Based on the Regional Specialized Meteorological Center(RSMC)Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETCs)over the western North Pacif...Based on the Regional Specialized Meteorological Center(RSMC)Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETCs)over the western North Pacific(WNP)during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method(FCM)according to their track patterns.The characteristics of the six hard-clustered ETCs with the highest membership coefficient are shown.Most tropical cyclones(TCs)that were assigned to clusters C2,C5,and C6 made landfall over eastern Asian countries,which severely threatened these regions.Among landfalling TCs,93.2%completed their ET after landfall,whereas 39.8%of ETCs completed their transition within one day.The frequency of ETCs over the WNP has decreased in the past four decades,wherein cluster C5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high(WPSH).This large-scale circulation pattern is favorable for C2 and causes it to become the dominant track pattern,owning to it containing the largest number of intensifying ETCs among the six clusters,a number that has increased insignificantly over the past four decades.The surface roughness variation and three-dimensional background circulation led to C5 containing the maximum number of landfalling TCs and a minimum number of intensifying ETCs.Our results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields,which will benefit the effective monitoring of these events over the WNP.展开更多
While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning me...While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.展开更多
BACKGROUND Despite advances in research on psychopathology and social media use,no comprehensive review has examined published papers on this type of research and considered how it was affected by the coronavirus dise...BACKGROUND Despite advances in research on psychopathology and social media use,no comprehensive review has examined published papers on this type of research and considered how it was affected by the coronavirus disease 2019(COVID-19)outbreak.AIM To explore the status of research on psychopathology and social media use before and after the COVID-19 outbreak.METHODS We used Bibliometrix(an R software package)to conduct a scientometric analysis of 4588 relevant studies drawn from the Web of Science Core Collection,PubMed,and Scopus databases.RESULTS Such research output was scarce before COVID-19,but exploded after the pandemic with the publication of a number of high-impact articles.Key authors and institutions,located primarily in developed countries,maintained their core positions,largely uninfluenced by COVID-19;however,research production and collaboration in developing countries increased significantly after COVID-19.Through the analysis of keywords,we identified commonly used methods in this field,together with specific populations,psychopathological conditions,and clinical treatments.Researchers have devoted increasing attention to gender differences in psychopathological states and linked COVID-19 strongly to depression,with depression detection becoming a new trend.Developments in research on psychopathology and social media use are unbalanced and uncoordinated across countries/regions,and more indepth clinical studies should be conducted in the future.CONCLUSION After COVID-19,there was an increased level of concern about mental health issues and a changing emphasis on social media use and the impact of public health emergencies.展开更多
The characteristics and causes of a drop in temperature during a cold wave process in the early winter of 2020/2021 were analyzed.The results show that the air temperature at 700-600 hPa over China was firstly and mos...The characteristics and causes of a drop in temperature during a cold wave process in the early winter of 2020/2021 were analyzed.The results show that the air temperature at 700-600 hPa over China was firstly and mostly influenced by the cold wave process,and then the cold air gradually extended to the lower layer,causing the most severe cooling in North China and its nearby areas.During the cold wave,the longitude of the upper-level jet over the Chinese mainland was larger;the Ural blocking high and the East Asian trough were stronger,so that the geopotential height gradient between the two was also significantly larger;the meridional air flow was abnormally strong,which was conducive to the southward transport of cold air from the middle and high latitudes.Results of the diagnostic analysis further show that the outbreak of the cold wave and the negative temperature tendency anomaly in the key area were mainly caused by the meridional temperature horizontal advection anomaly,while the temperature rise accompanied by abnormal air subsidence compensated for the abnormal decrease in temperature,which was conducive to the gradual rise of temperature in the key area.展开更多
The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence...The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.展开更多
A new method of data access which can effectively resolve the problem of high speed and real time reading data of nuclear instrument in small storage space is introduced. This method applies the data storage mode of ...A new method of data access which can effectively resolve the problem of high speed and real time reading data of nuclear instrument in small storage space is introduced. This method applies the data storage mode of “linked list” to the system of Micro Control Unit (MCU), and realizes the pointer access of nuclear data on the small storage space of MCU. Experimental results show that this method can solve some problems of traditional data storage method, which has the advantages of simple program design, stable performance, accurate data, strong repeatability, saving storage space and so on.展开更多
AIM:To determine the alterations in rat enterocyte mitochondrial respiratory function and enzyme activities following traumatic brain injury(TBI).METHODS:Fifty-six male SD rats were randomly divided into seven groups(...AIM:To determine the alterations in rat enterocyte mitochondrial respiratory function and enzyme activities following traumatic brain injury(TBI).METHODS:Fifty-six male SD rats were randomly divided into seven groups(8 rats in each group):a control group(rats with sham operation)and traumatic brain injury groups at 6,12,24 h,days 2,3,and 7after operation.TBI models were induced by Feendy’s free-falling method.Mitochondrial respiratory function(respiratory control ratio and ADP/O ratio)was measured with a Clark oxygen electrode.The activities of respiratory chain complexⅠ-Ⅳand related enzymes were determined by spectrophotometry.RESULTS:Compared with the control group,the mitochondrial respiratory control ratio(RCR)declined at 6 h and remained at a low level until day 7 after TBI(control,5.42±0.46;6 h,5.20±0.18;12 h,4.55±0.35;24 h,3.75±0.22;2 d,4.12±0.53;3 d,3.45±0.41;7 d,5.23±0.24,P<0.01).The value of phosphate-to-oxygen(P/O)significantly decreased at12,24 h,day 2 and day 3,respectively(12 h,3.30±0.10;24 h,2.61±0.21;2 d,2.95±0.18;3 d,2.76±0.09,P<0.01)compared with the control group(3.46±0.12).Two troughs of mitochondrial respiratory function were seen at 24 h and day 3 after TBI.The activities of mitochondrial complex Ⅰ (6 h:110±10,12 h:115±12,24 h:85±9,day 2:80±15,day 3:65±16,P<0.01)and complexⅡ(6 h:105±8,12 h:110±92,24 h:80±10,day 2:76±8,day 3:68±12,P<0.01)were increased at 6 h and 12 h following TBI,and then significantly decreased at 24 h,day 2 and day3,respectively.However,there were no differences in complex Ⅰ andⅡactivities between the control and TBI groups.Furthermore,pyruvate dehydrogenase(PDH)activity was significantly decreased at 6 h and continued up to 7 d after TBI compared with the control group(6 h:90±8,12 h:85±10,24 h:65±12,day 2:60±9,day 3:55±6,day 7:88±11,P<0.01).The changes inα-ketoglutaric dehydrogenase(KGDH)activity were similar to PDH,except that the decrease in KGDH activity began at 12 h after TBI(12 h:90±12,24 h:80±9,day 2:76±15,day 3:68±7,day7:90±13,P<0.01).No significant change in malate dehydrogenase(MDH)activity was observed.CONCLUSION:Rat enterocyte mitochondrial respiratory function and enzyme activities are inhibited following TBI.Mitochondrial dysfunction may play an important role in TBI-induced gastrointestinal dysfunction.展开更多
In this study, a three-dimensional mesoscale model was used to numerically simulate the well-known "98.7" heavy rainfall event that affected the Yangtze Valley in July 1998. Two experiments were conducted to...In this study, a three-dimensional mesoscale model was used to numerically simulate the well-known "98.7" heavy rainfall event that affected the Yangtze Valley in July 1998. Two experiments were conducted to analyze the impact of moist processes on the development of meso-β scale vortices(MβV) and their triggering by mesoscale wind perturbation(MWP). In the experiment in which the latent heat feedback(LHF) scheme was switched off, a stable low-level col field(i.e., saddle field—a region between two lows and two highs in the isobaric surface) formed, and the MWP triggered a weak MβV. However, when the LHF scheme was switched on as the MWP was introduced into the model, the MβV developed quickly and intense rainfall and a mesoscale low-level jet(mLLJ) were generated. The thickness of the air column and average temperature between 400 and 700 hPa decreased without the feedback of latent heat, whereas they increased quickly when the LHF scheme was switched on, with the air pressure falling at low levels but rising at upper levels. A schematic representation of the positive feedbacks among the mesoscale vortex, rainfall, and mLLJ shows that in the initial stage of the MβV, the MWP triggers light rainfall and the latent heat occurs at low levels, which leads to weak convergence and ageostrophic winds. In the mature stage of the MβV, convection extends to the middle-to-upper levels, resulting in an increase in the average temperature and a stretching of the air column. A low-level cyclonic circulation forms under the effect of Coriolis torque, and the m LLJ forms to the southeast of the MβV.展开更多
A new reliable cellular automaon(CA) model designed to account for stochasticity in traffic flow induced by heterogeneity in driving behavior is presented.The proposed model differs from most existing CA models in tha...A new reliable cellular automaon(CA) model designed to account for stochasticity in traffic flow induced by heterogeneity in driving behavior is presented.The proposed model differs from most existing CA models in that this new model focuses on describing traffic phenomena by coding into its rules the key idea that a vehicle's moving state is directly determined by a driver stepping on the accelerator or on the brake(the vehicle's acceleration).Acceleration obeys a deformed continuous distribution function when considering the heterogeneity in driving behavior and the safe distance, rather than equaling a fixed acceleration value with a probability, as is the rule in many existing CA models.Simulation results show that the new proposed model is capable of reproducing empirical findings in real traffic system.Moreover, this new model makes it possible to implement in-depth analysis of correlations between a vehicle's state parameters.展开更多
Despite substantial progress in neonatal care over the past two decades leading to improved survival of extremely premature infants, extreme prematurity continues to be associated with long term neurodevelopmental imp...Despite substantial progress in neonatal care over the past two decades leading to improved survival of extremely premature infants, extreme prematurity continues to be associated with long term neurodevelopmental impairments. Cerebral white matter injury is the predominant form of insult in preterm brain leading to adverse neurological consequences. Such brain injury pattern and unfavorable neurologic sequelae is commonly encountered in premature infants exposed to systemic inflammatory states such as clinical or culture proven sepsis with or without evidence of meningitis, prolonged mechanical ventilation, bronchopulmonary dysplasia, necrotizing enterocolitis and chorioamnionitis. Underlying mechanisms may include cytokine mediated processes without direct entry of pathogens into the brain, developmental differences in immune response and complex neurovascular barrier system that play a critical role in regulating the cerebral response to various systemic inflammatory insults in premature infants. Understanding of these pathologic mechanisms and clinical correlates of such injury based on serum biomarkers or brain imaging findings on magnetic resonance imaging will pave way for future research and translational therapeutic opportunities for the developing brain.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42075053 and 41975128)。
文摘Based on the Regional Specialized Meteorological Center(RSMC)Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETCs)over the western North Pacific(WNP)during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method(FCM)according to their track patterns.The characteristics of the six hard-clustered ETCs with the highest membership coefficient are shown.Most tropical cyclones(TCs)that were assigned to clusters C2,C5,and C6 made landfall over eastern Asian countries,which severely threatened these regions.Among landfalling TCs,93.2%completed their ET after landfall,whereas 39.8%of ETCs completed their transition within one day.The frequency of ETCs over the WNP has decreased in the past four decades,wherein cluster C5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high(WPSH).This large-scale circulation pattern is favorable for C2 and causes it to become the dominant track pattern,owning to it containing the largest number of intensifying ETCs among the six clusters,a number that has increased insignificantly over the past four decades.The surface roughness variation and three-dimensional background circulation led to C5 containing the maximum number of landfalling TCs and a minimum number of intensifying ETCs.Our results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields,which will benefit the effective monitoring of these events over the WNP.
基金This research was funded by National Natural Science Foundation of China under Grant No.61806171Sichuan University of Science&Engineering Talent Project under Grant No.2021RC15+2 种基金Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computer of Sichuan Province Universities on Bridge Inspection and Engineering under Grant No.2022QYJ06Sichuan University of Science&Engineering Graduate Student Innovation Fund under Grant No.Y2023115The Scientific Research and Innovation Team Program of Sichuan University of Science and Technology under Grant No.SUSE652A006.
文摘While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.
基金Supported by Guangxi Higher Education Undergraduate Teaching Reform Project,No.2022JGA146Guangxi Educational Science Planning Key Project,No.2022ZJY2791+1 种基金Guangxi Medical University Key Textbook Construction Project,No.Gxmuzdjc2223Guangxi Medical High-Level Key Talents Training“139”Program.
文摘BACKGROUND Despite advances in research on psychopathology and social media use,no comprehensive review has examined published papers on this type of research and considered how it was affected by the coronavirus disease 2019(COVID-19)outbreak.AIM To explore the status of research on psychopathology and social media use before and after the COVID-19 outbreak.METHODS We used Bibliometrix(an R software package)to conduct a scientometric analysis of 4588 relevant studies drawn from the Web of Science Core Collection,PubMed,and Scopus databases.RESULTS Such research output was scarce before COVID-19,but exploded after the pandemic with the publication of a number of high-impact articles.Key authors and institutions,located primarily in developed countries,maintained their core positions,largely uninfluenced by COVID-19;however,research production and collaboration in developing countries increased significantly after COVID-19.Through the analysis of keywords,we identified commonly used methods in this field,together with specific populations,psychopathological conditions,and clinical treatments.Researchers have devoted increasing attention to gender differences in psychopathological states and linked COVID-19 strongly to depression,with depression detection becoming a new trend.Developments in research on psychopathology and social media use are unbalanced and uncoordinated across countries/regions,and more indepth clinical studies should be conducted in the future.CONCLUSION After COVID-19,there was an increased level of concern about mental health issues and a changing emphasis on social media use and the impact of public health emergencies.
基金Supported by the National Natural Science Foundation of China(42075053,41275099).
文摘The characteristics and causes of a drop in temperature during a cold wave process in the early winter of 2020/2021 were analyzed.The results show that the air temperature at 700-600 hPa over China was firstly and mostly influenced by the cold wave process,and then the cold air gradually extended to the lower layer,causing the most severe cooling in North China and its nearby areas.During the cold wave,the longitude of the upper-level jet over the Chinese mainland was larger;the Ural blocking high and the East Asian trough were stronger,so that the geopotential height gradient between the two was also significantly larger;the meridional air flow was abnormally strong,which was conducive to the southward transport of cold air from the middle and high latitudes.Results of the diagnostic analysis further show that the outbreak of the cold wave and the negative temperature tendency anomaly in the key area were mainly caused by the meridional temperature horizontal advection anomaly,while the temperature rise accompanied by abnormal air subsidence compensated for the abnormal decrease in temperature,which was conducive to the gradual rise of temperature in the key area.
文摘The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.
文摘A new method of data access which can effectively resolve the problem of high speed and real time reading data of nuclear instrument in small storage space is introduced. This method applies the data storage mode of “linked list” to the system of Micro Control Unit (MCU), and realizes the pointer access of nuclear data on the small storage space of MCU. Experimental results show that this method can solve some problems of traditional data storage method, which has the advantages of simple program design, stable performance, accurate data, strong repeatability, saving storage space and so on.
基金Supported by The Scientific Research Foundation of the Chinese PLA Medical Programs,No.ms031
文摘AIM:To determine the alterations in rat enterocyte mitochondrial respiratory function and enzyme activities following traumatic brain injury(TBI).METHODS:Fifty-six male SD rats were randomly divided into seven groups(8 rats in each group):a control group(rats with sham operation)and traumatic brain injury groups at 6,12,24 h,days 2,3,and 7after operation.TBI models were induced by Feendy’s free-falling method.Mitochondrial respiratory function(respiratory control ratio and ADP/O ratio)was measured with a Clark oxygen electrode.The activities of respiratory chain complexⅠ-Ⅳand related enzymes were determined by spectrophotometry.RESULTS:Compared with the control group,the mitochondrial respiratory control ratio(RCR)declined at 6 h and remained at a low level until day 7 after TBI(control,5.42±0.46;6 h,5.20±0.18;12 h,4.55±0.35;24 h,3.75±0.22;2 d,4.12±0.53;3 d,3.45±0.41;7 d,5.23±0.24,P<0.01).The value of phosphate-to-oxygen(P/O)significantly decreased at12,24 h,day 2 and day 3,respectively(12 h,3.30±0.10;24 h,2.61±0.21;2 d,2.95±0.18;3 d,2.76±0.09,P<0.01)compared with the control group(3.46±0.12).Two troughs of mitochondrial respiratory function were seen at 24 h and day 3 after TBI.The activities of mitochondrial complex Ⅰ (6 h:110±10,12 h:115±12,24 h:85±9,day 2:80±15,day 3:65±16,P<0.01)and complexⅡ(6 h:105±8,12 h:110±92,24 h:80±10,day 2:76±8,day 3:68±12,P<0.01)were increased at 6 h and 12 h following TBI,and then significantly decreased at 24 h,day 2 and day3,respectively.However,there were no differences in complex Ⅰ andⅡactivities between the control and TBI groups.Furthermore,pyruvate dehydrogenase(PDH)activity was significantly decreased at 6 h and continued up to 7 d after TBI compared with the control group(6 h:90±8,12 h:85±10,24 h:65±12,day 2:60±9,day 3:55±6,day 7:88±11,P<0.01).The changes inα-ketoglutaric dehydrogenase(KGDH)activity were similar to PDH,except that the decrease in KGDH activity began at 12 h after TBI(12 h:90±12,24 h:80±9,day 2:76±15,day 3:68±7,day7:90±13,P<0.01).No significant change in malate dehydrogenase(MDH)activity was observed.CONCLUSION:Rat enterocyte mitochondrial respiratory function and enzyme activities are inhibited following TBI.Mitochondrial dysfunction may play an important role in TBI-induced gastrointestinal dysfunction.
基金supported by the National Grand Fundamental Research 973 Program of China (Grant No.2015CB452800)the National Natural Science Foundation of China (Grant Nos.41275099,41205073 and 41275012)the Natural Science Foundation of the Nanjing Joint Center of Atmospheric Research (Grant No.NJCAR2016MS02)
文摘In this study, a three-dimensional mesoscale model was used to numerically simulate the well-known "98.7" heavy rainfall event that affected the Yangtze Valley in July 1998. Two experiments were conducted to analyze the impact of moist processes on the development of meso-β scale vortices(MβV) and their triggering by mesoscale wind perturbation(MWP). In the experiment in which the latent heat feedback(LHF) scheme was switched off, a stable low-level col field(i.e., saddle field—a region between two lows and two highs in the isobaric surface) formed, and the MWP triggered a weak MβV. However, when the LHF scheme was switched on as the MWP was introduced into the model, the MβV developed quickly and intense rainfall and a mesoscale low-level jet(mLLJ) were generated. The thickness of the air column and average temperature between 400 and 700 hPa decreased without the feedback of latent heat, whereas they increased quickly when the LHF scheme was switched on, with the air pressure falling at low levels but rising at upper levels. A schematic representation of the positive feedbacks among the mesoscale vortex, rainfall, and mLLJ shows that in the initial stage of the MβV, the MWP triggers light rainfall and the latent heat occurs at low levels, which leads to weak convergence and ageostrophic winds. In the mature stage of the MβV, convection extends to the middle-to-upper levels, resulting in an increase in the average temperature and a stretching of the air column. A low-level cyclonic circulation forms under the effect of Coriolis torque, and the m LLJ forms to the southeast of the MβV.
基金Project supported by the National Key Research and Development Program of China(Grant No.2018YFC0809900)the National Natural Science Foundation of China(Grant Nos.71774093 and 71473146)
文摘A new reliable cellular automaon(CA) model designed to account for stochasticity in traffic flow induced by heterogeneity in driving behavior is presented.The proposed model differs from most existing CA models in that this new model focuses on describing traffic phenomena by coding into its rules the key idea that a vehicle's moving state is directly determined by a driver stepping on the accelerator or on the brake(the vehicle's acceleration).Acceleration obeys a deformed continuous distribution function when considering the heterogeneity in driving behavior and the safe distance, rather than equaling a fixed acceleration value with a probability, as is the rule in many existing CA models.Simulation results show that the new proposed model is capable of reproducing empirical findings in real traffic system.Moreover, this new model makes it possible to implement in-depth analysis of correlations between a vehicle's state parameters.
文摘Despite substantial progress in neonatal care over the past two decades leading to improved survival of extremely premature infants, extreme prematurity continues to be associated with long term neurodevelopmental impairments. Cerebral white matter injury is the predominant form of insult in preterm brain leading to adverse neurological consequences. Such brain injury pattern and unfavorable neurologic sequelae is commonly encountered in premature infants exposed to systemic inflammatory states such as clinical or culture proven sepsis with or without evidence of meningitis, prolonged mechanical ventilation, bronchopulmonary dysplasia, necrotizing enterocolitis and chorioamnionitis. Underlying mechanisms may include cytokine mediated processes without direct entry of pathogens into the brain, developmental differences in immune response and complex neurovascular barrier system that play a critical role in regulating the cerebral response to various systemic inflammatory insults in premature infants. Understanding of these pathologic mechanisms and clinical correlates of such injury based on serum biomarkers or brain imaging findings on magnetic resonance imaging will pave way for future research and translational therapeutic opportunities for the developing brain.
