Triumph of the Bike Sharing: Understanding Spatiotemporal Patterns of Dock-less Shared Bicycles in Shenzhen

As an innovative transport alternative for short-distance trips, dock-less bike share has swept across the globe since 2016. Its great convenience, flexibility, low fare and health benefits attract urban dwellers' interests. For cities, it also created great economic value, environmental benefits and close integration with existing public transit system. With dozens of bike-share companies quickly flooding city streets with millions of brightly colored shared bicycles, China is experiencing a bike share boom since 2016. In this study, I take Shenzhen as a research example to explore how bike sharing usage pattern is affected by different factors. The results reveal that there are five significant hot spots of bike share usage in Shenzhen, presenting a distinct layer structure. In addition, the results also indicate population, employment, restaurants, companies, schools, urban road network, metro station and bike availability are the most significant influencing factors. Various stakeholders can take advantage of the analysis. For bike share operation companies, they should prioritize the integration of bike share and metro system rather than bus system. In addition, it is important to deploy specialized team to work on the oversupply and under-supply of bikes during peak hours. For government, the real-time data generated by bike share usage could assist decision-makers to deploy the bike-related infrastructure, even the transport infrastructure in a more effective way. In short, there is an interesting and complicated interaction between bike sharing activities and our urban environment. We are looking forward to seeing bike sharing to bring more amazing changes and surprises to our cities!Master of City and Regional Plannin

Revealing social dimensions of urban mobility with big data: A timely dialogue

Considered a total social phenomenon, mobility is at the center of intricate social dynamics in cities and serves as a reading lens to understand the whole society. With the advent of big data, the potential for using mobility as a key social analyzer was unleashed in the past decade. The purpose of this research is to systematically review the evolution of big data's role in revealing social dimensions of urban mobility and discuss how they have contributed to various research domains from early 2010s to now. Six major research topics are detected from the selected online academic corpuses by conducting keywords-driven topic modeling techniques, reflecting diverse research interests in networked mobilities, human dynamics in spaces, event modeling, spatial underpinnings, travel behaviors and mobility patterns, and sociodemographic heterogeneity. The six topics reveal a comprehensive, research-interests, evolution pattern, and present current trends on using big data to uncover social dimensions of human mobility activities. Given these observations, we contend that big data has two contributions to revealing social dimensions of urban mobility: as an efficiency advancement and as an equity lens. Furthermore, the possible limitations and potential opportunities of big data applications in the existing scholarship are discussed. The review is intended to serve as a timely retrospective of societal-focused mobility studies, as well as a starting point for various stakeholders to collectively contribute to a desirable future in terms of mobility

Revealing social dimensions of urban mobility with big data: A timely dialogue

Considered a total social phenomenon, mobility is at the center of intricate social dynamics in cities and serves as a reading lens to understand the whole society. With the advent of big data, the potential for using mobility as a key social analyzer was unleashed in the past decade. The purpose of this research is to systematically review the evolution of big data's role in revealing social dimensions of urban mobility and discuss how they have contributed to various research domains from early 2010s to now. Six major research topics are detected from the selected online academic corpuses by conducting keywords-driven topic modeling techniques, reflecting diverse research interests in networked mobilities, human dynamics in spaces, event modeling, spatial underpinnings, travel behaviors and mobility patterns, and sociodemographic heterogeneity. The six topics reveal a comprehensive, research-interests, evolution pattern, and present current trends on using big data to uncover social dimensions of human mobility activities. Given these observations, we contend that big data has two contributions to revealing social dimensions of urban mobility: as an efficiency advancement and as an equity lens. Furthermore, the possible limitations and potential opportunities of big data applications in the existing scholarship are discussed. The review is intended to serve as a timely retrospective of societal-focused mobility studies, as well as a starting point for various stakeholders to collectively contribute to a desirable future in terms of mobility

In Situ Kinase Profiling Reveals Functionally Relevant Properties of Native Kinases

SummaryProtein kinases are intensely studied mediators of cellular signaling, yet important questions remain regarding their regulation and in vivo properties. Here, we use a probe-based chemoprotemics platform to profile several well studied kinase inhibitors against >200 kinases in native cell proteomes and reveal biological targets for some of these inhibitors. Several striking differences were identified between native and recombinant kinase inhibitory profiles, in particular, for the Raf kinases. The native kinase binding profiles presented here closely mirror the cellular activity of these inhibitors, even when the inhibition profiles differ dramatically from recombinant assay results. Additionally, Raf activation events could be detected on live cell treatment with inhibitors. These studies highlight the complexities of protein kinase behavior in the cellular context and demonstrate that profiling with only recombinant/purified enzymes can be misleading

The maximum matching extendability and factor-criticality of 1-planar graphs

A graph is $1$-$planar$ if it can be drawn in the plane so that each edge is crossed by at most one other edge. Moreover, a 1-planar graph $G$ is $optimal$ if it satisfies $|E(G)|=4|V(G)|-8$. J. Fujisawa et al. [16] first considered matching extension of optimal 1-planar graphs, obtained that each optimal 1-planar graph of even order is 1-extendable and characterized 2-extendable optimal 1-planar graphs and 3-matchings extendable to perfect matchings as well. In this short paper, we prove that no optimal $1$-planar graph is 3-extendable. Further we mainly obtain that no 1-planar graph is 5-extendable by the discharge method and also construct a 4-extendable 1-planar graph. Finally we get that no 1-planar graph is 7-factor-critical and no optimal 1-planar graph is 6-factor-critical.Comment: 13 pages, 8 figure

Visualizing Hong Kong’s mass transit usage under COVID-19

The COVID-19 pandemic hit the world hard. It has induced many abrupt changes, including lockdowns and business closures. Using changes in local public transit usage as proxies of mobility patterns in a typical high-density city where public transit carries ≥ 90% of passenger trips, this featured graphic geovisualizes to what degree human mobility patterns in Hong Kong were affected by the pandemic and related local and remote events such as the Wuhan lockdown and the Hong Kong government’s ‘work from home’ policy, where the patterns are affected the most, and possibly by/after which event(s). The visuals show that trips to and from business and commercial districts, low- to middle-income new towns, and station areas near the checking point serving cross-border visitors/residents might be most affected

Mode Choice of Commuter Students in a College Town: An Exploratory Study from the United States

Research of travel behaviors of university students is of theoretical and empirical importance. The literature, however, has paid little attention to mode choice of students at college towns. This study aims to specifically explore influence factors of the mode choice of college town students. After conducting a survey of commuter students at Iowa State University, a college-town university in the United States, the study uses both simple statistics and advanced statistical models (e.g., multinomial logit and nested logit models) to analyze the data and produces findings to confirm and test existing knowledge and to gain new insights. Firstly, students at a college town are more likely to adopt greener (non-driving-alone) modes, especially walking, to commute compared to their counterparts at urban universities; this is as revealed in the literature. Secondly, students may use “bundled services” to fulfill their travel needs. The students who prioritized rent affordability in housing choice tend to live in proximity to bus stops and are more likely to ride buses. Lastly, commuter students who do not drive alone to school tend to prefer a residence with transit proximity. Moreover, students who reside in proximity to transit and who reported “peer effects” would use non-driving modes more if commute time was shortened

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people\u27s increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station\u27s elasticity as the ratio of changes in its average trip length to the COVID-19 cases\u27 footprints around that station. We regard those footprints as a proxy for people\u27s perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations\u27 elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations\u27 elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19

Practical, regulatory and clinical considerations for development of inhalation drug products

The formulation and device collectively constitute an inhalation drug product. Development of inhaled drugs must consider the compatibility between formulation and device in order to achieve the intended pharmaceutical performance and usability of the product to improve patient compliance with treatment instruction. From the points of formulation, device and patient use, this article summarizes the inhalation drugs, including pressurized metered dose inhaler (pMDI), dry powder inhaler (DPI), and nebulizer that are currently available in the US and UK markets. It also discusses the practical considerations for the development of inhalers and provides an update on the corresponding regulations of the FDA (U.S. Food and Drug Administration) and the EMA (European Medicines Agency)