the possibility of international waste plastic trading. This study focuses on waste plastic trade between the US and mainland China by performing a correlation analysis of trade data. It is suggested in this study that although waste plastics are traded from the US to mainland China in general, as many of us believes, the route is gradually shifting in the past years. With tightening Chinese customs regulations, waste plastic from the US now tends to take a transit in a third destination (Hong Kong SAR for instance) for preliminary treatment to bypass Chinese customs inspection. Such phenomenon is worth noting, as a complication in waste plastic trading route hinders waste plastic transboundary movement monitoring. Furthermore, it will have adverse consequent consumer, industrial, and environmental impacts. It is thus necessary for national competent authorities to strengthen cooperative study and communication capacity in the future as a response to the changing waste plastic trade pattern.</p

Policy and newly confirmed cases universally shape the human mobility during COVID-19

Understanding how human mobility pattern changes during the COVID-19 is of great importance in controlling the transmission of the pandemic. This pattern seems unpredictable due to the complex social contexts, individual behaviors, and limited data. We analyze the human mobility data of over 10 million smart devices in three major cities in China from January 2020 to March 2021. We find that the human mobility across multi-waves of epidemics presents a surprisingly similar pattern in these three cities, despite their significant gaps in geographic environments and epidemic intensities. In particular, we reveal that the COVID-19 policies and statistics (i.e., confirmed cases) dominate human mobility during the pandemic. Thus, we propose a universal conditional generative adversarial network based framework to estimate human mobility, integrating COVID-19 statistics and policies via a gating fusion module. Extensive numerical experiments demonstrate that our model is generalizable for estimating human mobility dynamics accurately across three cities with multi-waves of COVID-19. Beyond, our model also allows policymakers to better evaluate the potential influences of various policies on human mobility and mitigate the unprecedented and disruptive pandemic