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Background: Quinine (QN) remains an effective drug for malaria treatment. However, quinine resistance (QNR) in Plasmodium falciparum has been reported in many malaria-endemic regions particularly in African countries. Genetic polymorphism of the P. falciparum Na+/H+ exchanger (pfnhe1) is considered to influence QN susceptibility. Here, ms4760 alleles of pfnhe1 were analysed from imported African P. falciparum parasites isolated from returning travellers in Wuhan, Central China.Methods: A total of 204 dried-blood spots were collected during 2011-2016. The polymorphisms of the pfnhe1 gene were determined using nested PCR with DNA sequencing.Results: Sequences were generated for 99.51% (203/204) of the PCR products and 68.63% (140/204) of the isolates were analysed successfully for the pfnhe1 ms4760 haplotypes. In total, 28 distinct ms4760 alleles containing 0 to 5 DNNND and 1 to 3 NHNDNHNNDDD repeats were identified. For the alleles, ms4760-1 (22.86%, 32/140), ms4760-3 (17.86%, 25/140), and ms4760-7 (10.71%, 15/140) were the most prevalent profiles. Furthermore, 5 undescribed ms4760 alleles were reported.Conclusions: The study offers an initial comprehensive analysis of pfnhe1 ms4760 polymorphisms from imported P. falciparum isolates in Wuhan. Pfnhe1 may constitute a good genetic marker to evaluate the prevalence of QNR in malaria-endemic and non-endemic regions. [ABSTRACT FROM AUTHOR]

Highlights • The influencing factors of rear-end crash risk were initially analyzed by using the desired safety margin (DSM) model. • The influencing factors including upper and lower limits of DSM, acceleration and deceleration rate and response time on the car-following behavior are investigated. And simulation experiments were designed to evaluate the impact of heterogeneity of car-following behavior on rear-end crash risk. • Decreasing the lower (or upper) limit of the DSM, increasing the response time, decreasing the sensitivity coefficient for deceleration, or increasing the sensitivity coefficient for acceleration can increase rear-end crash risk. • Two critical factors affect shock waves, namely, driving behavior characteristics and proportion of different driving styles. A wide extent of driving behavior heterogeneity can attenuate shock waves and subsequently reduce rear-end crash risk. • Exploring the effect of each driving behavior parameter on rear-end crash probability is useful for urban road traffic control, and it can provide improved understanding of abnormal driving behavior characteristics to reduce rear-end crash risks. Abstract An increasing number of vehicles travel on freeways result not only in traffic congestions but also accidents. Rear-end crashes in freeways can be collectively attributed to drivers, vehicles, and road infrastructure, but driving behavior plays a key role in influencing car-following safety. This study aims to investigate the impact of heterogeneity of driving behavior on rear-end crash risk. Driving behavior depends on perceived risk levels, acceleration and deceleration habits, and driver reaction characteristics. Thus, the influencing factors of rear-end crash risk were initially analyzed by using the desired safety margin (DSM) model. Subsequently, five driving behavior parameters, including upper and lower limits of DSM, sensitivity coefficients of acceleration and deceleration, and response time, were calibrated by using the vehicle trajectories from the Next Generation Simulation I-80 datasets. Simulation experiments were designed to evaluate the impact of heterogeneity of car-following behavior on rear-end crash risk. Results showed that decreasing the lower (or upper) limit of the DSM, increasing the response time, increasing the sensitivity coefficient for acceleration, or decreasing the sensitivity coefficient for deceleration can increase rear-end crash risk. In addition, if stable and unstable driving styles coexist, then their proportions have important influences on rear-end crash risk. These results imply that two critical factors affect shock waves, namely, driving behavior characteristics and proportion of different driving styles. Thus, a potential strategy for the adjustment of the proportions of unstable driving styles can attenuate shock waves and reduce rear-end crash risk to a certain extent. Moreover, a wide extent of driving behavior heterogeneity can attenuate shock waves and subsequently reduce rear-end crash risk. Overall, driving behavior heterogeneity has an important impact on rear-end crash risk. Exploring the effect of each driving behavior parameter on rear-end crash probability is useful for urban road traffic control, and it can provide improved understanding of abnormal driving behavior characteristics to minimize rear-end crash risks. [ABSTRACT FROM AUTHOR]

Most existing studies on routing guidance only paid attention to the average path travel time, which failed to consider travel time reliability (TTR) preferences by different travelers. In this study, a moment-matching-based hybrid genetic algorithm (MHGA) is proposed to search the reliable shortest path (RSP) in stochastic road networks with link correlations. First, the goodness-of-fit results based on field data reveal that lognormal distributions are more appropriate for characterizing link travel times. The impact of topological distance (measured by the number of links) and road type on link correlations is also scrutinized. Then, a moment-matching method (MOM) is utilized to determine the parameters of the approximate path travel time distribution (TTD) by accounting for link correlations. A local search algorithm is designed to improve the search ability of the path finding algorithm. In view of travelers’ risk tolerance, the algorithm enables the provision of personalized routing guidance for individual travelers. Furthermore, to support path finding applications in a large-scale network, heuristic constraints are imposed to help reduce the computational workload and accelerate the convergence speed of the search process. Finally, numerical case studies based on synthetic networks and a real road network in Beijing are presented, and the results help demonstrate that the algorithm has good potential to solve RSP searching problems in a large-scale network with desirable efficiency. [ABSTRACT FROM AUTHOR]

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network. [ABSTRACT FROM AUTHOR]

Concerns over transportation energy consumption and green-household gas (GHG) emissions have prompted a growing body of research into the influence of built environment on travel behavior. Studies on the relationship between land use and travel behavior are often at a certain aggregated spatial unit such as traffic analysis zone (TAZ), spatial issues occur among individuals clustered within a zone because of the locational effects. However, recognition of the spatial issues in travel modeling was not sufficiently investigated yet. The object of this study is twofold. First, a multilevel hazard model was applied to accommodate the spatial context in which individuals generate commuting distance. Second, this research provides additional insights into examine the effects of socio-demographics and built environment on commuting distance. Using Washington metropolitan area as the case, the built environment measures were calculated for each TAZ. To estimate the model parameters, the robust maximum likelihood estimation method for a partial function was used, and the model results confirmed the important roles that played by the TAZ and individual level factors in influencing commuting distance. Meanwhile, a comparison among the general multilevel model, single level and multilevel hazard models was conducted. The results suggest that application of the multilevel hazard-based model obtains significant improvements over traditional model. The significant spatial heterogeneity parameter indicates that it is necessary to accommodate the spatial issues in the context of commuting distance. The results are expected to give urban planners a better understanding on how the TAZ and individual level factors influence the commuting distance, and consequently develop targeted countermeasures. [ABSTRACT FROM AUTHOR]

Studying driver awareness of information, particularly risk perception, is vital to understanding driving behavior and improving traffic safety. In the dynamic interaction of a driver-vehicle-environment system, risk perception of drivers changes dynamically. In this study, we focused on drivers’ risk perception at unsignalized intersections in China and analyzed their crossing behavior with consideration of risk perception. Based on cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established for the crossing processes between two straight-moving vehicles from the orthogonal direction. Drivers’ acceptable risk perception levels were identified using a self-developed data analysis method. On the basis of game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed, then the crossing behavior models of drivers were established. Finally, the behavior models were validated using data collected from real-world vehicle movements and driver decisions. The results showed that the developed behavior models had both high accuracy and good applicability. This study would provide theoretical and algorithmic references for the microscopic simulation and active safety control system of vehicles. [ABSTRACT FROM AUTHOR]

• We propose a novel COVID-19 multiple outbreak analysis method (NCMOA). • This method divides the impact of multiple outbreaks on time and degree separately. • The differentiated air traffic recovery patterns are analyzed in the case study. • The impacts on epi -center and the nation have different critical influence factors. • The implication on policy formulation of airline and government is proposed. The spread of COVID-19 results in a significant drop in traffic levels worldwide. Quantifying the impact of multiple COVID-19 outbreaks on traffic systems is critical to developing differentiated policies in the future. This paper proposes a novel COVID-19 multiple outbreak analysis method (NCMOA), dividing the impact scope and degree under multiple COVID-19 disturbances, and using the recovery rate and accumulated loss to quantify the impacts on air passenger flow. A case study based on Chinese national air traffic flow is executed, and the recovery patterns and the differentiated disturbances are analyzed. Results show that air passenger flow recovers with a similar pattern after the first outbreak, and subsequent outbreaks cause local effects and cannot affect the overall recovery pattern. Further, the heterogeneous influence factors and trends on the epi -centers (EC) and the nation are analyzed. In addition, the methods and results of this paper quantify the impact of COVID-19 on air passenger flow at a more detailed level under multiple disturbances. They could provide a basis for differentiated policy formulation of airlines and government in the future. [ABSTRACT FROM AUTHOR]

The k -nearest neighbor (KNN) model is an effective statistical model applied in short-term traffic forecasting that can provide reliable data to guide travelers. This study proposes an improved KNN model to enhance forecasting accuracy based on spatiotemporal correlation and to achieve multistep forecasting. The physical distances among road segments are replaced with equivalent distances, which are defined by the static and dynamic data collected from real road networks. The traffic state of a road segment is described by a spatiotemporal state matrix instead of only a time series as in the original KNN model. The nearest neighbors are selected according to the Gaussian weighted Euclidean distance, which adjusts the influences of time and space factors on spatiotemporal state matrices. The forecasting accuracies of the improved KNN and of four other models are compared, and experimental results indicate that the improved KNN model is more appropriate for short-term traffic multistep forecasting than the other models are. This study also discusses the application of the improved KNN model in a time-varying traffic state. [ABSTRACT FROM AUTHOR]

Urban expressways usually experience several levels of service (LOS) because of the stop-and-go traffic flow caused by congestion. Moreover, multiple shock waves generate at different LOS interfaces. The dynamic of shock waves strongly influences the travel time reliability (TTR) of urban expressways. This study proposes a path TTR model that considers the dynamic of shock waves by using probability-based method to characterize the TTR of urban expressways with shock waves. Two model parameters are estimated, namely distribution of travel time (TT) per unit distance and travel distances in different LOS segments. Generalized extreme value distribution and generalized Pareto distribution are derived as distributions of TT per unit distance for six different LOS. Distribution parameters are estimated by using historical floating car data. Travel distances in different LOS segments are calculated based on shock wave theory. The range of TT along the path, which can help drivers arrange their trips, can be obtained from the TTR model. Finally, comparison is made among the proposed TTR model, generalized Pareto contrast model, which does not consider different LOS or existence of shock waves, and normal contrast model, which assumes TT per unit distance as normal distribution without considering shock wave. Results show that the proposed model achieves higher prediction accuracy and reduces the prediction range of TT. The conclusions can be further extended to TT prediction and assessment of measures to improve reliability of TT in a network. [ABSTRACT FROM AUTHOR]

Abstract: Reconstruction of pedestrian–vehicle accident is a worldwide problem. Numerous previous studies have been carried out on accidents with vehicular skid marks or definite pedestrian throw distances. However, little could be done if marks or throw distances cannot be obtained in accident reconstruction. This paper first describes the physical model of dynamic process of pedestrian head impact on windshield glazing. Some simplifications are made to obtain a better and more practical model, including discussing the support boundary conditions. Firstly, the paper modeled the relations between pedestrian impact speed and deflection of windshield glazing based on the impact dynamics and thin plate theory. Later, the relations of vehicle impact speed and deflection are discussed. Therefore, a model of vehicle impact speed versus deflection of windshield glazing is developed. The model is then verified by ten real-world accident cases to demonstrate its accuracy and reliability. This model provides investigators a new method to reconstruct pedestrian–vehicle accidents. [Copyright &y& Elsevier]

• We propose a lane change prediction model with adaptability and portability. • The proposed model introduces the decision threshold which changes according to environments. • Bayesian inference is used to update the decision threshold on time. • The adaptability and stability of the model are proved. Predicting lane change behavior of surrounding vehicles is critical for autonomous vehicles as the lane change may cause conflict between vehicles. Most of the existing lane change prediction models cannot update their model to adapt to the change of road and traffic environment, such as free and congested traffic environments and roads with different levels. Thus, those models cannot be robust. This paper aims to break this limitation and builds a lane change prediction model for surrounding vehicles based on machine learning methods. The lane change prediction model contains a basic model and an adaptive model: the basic model is a long short-term memory (LSTM) based prediction model which reflects the decision-making mode for drivers; the adaptive prediction model embeds an adaptive decision threshold on the basic model, and the threshold updates by Bayesian Inference method on time. We prove the performance of the adaptive model based on the HighD dataset, and the results are inspiring that the model achieves 93.64–97.52% accuracies for target-vehicle in the left adjacent lane and 94.30–98.01% accuracies for target-vehicle in the right adjacent lane. Besides, our proposed model is capable of different driving environments, especially when the traditional LSTM method cannot capture drivers' lane change decision well. Because of the timeliness and transferability nature of our proposed model, this model could be applied to the Advanced Driver Assistance System (ADAS) and autonomous vehicles in the future to reduce driving perception errors. [ABSTRACT FROM AUTHOR]

The extraction and analysis of driving style are essential for a comprehensive understanding of human driving behaviours. Most existing studies rely on subjective questionnaires and specific experiments, posing challenges in accurately capturing authentic characteristics of group drivers in naturalistic driving scenarios. As scenario-oriented naturalistic driving data collected by advanced sensors becomes increasingly available, the application of data-driven methods allows for a exhaustive analysis of driving styles across multiple drivers. Following a theoretical differentiation of driving ability, driving performance, and driving style with essential clarifications, this paper proposes a quantitative determination method grounded in large-scale naturalistic driving data. Initially, this paper defines and derives driving ability and driving performance through trajectory optimisation modelling considering various cost indicators. Subsequently, this paper proposes an objective driving style extraction method grounded in the Gaussian mixture model. In the experimental phase, this study employs the proposed framework to extract both driving abilities and performances from the Waymo motion dataset, subsequently determining driving styles. This determination is accomplished through the establishment of quantifiable statistical distributions designed to mirror data characteristics. Furthermore, the paper investigates the distinctions between driving styles in different scenarios, utilising the Jensen–Shannon divergence and the Wilcoxon rank-sum test. The empirical findings substantiate correlations between driving styles and specific scenarios, encompassing both congestion and non-congestion as well as intersection and non-intersection scenarios. • A quantitative method of driving performance through trajectory costs is proposed. • A general driving style extraction method for the large-scale dataset is proposed. • Driving styles of different traffic scenarios are significantly different. [ABSTRACT FROM AUTHOR]

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs. • Propose a safety-enhanced eco-driving strategy for CAVs under mixed traffic flow. • Develop two algorithms to alleviate intersection queuing effects for eco-driving. • While ensuring traffic efficiency, optimize CAVs' fuel consumption and driving risk. • Perform numerical experiments to assess the benefits of the proposed framework. • Reveal conventional eco-driving strategy may result in safety risks. [ABSTRACT FROM AUTHOR]

• Driver route choice behavior in the connected vehicle environment is investigated. • The heterogeneity of drivers in attention to fuel consumption and travel time is considered. • Driving experience and the real-time information are taken into considered. • Multi-agent simulation approach is used to describe the traffic system. Drivers can move with the real-time traffic information under the connected vehicle environment. Meanwhile, individual driving behavior (e.g., route choice) can influence the traffic state of the connected vehicle. This study contributes to investigate the relationship between the performance of traffic system and the driver route choice behavior in the connected vehicle environment. Especially, the heterogeneity of drivers in attention to fuel consumption and travel time is considered. A dynamic process of commuter day-to-day route choice behavior under the connected vehicle environment was proposed, the driving experience and the real-time information that affect the driver route choice are taken into considered simultaneously. Using the multi-agent simulation approach, the microcosmic and macroscopic behaviors of driver-vehicle-unit agents were described. A series of simulation experiments were conducted under the different percentages of drivers who pay enough attention to fuel consumption, and then simulation results were compared. The simulation results demonstrated that the performance of traffic state is better when enough drivers put emphasis on fuel consumption, and the optimal percentage of these drivers is about 60%. It is indicated that the travel time is more sensitive to the change of traffic state than fuel consumption, and the traffic system with enough drivers who care the fuel consumption could bring the better performance. This study has a guidance function to the application and promotion of eco-driving for government to optimize the structure of different types of drivers in the connected vehicle environment. [ABSTRACT FROM AUTHOR]

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts. [ABSTRACT FROM AUTHOR]

• A driving simulator study examined the effects of time budget and task on take-over performance for lower crash risk (LCR) and high crash risk (HCR) drivers. • LCR drivers had shorter brake reaction time compared to HCR drivers. • Reading the news and watching a video seem to have a similar effect on the drivers' workload. Understanding driver behavior of conditionally automated driving is necessary to ensure a safe transition from automated to manual driving. This study aimed to examine the difference in take-over performance between high crash risk (HCR) and lower crash risk (LCR) drivers in emergency take-over situations during conditionally automated driving. In the current simulator study, a 3 × 3 (within-subjects) factorial design was used, including the task factors (no task, reading the news, and watching a video) and time budget factors (time budget = 3 s, 4 s, and 5 s). Forty-eight participants completed a test drive on an approximately 10 km long two-way six-lane urban road. The participants firstly were in manual control and then switched to the automated driving mode at a speed of 50 km/h. The automated driving system was able to detect a broken car in the ego-lane and requested the driver to take over the control of the vehicle. There are at least one or two other vehicles or motorcycles on each side of the ego-vehicle, resulting in fewer escape paths. For the two non-handheld non-driving-related tasks (NDRTs), the participants were asked to be fully engaged in a task without any need to monitor the road environments. Each participant completed nine emergency take-over situations. The participants were classified into two groups that were labeled LCR (N ≤ 2) and HCR drivers (N ≥ 3) according to the number of accidents per driver. The results show that LCR drivers had shorter brake reaction time compared to HCR drivers. For all drivers, the engagement in a task led to longer response times, and the time budget affected the longitudinal vehicle control. In addition, the task affected the response times for LCR and HCR drivers, but only the time budget affected the longitudinal vehicle control for LCR drivers. For all drivers, LCR and HCR drivers, the time budget and task affected the safety of take-over. Especially, the two non-handheld everyday tasks seem to have a similar effect on the drivers' workload. Therefore, the HCR drivers had a lower hazard perception compared to the LCR drivers, and the factor regarding the individual difference of driving ability in take-over situations should be considered to design safe take-over concepts for automated vehicles. [ABSTRACT FROM AUTHOR]

Deploying dedicated lanes for automated vehicles (AVs) can effectively alleviate the coordination issues between AVs and manual vehicles (MVs). However, AV platoons running on dedicated AV lanes (DAVLs) have a prominent collective behavior characteristic of small inter-vehicle distance. The nearby MV drivers' imitation of this characteristic may reduce their car-following time headway (THW). The researchers conducted a simulation experiment to investigate the influence of DAVL assignments, inter-vehicle distances of AV platoons and AV platoon speed on the car-following performance of nearby MV drivers. The data of mean THW, standard deviation of THW, standard deviation of lateral position, standard deviation of velocity, standard deviation of horizontal gaze position and mean saccadic peak velocity were collected from 36 participants. Statistical analysis results show that the three factors considerably affected the MV drivers' car-following performance. In particular, the MV drivers showed a worse car-following safety but a better driving stability when the left lane was dedicated to AVs than when the right lane was dedicated to AVs (Note the experiments were done in a drive-on-the-left environment.). With respect to the inter-vehicle distances of AV platoons, the MV drivers' car-following safety was poorer under the 4 m condition than that under the 10 and 18 m conditions. In addition, the MV drivers showed a poorer car-following safety and bore a larger mental workload when driving next to the AV platoons running at 110 km/h. This study may provide some suggestions for DAVLs. Assigning the right lane of a three-lane motorway as the DAVL may have a slighter negative impact on the nearby MV drivers in China. In terms of traffic management in DAVLs, the inter-vehicle distance of AV platoons can be reduced to 10 m, and the speed of AVs should not be higher than the design speed of adjacent MV lanes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The takeover process in level 3 automated driving determines the controllability of the functions of automated vehicles and thereby traffic safety. In this study, we attempted to explain drivers' takeover performance variation in a level 3 automated vehicle in consideration of the effects of trust, system characteristics, environmental characteristics, and driver characteristics with a structural equation model. The model was built by incorporating drivers' takeover time and quality as endogenous variables. A theoretical framework of the model was hypothesized on the basis of the ACT-R cognitive architecture and relevant research results. The validity of the model was confirmed using data collected from 136 driving simulator samples under the condition of voluntary non-driving-related tasks. Results revealed that takeover time budget was the most critical factor in promoting the safety and stability of takeover process, which, together with traffic density, drivers' age and manual driving experience, determined drivers' takeover quality directly. In addition, the pre-existing experience with an automated system or a similar technology and self-confidence of the driver, as well as takeover time budget, strongly influenced the takeover time directly. Apart from the direct effects mentioned above, trust, as an intermediary variable, explained a major portion of the variance in takeover time. Theoretically, these findings suggest that takeover behavior could be comprehensively evaluated from the two dimensions of takeover time and quality through the combination of trust, driver characteristics, environmental characteristics, and vehicle characteristics. The influence mechanism of the above factors is complex and multidimensional. In addition to the form of direct influence, trust, as an intermediary variable, could reflect the internal mechanism of the takeover behavior variation. Practically, the findings emphasize the crucial role of trust in the change in takeover behavior through the dimensions of subjective trust level and monitoring strategy, which may provide new insights into the function design of takeover process., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Quinine (QN) remains an effective drug for malaria treatment. However, quinine resistance (QNR) in Plasmodium falciparum has been reported in many malaria-endemic regions particularly in African countries. Genetic polymorphism of the P. falciparum Na + /H + exchanger (pfnhe1) is considered to influence QN susceptibility. Here, ms4760 alleles of pfnhe1 were analysed from imported African P. falciparum parasites isolated from returning travellers in Wuhan, Central China., Methods: A total of 204 dried-blood spots were collected during 2011-2016. The polymorphisms of the pfnhe1 gene were determined using nested PCR with DNA sequencing., Results: Sequences were generated for 99.51% (203/204) of the PCR products and 68.63% (140/204) of the isolates were analysed successfully for the pfnhe1 ms4760 haplotypes. In total, 28 distinct ms4760 alleles containing 0 to 5 DNNND and 1 to 3 NHNDNHNNDDD repeats were identified. For the alleles, ms4760-1 (22.86%, 32/140), ms4760-3 (17.86%, 25/140), and ms4760-7 (10.71%, 15/140) were the most prevalent profiles. Furthermore, 5 undescribed ms4760 alleles were reported., Conclusions: The study offers an initial comprehensive analysis of pfnhe1 ms4760 polymorphisms from imported P. falciparum isolates in Wuhan. Pfnhe1 may constitute a good genetic marker to evaluate the prevalence of QNR in malaria-endemic and non-endemic regions.

A critical phenomenon is an intrinsic feature of traffic dynamics, during which transition between isolated local flows and global flows occurs. However, very little attention has been given to the question of how the local flows in the roads are organized collectively into a global city flow. Here we characterize this organization process of traffic as "traffic percolation," where the giant cluster of local flows disintegrates when the second largest cluster reaches its maximum. We find in real-time data of city road traffic that global traffic is dynamically composed of clusters of local flows, which are connected by bottleneck links. This organization evolves during a day with different bottleneck links appearing in different hours, but similar in the same hours in different days. A small improvement of critical bottleneck roads is found to benefit significantly the global traffic, providing a method to improve city traffic with low cost. Our results may provide insights on the relation between traffic dynamics and percolation, which can be useful for efficient transportation, epidemic control, and emergency evacuation.