Delayed effects of air pollution on public bike-sharing system use in Seoul, South Korea: A time series analysis.

BACKGROUND: As a complementary means to urban public transit systems, public bike-sharing provides a green and active mode of sustainable mobility, while reducing carbon-dioxide emissions and promoting health. There has been increasing interest in factors affecting bike-sharing usage, but little is known about the effect of ambient air pollution. METHOD: To assess the short-term impact of daily exposure to multiple air pollutants (PM2.5, PM10, NO2, and O3) on the public bike-sharing system (PBS) usage in Seoul, South Korea (2018-2021), we applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM). The model was adjusted for day of the week, holiday, temperature, relative humidity, and long-term trend. We also conducted stratification analyses to examine the potential effect modification by age group, seasonality, and COVID-19. RESULTS: We found that there was a negative association between daily ambient air pollution and the PBS usage level at a single lag day 1 (i.e., air quality a day before the event) across all four pollutants. Our results suggest that days with high levels of air pollutants (at 95th percentile) are associated with a 0.91% (0.86% to 0.96%) for PM2.5, 0.89% (0.85% to 0.94%) for PM10, 0.87% (0.82% to 0.91%) for O3, and 0.92% (0.87% to 0.98%) for NO2, reduction in cycling behavior in the next day compared to days with low levels of pollutants (at 25th percentile). No evidence of effect modification was found by seasonality, age nor the COVID-19 pandemic for any of the four pollutants. CONCLUSIONS: Our findings suggest that high concentrations of ambient air pollution are associated with decreased rates of PBS usage on the subsequent day regardless of the type of air pollutant measured.

A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation.

Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial-Temporal Graph Convolutional Recurrent Network (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the accuracy of transportation flow estimation. Extensive experiments on two real-world datasets of transportation flow, New York bike-sharing system and Hangzhou metro system, prove the effectiveness of the proposed model. Compared to the current state-of-the-art baselines, it decreases the estimation error by 98% in the metro system and 63% in the bike-sharing system.

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study.

The COVID-19 pandemic has remarkably impacted urban mobility. All non-essential movements were restricted in Valencia (Spain) to contain the virus. Thus, the transport usage patterns of Valencia's bike-sharing system (BSS) users changed during this emergency situation. The primary objective of this study was to analyse the behaviour patterns of BSS users in Valencia before and after the COVID-19 outbreak, specifically those who maintained or changed their transport routines. A within-subjects comparison design was developed using a group of BSS users before and after the onset of the pandemic. Data mining techniques were used on a sample of 4355 regular users and 25 variables were calculated to classify users by self-organising maps analysis. The results show a significant reduction (40 %) in BSS movements after the outbreak during the entire post-outbreak year. There was some recovery during the rest of 2020; however, this has yet to reach the pre-pandemic levels, with variations observed based on the activities performed in different areas of the city. Of the users, 63 % changed their BSS use patterns after the onset of the pandemic (LEAVE group), while 37 % maintained their patterns (REMAIN group). The user profile of the REMAIN group was characterised by a general reduction of approximately 35 % of journeys during 2020, with a slight increase in morning movements compared to those made in the evening. These users also presented an equivalent number of cycling days to those of the previous year, reduced the number of connections and increased the network's density and the travelling speed. These results can be useful in estimating the percentage of people who do not vary their usual behaviour during emergencies. Finally, several policy implications are outlined based on the findings.

Enhancing consumption of sustainable transportation: Determinants and outcome of consumer engagement with bike-sharing service.

Bike-sharing service has become a popular sustainable means of transportation due to its direct impact on traffic congestion, energy consumption, the environment, and people's quality of life. Existing literature suggests that sustainable consumption can be promoted by engaging consumers with green products. This study examined drivers and the outcome of consumer engagement with bike-sharing services based on the technology acceptance model (TAM). A survey was conducted to collect the data from the users of the bike-sharing service in Kuala Lumpur. Structural equation modelling was used to analyse the data and find the relationship between variables. The empirical analyses showed that perceived ease of use and perceived usefulness of the bike-sharing service positively impact all facets of consumer engagement with bike-sharing service, which subsequently influences the continuance usage intention of bike-sharing service. The findings of this study offer useful insights that could enhance the consumption of bike-sharing service. This study also offers some guidelines to transportation practitioners, policymakers, and urban planners regarding promoting healthy and sustainable travel behaviour among urban commuters through bike-sharing service.

A novel predict-then-optimize method for sustainable bike-sharing management: a data-driven study in China.

Sustainable operations management will appeal to the post-pandemic world. As the economy recovers, the surging demand for low-carbon bike-sharing has led to exacerbated mismatch in urban transportation. It is a serious challenge to optimize the reallocation schedule of sharing bikes among multiple positions in a network. To address the problem, we develop a novel predict-then-optimize method consisting of a data-driven robust optimization model and a branch-and-price algorithm. The optimization model derives the predicted demand surplus of each position based on historical data, enabling the optimal reallocation schedule in the network at minimum operational costs. Based on the prediction, the branch-and-price algorithm can find out the best routes of assigning bikes to specific positions that further improves transportation efficiency. Finally, we deploy the predict-then-optimize method to a realistic bike-sharing network in one major city of China. The computational results demonstrate that our method can significantly save the cost of operations and reduce the waste of resources. Therefore, the novel predict-then-optimize method has a great potential to facilitate the sustainable development of bike-sharing systems in urban transportation.

Exploring Use Acceptance of Electric Bicycle-Sharing Systems: An Empirical Study Based on PLS-SEM Analysis.

The electric bicycle-sharing system (EBSS) is the fourth-generation urban shared bicycle travel system, which effectively improves the travel efficiency of urban residents and solve the problem of urban congestion. This study attempts to use an extended technology acceptance model (TAM) method to study the acceptance of EBSSs. We had introduced four potential variables, including perceived pleasure (PP), perceived environmental value (PEV), perceived cost (PC), and perceived reliability (PR), into the classic TAM to form a new EBSS-TAM. Data were obtained by using a Likert scale questionnaire from 399 citizens in China. Partial least-squares structural equation modeling (PLS-SEM) with reflective constructs was employed as the analysis method. The results showed that: (1) the EBSS-TAM can explain user behaviors regarding the use of EBSSs. PP has a positive impact on behavior attitude (BA) while having no impact on behavior intention (BI). PEV has no impact on BA and BI. PC has a negative impact on BA and has no impact on BI. PR has a positive impact on BA while having no impact on BI. Perceived ease of use (PEU) has a positive impact on PP and PEV. (2) Younger users (under 35 years old) are more likely to change from liking CBSSs to using EBSSs than older users are. Male users are more satisfied with EBSSs because of their ease of use. The users who never used CBSSs are more likely to perceive the environmental protection value of EBSSs. Some managerial implications were proposed for the EBSSs.

The impact of the COVID-19 pandemic on the behaviour of bike sharing users.

Globally most governments implemented a 'Working from Home' (home office) strategy to contain the spread of the coronavirus in 2020 in order to ensure public safety and minimize the transmission of the virus. Unsurprisingly studies have found that COVID-19 has had a detrimental impact on urban transportation systems; however, the number of shared bicycle riders is progressively growing compared to other modes of public transit. The aim of this study is to investigate the influence of COVID-19 on the usage of shared bicycle systems in order to identify passenger travel patterns and habits. In addition, bicycle rentals are becoming more popular in some locations. This demonstrates that bike sharing as a transport option has a high level of social adaptability and is progressively being adopted by the general population in a fashion that promotes the resilience of transport systems.

Riding the wave: Predicting the use of the bike-sharing system in Barcelona before and during COVID-19.

To simultaneously promote health, economic, and environmental sustainability, a number of cities worldwide have established bike-sharing systems (BSS) that complement the conventional public transport systems. As the rapid spread of COVID-19 becoming a global pandemic disrupted urban mobility due to government-imposed lockdowns and the heightened fear of infection in crowded spaces, populations were increasingly less likely to use public transportation and instead shifted toward alternative transport systems, including BSS. In this study, we use probabilistic machine learning in a quasi-experimental research design to identify how the relevance of a comprehensive set of factors to predict the use of Bicing (the BSS in Barcelona) may have changed as COVID-19 unfolded. We unpack the key factors in predicting the use of Bicing, uncovering evidence of increasing bike-related built infrastructure (e.g., tactical urbanism), trip distance, and the income levels of neighborhoods as the most relevant predictors. Moreover, we find that the relevance of the factors in predicting Bicing usage has generally decreased during the global pandemic, suggesting altered societal behavior. Our study enhances the understanding of BSS and societal behavior, which can have important implications for developing resilient programs for cities to adopt sustainable practices through transport policy, infrastructure planning, and urban development.

The periodicity and initial evolution of micro-mobility systems: a case study of the docked bike-sharing system in New York City, USA.

Objectives: This study developed an analytical framework that aims at understanding the evolutionary processes of a micro-mobility system (for example, bike-sharing), which offers insights into the transforming nature of a city transport system. Methods: Firstly, the framework applied a Gaussian Mixture Model to examine the long-term fluctuations of travel demands. Secondly, it investigated the growth trajectories of service points via exponential and logistic growth models. Cumulative connections with other points represented the growth of a service location. An eigendecomposition approach was used to uncover the hidden structures behind the growth curves. Results: This framework was applied in the docked bike-sharing program in New York City, USA. The results show that there existed periodic patterns of travel demands in the long term. The majority of stations grew rapidly after they began to operate. However, the temporal signatures of stations' growth displayed some variations across different locations. Conclusion: This proposed workflow can be employed in other cities with similar context to better investigate how micro-mobility systems evolve.

Information sharing system for donation interval query in Yangtze River Delta / 中国输血杂志

【Objective】 To continuously improve the management of blood donors and ensure blood safety in the Yangtze River Delta(Zhejiang province, Jiangsu province, Anhui province and Shanghai city). 【Methods】 The information sharing system for donation interval query in the Yangtze River Delta was established in July 2020 to provide data and technical support to block blood donors who should not redonate due to insufficient intervals. 【Results】 Blood stations in the Yangtze River Delta unified the information sharing standard, developed the query service and realized the information sharing of donation interval query. 【Conclusion】 The establishment of this system could reduce the risk of blood donation from eligible donos with insufficient donation interval, and further improve the management of blood donors and blood safety in the Yangtze River Delta.

Use of an electronic health record data sharing system for identifying current contraceptive use within the WWAMI region Practice and Research Network.

OBJECTIVE: To evaluate the ability of electronic health record (EHR) data extracted into a data-sharing system to accurately identify contraceptive use. STUDY DESIGN: We compared rates of contraceptive use from electronic extraction of EHR data via a data-sharing system and manual abstraction of the EHR among 142 female patients ages 15-49 years from a family medicine clinic within a primary care practice-based research network (PBRN). Cohen's kappa coefficient measured agreement between electronic extraction and manual abstraction. RESULTS: Manual abstraction identified 62% of women as contraceptive users, whereas electronic extraction identified only 27%. Long acting reversible (LARC) methods had 96% agreement (Cohen's kappa 0.78; confidence interval, 0.57-0.99) between electronic extraction and manual abstraction. EHR data extracted via a data-sharing system was unable to identify barrier or over-the-counter contraceptives. CONCLUSIONS: Electronic extraction found substantially lower overall rates of contraceptive method use, but produced more comparable LARC method use rates when compared to manual abstraction among women in this study's primary care clinic. IMPLICATIONS: Quality metrics related to contraceptive use that rely on EHR data in this study's data-sharing system likely under-estimated true contraceptive use.

A cloud-based home health care information sharing system to connect patients with home healthcare staff -A case report of a study in a mountainous region.

We have developed a cloud system, the e-Renraku Notebook (e-RN) for sharing of home care information based on the concept of "patient-centricity". In order to assess the likelihood that our system will enhance the communication and sharing of information between home healthcare staff members and home-care patients, we selected patients who were residing in mountainous regions for inclusion in our study. We herein report the findings.Eighteen staff members from 7 medical facilities and 9 patients participated in the present study.The e-RN was developed for two reasons: to allow patients to independently report their health status and to have staff members view and respond to the information received. The patients and staff members were given iPads with the pre-installed applications and the information being exchanged was reviewed over a 54-day period.Information was mainly input by the patients (61.6%), followed by the nurses who performed home visits (19.9%). The amount of information input by patients requiring high-level nursing care and their corresponding staff member was significantly greater than that input by patients who required low-level of nursing care.This patient-centric system in which patients can independently report and share information with a member of the healthcare staff provides a sense of security. It also allows staff members to understand the patient's health status before making a home visit, thereby giving them a sense of security and confidence. It was also noteworthy that elderly patients requiring high-level nursing care and their staff counterpart input information in the system significantly more frequently than patients who required low-level care.

Design and development of centralized management and sharing system of medical equipment / 医疗卫生装备

Objective To solve the problems in medical equipment utilization,supervision,management,clinical safety and etc.Methods A centralized management and sharing system of medical equipment was developed with functional requirements analysis,C/S architecture and programming.Results The system eliminated the needs for manual accounting and allocation,enhanced medical equipment usage rate and improved equipment borrowing procedure.Conclusion The system contributes to dynamic management and efficacy evaluation of medical equipment,so as the working efficiency can be increased while the costs can be decreased for manpower and materials.

Research of the medication rules of ankylosing spondylitis based on the famous doctors' medical records / 国际中医中药杂志

Objective Applyingthe traditional Chinese medical knowledge service and sharing system, to explore the medication rules of ankylosing spondylitis in the medical records.Methods The ankylosing spondylitis medical records in the traditional chinese medical knowledge service and sharing system has been searched and 149 prescriptions were identified. The medication frequency, association rules, herbal combination and the cluster analyses were explored.Results Among the 149 prescriptions with 210 drugs, the use of high frequency drugs wereGouji, Baishao, Danggui, Duzhong, Weilingxian, and other medication whose frequency was more than 20 times were good at tonifying liver and kidney, activating blood circulation, clearing heat and promoting diuresis, dispelling cold and removing dampness. The high frequency of drug combinations included Sangjisheng and Gouji, Duzhong and Gouji, Baishao and Gouji, Guizhi and Baishao, Gancao and Danggui.Conclusions The treatment of ankylosing spondylitis with Chinese medicine is mainly based on tonifying liver and kidney, supplemented with drugs for warming Kidney Yang, dispelling cold and dehumidification, promoting blood circulation and removing blood stasis, clearing heat and promoting diuresis, dispelling wind and cold.

Study on Rules of Acupoints Selection of Acupuncture and Moxibustion in Treatment of Alzheimer Disease / 中国中医药信息杂志

Objective To explore the rule of acupoints selection of acupuncture and moxibustion in treating Alzheimer disease based on TCM Knowledge Service and Sharing System (V1.2.8); To provide references for the clinic. Methods Literature about acupuncture and moxibustion for the treatment of Alzheimer disease in CNKI, Choingqing Weipu, Wanfang database, and CBM from 1st January, 1996 to 1st January, 2016 was searched. Excel2007 was used to establish database and enter into TCM Knowledge Service and Sharing System (V1.2.8). The data were analyzed by data mining method such as frequency analysis, association rule analysis, point-to-pair analysis and core prescription analysis. Results After screening, 125 acupuncture prescriptions were included, involving 108 acupoints and 998 total acupoints selection frequency. Baihui had the highest frequency (78 times, 62.40％). The core acupoint combinations were Zusanli-Baihui, Zusanli-Sanyinjiao, and Baihui-Sishencong. The main core prescription was Bauhui, Zusanli, Sishencong, Shenmen, Taixi, Sanyinjiao, Neiguan, Taichong, Fenglong, Dazhui, Fengchi, Shenshu, Shenting, Xuanzhong. The second core prescription was Hegu, Yintang, Tanzhong, Xuehai, Geshu, Ganshu, Pishu, Qihai, Yanglingyuan, and Shangxing. Conclusion The rules of acupoints selection analyzed by TCM Knowledge Service and Sharing System (V1.2.8) for Alzheimer disease can be used to guide clinical application, but with modification according to symptoms.

Upgrading of the Quality Control System of the Clinical Scientific Research Sharing System / 医学信息学杂志

Based on the research platform constructed in the process of upgrading of the clinical scientific research sharing system,the paper optimizes the scientific research data structure,analyzes the problem of the original data quality,optimizes the original storage mode,and establishes a new data quality control system,so as to connect the clinical scientific research sharing system with the future development orientation of the medical big data era and precision medicine.

Application of Non-pharmacotherapy Treating Cervical Radiculopathy in Real-world Study / 世界科学技术-中医药现代化

This study was aimed to analyze the application of non-pharmacotherapy in treating cervical radiculopathy (CR) in real-world,and to provide clinical reference for CR non-pharmacotherapy.The clinical real-world data of CR was extracted by using information sharing system of traditional Chinese medicine (TCM) and clinical research.Six hundred and twenty-eight inpatients and outpatients with CR were enrolled from December 2012 to July 2014 in the information system database of Wangjing Hospital.Basic characteristics of the non-pharmacotherapy groups were analyzed by statistical description method.The node degree and mutual information value were recorded for non-pharmacotherapy application of all patients by using liquorice software.Complex network diagrams were generated.The results showed that 47％ of CR patients received non-pharmacotherapy (294/628),including 67 males and 227 females.The average age of patients was 49 years old,and the prevalence of the disease was the highest from 45 to 65 years old.In all patients,the usage of manipulation and cervical traction was higher,and the combination of manipulation and acupuncture was the most.Within outpatients,the proportion of cervical traction was higher,and the combination of manipulation and acupuncture was the most frequently.Within inpatients,the proportion of manipulation and cervical traction was higher,and the combination of comprehensive physical therapy and exercise therapy was the most frequently.It was concluded that non-pharmacotherapy has been commonly used in clinical treatment of CR.Cervical traction and manipulation was the widest applications.The combination treatment was in wide application.Future studies should increase the sample size of CR patients from different regions,and enhance gradually the level of evidence of clinical research for non-pharmacotherapy treating CR.

Study on Regularity of Professor Shen Zhongyuan's Treatment of Hepatitis B Liver Cirrhosis Based on Data Mining / 世界科学技术-中医药现代化

Through the clinical research information sharing system,this study was aimed to explore the law of Professor Shen Zhongyuan's treatment of hepatitis B liver cirrhosis,and to inherit academic thoughts of senior famous traditional Chinese medicine (TCM) doctors.The structured electronic medical record system was used.The information of Professor Shen Zhongyuan expert outpatient with hepatitis B cirrhosis was collected from September 2012 to September 2015.And then,the medical data were extracted and analyzed to find out the medication regularity of hepatitis B cirrhosis and the distribution rule of the taste tropism of herbal medicine in the treatment.The results showed that 388 cases of hepatitis B cirrhosis patients with a total of 127 kinds of herbal medicine.The cumulative using frequency of herbal medicine was 5595.The most commonly used 10 types of Chinese medicine were turmeric,citrus aurantium,cuttlefish bone,atractylodes,rhizoma coptidis,salvia miltiorrhiza,lobelia,artemisia capillaris,fructus trichosanthis,and chicken's gizzard-membrane.The taste of mostly used Chinese medicine is bitter and sweet,followed by acrid.The nature of Chinese medicine is cold,slightly cold,warm and even.The meridian tropism of Chinese medicine is mainly to the liver and spleen meridian.It was concluded that Professor Shen in treatment of hepatitis B cirrhosis drug laws was often to use heat-clearing and toxin-relieving,qi-reinforcing and spleen-invigorating medicine,liver-soothing and qi-regulating medicine,blood-activating and stasis-removing medicine.The nature of herbal medicine used is mostly cold,slightly cold,warm and even.It is based on the TCM syndrome differentiation and treatment thought of “regulating yin-yang to maintain the balance.” It showed the important principle of “toxin-removing,stasis-removing,liver-regulating and spleen-strengthening” by professor Shen.

Correlational Study on Traditional Chinese Medicine Syndrome Distribution Characteristics of HIV Infection in Xinjiang Region Based on Information Sharing System / 世界科学技术-中医药现代化

This paper was aimed to study traditional Chinese medicine (TCM) syndrome distribution characteristics of HIV infection in Xinjiang region based on the Clinical Medical Research Information Sharing System (CMRISS).CMRISS was used to establish a database (data were from 1151 hospital electronic medical records from May 2011 to March 2012).Oracle and ETL software were used in the data processing.Visualization analysis was used in the data mining.The results showed that syndrome distribution of HIV infection in Xinjiang region was concentrated in four categories with the blockade of dampness due to qi deficiency,deficiency of both qi and yin,yin deficiency of liver and kidney,and the stagnation of liver qi.The syndrome of blockade of dampness due to qi deficiency was relatively concentrated in the range of 20-30 years old,with the majority of female population.The proportion of sexually transmitted infection was more.The syndrome of deficiency of both qi and yin was relatively concentrated in the age range of 30-40 years old,with the majority of male population.The large proportion of infection was due to intravenous drug use.Among different TCM syndromes,Uyghur population occupied relatively large part.Stratified analysis on disease course due to the change of TCM syndrome according to 1 year,3 years and 5 years revealed the disease development rule from excess syndrome,deficiency combined with excess,to deficiency syndrome.It was concluded that the application of CMRISS was able to process a large amount of clinical data.The data mining results can be used to guide clinical practice.It provided a better platform for the scientific research of TCM clinical practice.

Privacy-Preserving and Secure Sharing of PHR in the Cloud.

As a new summarized record of an individual's medical data and information, Personal Health Record (PHR) can be accessible online. The owner can control fully his/her PHR files to be shared with different users such as doctors, clinic agents, and friends. However, in an open network environment like in the Cloud, these sensitive privacy information may be gotten by those unauthorized parties and users. In this paper, we consider how to achieve PHR data confidentiality and provide fine-grained access control of PHR files in the public Cloud based on Attribute Based Encryption(ABE). Differing from previous works, we also consider the privacy preserving of the receivers since the attributes of the receivers relate to their identity or medical information, which would make some sensitive data exposed to third services. Anonymous ABE(AABE) not only enforces the security of PHR of the owners but also preserves the privacy of the receivers. But a normal AABE with a single private key generation(PKG) center may not match a PHR system in the hierarchical architecture. Therefore, we discuss not only the construction of the PHR sharing system base on AABE but also how to construct the PHR sharing system based on the hierarchical AABE. The proposed schemes(especially based on hierarchical AABE) have many advantages over the available such as short public keys, constant-size private keys, which overcome the weaknesses in the existing works. In the standard model, the introduced schemes achieve compact security in the prime order groups.