DynLight: Realize dynamic phase duration with multi-level traffic signal control

Adopting reinforcement learning (RL) for traffic signal control (TSC) is increasingly popular, and RL has become a promising solution for traffic signal control. However, several challenges still need to be overcome. Firstly, most RL methods use fixed action duration and select the green phase for the next state, which makes the phase duration less dynamic and flexible. Secondly, the phase sequence of RL methods can be arbitrary, affecting the real-world deployment which may require a cyclical phase structure. Lastly, the average travel time and throughput are not fair metrics to evaluate TSC performance. To address these challenges, we propose a multi-level traffic signal control framework, DynLight, which uses an optimization method Max-QueueLength (M-QL) to determine the phase and uses a deep Q-network to determine the duration of the corresponding phase. Based on DynLight, we further propose DynLight-C which adopts a well-trained deep Q-network of DynLight and replace M-QL with a cyclical control policy that actuates a set of phases in fixed cyclical order to realize cyclical phase structure. Comprehensive experiments on multiple real-world datasets demonstrate that DynLight achieves a new state-of-the-art. Furthermore, the deep Q-network of DynLight can learn well on determining the phase duration and DynLight-C demonstrates high performance for deployment.Comment: 9 pages, 9 figure

Non-communicable diseases sustained high call: China's health care model should be transformed as soon as possible

Background: There is sufficient evidence that the prevalence of non-communicable diseases (NCDs) in China is increasing rapidly. Results from the Fourth China National Health Services Survey (2008) show that compared to 2003, the prevalence of chronic diseases in China increased by 5%, while results from the Fifth National Health Services Survey (2013) showed an increase of 9% since 2008. As the world's most populous country and in the face of the rapid rise of non-communicable diseases, China lacks effective measures to achieve significant results on aspects of tobacco use, unhealthy diet, lack of exercise, harmful alcohol use and other risk factors. Of more concern is that the Chinese health care model is still stuck in the "medical services" stage. The "therapy" of health care models not only cause China to experience high health costs - there is also a steady increase in adverse health outcomes, with a failure to timely and effectively respond to the challenges of NCDs. Purpose: This article aims to analyze health care inputs and outputs since Chinese health care reform, and to provide a useful reference to improve Chinese future health care policies

A New Logistic Dynamic Particle Swarm Optimization Algorithm Based on Random Topology

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research

Fan-Shaped Model for Generating the Anisotropic Catchment Area of Subway Stations based on Feeder Taxi Trips

The catchment areas of subway stations have always been considered as a circular shape in previous research. Although some studies show the catchment area may be affected by road conditions, public transportation, land use, and other factors, few studies have discussed the shape of the catchment area. This study focuses on analyzing the anisotropy of catchment areas and developing a sound methodology to generate them. Based on taxi global positioning system (GPS) data, this paper first proposes a data mining method to identify feeder taxi trips around subway stations. Then, a fan-shaped model is proposed and applied to Xi\u27an Metro Line 1 to generate catchment areas. The number and angle of fan areas are determined according to the spatial distribution characteristics of GPS points. Results show that the acceptable distance of the catchment area has significant differences in different directions. The average maximum acceptable distance of one station is 2.31 times the minimum. Furthermore, for feeder taxis, the overlap ratio of the catchment area is very high. Travelers in several places could choose several different stations during the travel. A multiple linear regression model was introduced to find the influencing factors, and the result shows the anisotropy of the catchment area is affected not only by neighboring subway stations, but also by the road network, distance from the city center, and so on

The human parvovirus B19 non-structural protein 1 N-terminal domain specifically binds to the origin of replication in the viral DNA

The non-structural protein 1 (NS1) of human parvovirus B19 plays a critical role in viral DNA replication. Previous studies identified the origin of replication in the viral DNA, which contains four DNA elements, namely NSBE1 to NSBE4, that are required for optimal viral replication (Guan et al, 2009, J. Virology, 83, 9541-9553). Here we have demonstrated in vitro that the NS1 N-terminal domain (NS1N) binds to the origin of replication in a sequence-specific, length-dependent manner that requires NSBE1 and NSBE2, while NSBE3 and NSBE4 are dispensable. Mutagenesis analysis has identified nucleotides in NSBE1 and NSBE2 that are critical for NS1N binding. These results suggest that NS1 binds to the NSBE1-NSBE2 region in the origin of replication, while NSBE3 and NSBE4 may provide binding sites for potential cellular factors. Such a specialized nucleoprotein complex may enable NS1 to nick the terminal resolution site and separate DNA strands during replication

The human parvovirus B19 non-structural protein 1 N-terminal domain specifically binds to the origin of replication in the viral DNA

The non-structural protein 1 (NS1) of human parvovirus B19 plays a critical role in viral DNA replication. Previous studies identified the origin of replication in the viral DNA, which contains four DNA elements, namely NSBE1 to NSBE4, that are required for optimal viral replication (Guan et al, 2009, J. Virology, 83, 9541-9553). Here we have demonstrated in vitro that the NS1 N-terminal domain (NS1N) binds to the origin of replication in a sequence-specific, length-dependent manner that requires NSBE1 and NSBE2, while NSBE3 and NSBE4 are dispensable. Mutagenesis analysis has identified nucleotides in NSBE1 and NSBE2 that are critical for NS1N binding. These results suggest that NS1 binds to the NSBE1-NSBE2 region in the origin of replication, while NSBE3 and NSBE4 may provide binding sites for potential cellular factors. Such a specialized nucleoprotein complex may enable NS1 to nick the terminal resolution site and separate DNA strands during replication