ABSTRACT
STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To compare the results of revision extension of fusion surgery using the newly designed revision rod and implant-replacement surgery in thoracolumbar spine. METHODS: Thirty-one patients who underwent extension of fusion surgery using the revision rod for adjacent segment disease were included in this study. Thirty-one patients who underwent implant-replacement revision surgery were selected as a control group by matching age, sex, preoperative diagnosis, and number of revision segments. RESULTS: The mean age was 70.7 ± 8.0 years in the revision rod (RR) group and 69.0 ± 8.4 years in the control group. Preoperative diagnoses, underlying diseases, and mean number of revision segments (2.2 ± 1.1) were similar in both groups. The change of hemoglobin (1.0 ± 1.9 vs 2.5 ± 1.5 g/dl; P < .01), hematocrit (4.1 ± 4.9 vs 7.2 ± 4.4 % P < .01) and albumin (.8 ± .9 vs 1.3 ± .4 g/dl; P < .01) levels before and after surgery showed significant differences between the two groups. Hemovac drainage was significantly less in the RR group (P = .01). The mean operative time was shorter in the RR group (203.5 ± 9.5 min vs 233.5 ± 8.7 min; P = .12) with no statistical difference. Radiological results showed that the average lumbar lordosis 2 years after surgery was lower in the RR group compared to the control group (25.1 ± 9.9° vs 32.9 ± 9.8°; P = .02). Union rates and clinical outcomes were not different between the two groups. CONCLUSIONS: Revision extension of fusion surgery using a newly designed revision rod had less hemovac drainage and superior laboratory findings compared to implant-replacement revision surgery.
ABSTRACT
Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions.
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Automobile Driving , Accidents, Traffic , Autonomous Vehicles , SoftwareABSTRACT
INTRODUCTION: The role of cycling has become more important in the urban transport system during the Covid-19 pandemic. As public transport passengers have tried to avoid crowded vehicles due to safety concerns, a rapid surge of cycling activities has been noted in many countries. This implies that more cyclists might be exposed to air pollution, potentially leading to health problems in cities like Seoul where the level of air pollution is high. METHODS: We utilised three years of bike sharing programme (Ddareungi) data in Seoul and time series models to examine the changes in the relationship between particulate concentration (PM2.5) and total daily cycling duration before and during the pandemic. RESULTS: We find that cyclists reacted less to the PM2.5 level during the pandemic, potentially due to the lack of covid-secure travel modes. Specifically, our results show significant negative associations between concentrations of PM2.5 and total daily cycling duration before the pandemic (year 2018 and 2019). However, this association became insignificant in 2020. CONCLUSIONS: Building comprehensive cycling infrastructure that can reduce air pollution exposure of cyclists and improving air quality alert systems could help build a more resilient city for the future.
ABSTRACT
The relationship between the built environment and walking has been analyzed for decades. However, the seasonality effects on the relationship between the built environment and walking have not been well examined even though weather is one of the key determinants of walking. Therefore, this study used 2007-8 Scottish Household Survey data collected over two years and estimated the interaction effects between the urbanization setting (i.e., residential locations: urban, town and rural areas) and seasons (i.e., spring, summer, autumn and winter) on walking. Scottish Urban-rural classification scheme is measured based on the population and access to large cities, and used as a key independent variable. The number of walking days for specific purposes such as work or shopping (utilitarian walking) during the past 7 days is used as a dependent variable. The results show that there are significant geographical variations of seasonality effect on utilitarian walking. That is, people living in rural areas are more sensitive to seasonality impacts than those living in urban areas. In addition, we found that the association between urbanization setting and utilitarian walking varies across seasons, indicating that their relationship can be miss-estimated if we ignore the seasonality effects. Therefore, policy makers and practitioners should consider the seasonality effects to evaluate the effectiveness of land use policy correctly. Finally, we still find the significant association between the urbanization setting and utilitarian walking behaviour with the consideration of seasonality effects, supporting the claim of New Urbanism.
