In this study, the rapidity distribution, collective flows, and nuclear stopping power in ^(197)Au+^(197)Au collisions at intermediate energies were investigated using the ultrarelativistic quantum molecular dynamics(...In this study, the rapidity distribution, collective flows, and nuclear stopping power in ^(197)Au+^(197)Au collisions at intermediate energies were investigated using the ultrarelativistic quantum molecular dynamics(UrQMD) model with GEMINI++ code. The UrQMD model was adopted to simulate the dynamic evolution of heavy-ion collisions, whereas the GEMINI++ code was used to simulate the decay of primary fragments produced by UrQMD. The calculated results were compared with the INDRA and FOPI experimental data. It was found that the rapidity distribution, collective flows, and nuclear stopping power were affected to a certain extent by the decay of primary fragments, especially at lower beam energies. Furthermore, the experimental data of the collective flows and nuclear stopping power at the investigated beam energies were better reproduced when the sequential decay effect was included.展开更多
Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic si...Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.展开更多
Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biase...Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.展开更多
Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous acti...Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activ-ity.Therefore,a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor(NIoTS)and transfer learning is proposed.First,the NIoTS is installed in the athlete’s leg muscles to collect activity signals.Second,the noise component in the activity signal is removed using the de-noising method based on mathematical morphology.Finally,the depth feature of the activity signal is extracted through the deep transfer learning model,and the Euclidean distance between the extracted feature and the depth feature of the frontal emergency stops dangerous activity signal is compared.If the European distance is small,it can be judged as the frontal emergency stops dangerous activity,and the frontal emergency stops dangerous activity recognition is realized.The results show that the average time delay of activity signal acquisition of the algorithm is low,the signal-to-noise ratio of the action signal is high,and the activity signal mean square error is low.The variance of the frontal emergency stops dangerous activity recognition does not exceed 0.5.The difference between the appearance time of the dangerous activity and the recognition time of the algorithm is 0.15 s,it can accurately and quickly recognize the frontal emergency stops the dangerous activity.展开更多
Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian...Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian inference, the flow of subsequent research was to make the mapping formula multidimensional, to introduce the concept of time, and to derive a Markov (decision) process. Furthermore, we formulated stochastic differential equations to derive them [2]. This paper refers to type 2 vague events based on a second-order mapping equation. This quadratic mapping formula gives a certain rotation named as possibility principal factor rotation by transforming a non-mapping function by a relation between two mapping functions. In addition, the derivation of the Type 2 Complex Markov process and the initial and stopping conditions in this rotation are mentioned. .展开更多
基金partly supported by the National Natural Science Foundation of China (Nos. U2032145 and 11875125)the National Key Research and Development Program of China (No. 2020YFE0202002)。
文摘In this study, the rapidity distribution, collective flows, and nuclear stopping power in ^(197)Au+^(197)Au collisions at intermediate energies were investigated using the ultrarelativistic quantum molecular dynamics(UrQMD) model with GEMINI++ code. The UrQMD model was adopted to simulate the dynamic evolution of heavy-ion collisions, whereas the GEMINI++ code was used to simulate the decay of primary fragments produced by UrQMD. The calculated results were compared with the INDRA and FOPI experimental data. It was found that the rapidity distribution, collective flows, and nuclear stopping power were affected to a certain extent by the decay of primary fragments, especially at lower beam energies. Furthermore, the experimental data of the collective flows and nuclear stopping power at the investigated beam energies were better reproduced when the sequential decay effect was included.
文摘Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.
文摘Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.
文摘Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activ-ity.Therefore,a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor(NIoTS)and transfer learning is proposed.First,the NIoTS is installed in the athlete’s leg muscles to collect activity signals.Second,the noise component in the activity signal is removed using the de-noising method based on mathematical morphology.Finally,the depth feature of the activity signal is extracted through the deep transfer learning model,and the Euclidean distance between the extracted feature and the depth feature of the frontal emergency stops dangerous activity signal is compared.If the European distance is small,it can be judged as the frontal emergency stops dangerous activity,and the frontal emergency stops dangerous activity recognition is realized.The results show that the average time delay of activity signal acquisition of the algorithm is low,the signal-to-noise ratio of the action signal is high,and the activity signal mean square error is low.The variance of the frontal emergency stops dangerous activity recognition does not exceed 0.5.The difference between the appearance time of the dangerous activity and the recognition time of the algorithm is 0.15 s,it can accurately and quickly recognize the frontal emergency stops the dangerous activity.
文摘Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian inference, the flow of subsequent research was to make the mapping formula multidimensional, to introduce the concept of time, and to derive a Markov (decision) process. Furthermore, we formulated stochastic differential equations to derive them [2]. This paper refers to type 2 vague events based on a second-order mapping equation. This quadratic mapping formula gives a certain rotation named as possibility principal factor rotation by transforming a non-mapping function by a relation between two mapping functions. In addition, the derivation of the Type 2 Complex Markov process and the initial and stopping conditions in this rotation are mentioned. .
文摘减轻损失规则的重要内容是减轻损失义务,包含两方面的意思:第一,原告有义务采取积极措施(positive steps)将被告违约所致损失降至最低范围;第二,在被告适当履行合同的情况下,原告实施某些行为是适当的,但在被告已经违约的场合,原告倘若再实施这些行为,就会不公正地扩大违约造成的损失(unjustifiably augment the loss)。减轻损失义务大多源自法律的规定,也有基于当事人的约定产生的情形。减轻损失的措施可以类型化为停止履行、替代安排、守约方与违约方再协商、继续履行。守约方应当采取什么样减轻损失的措施,要取决于周围情事。其措施是否适当,宜以一个理性人、经济人的认识作为判断标准比较合理,并且对这种适当的要求不宜太高。在程序方面,要根据双方当事人提供的证据做出事实的认定。是否需要将选择措施之事预先通知违约方,主流观点主张,虽然通知并非“坏事”,但这充其量只是看整个行为是否合理的其中一个因素,而且不一定是重要因素。守约方采取减轻损失的措施支出的合理费用,应由违约方负责赔偿。守约方违反减轻损失的义务,不向违约方就此承担违约责任,但由此扩大的损失无权请求违约方予以赔偿。
