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
Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping pr...Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.展开更多
The aim of analysis is to understand how unreliable information influences user behaviour and how much it discourages public transport use. For this purpose, a Stated Preference Survey was carried out in order to know...The aim of analysis is to understand how unreliable information influences user behaviour and how much it discourages public transport use. For this purpose, a Stated Preference Survey was carried out in order to know the preferences of public transport users relating to information needs and uncertainty on the information provided by Advanced Traveller Information System (ATIS). The perceived uncertainty is defined as information inaccuracy. In our study, we considered the difference between forecasted or scheduled waiting time at the bus stop and/or metro station provided by ATIS, and that experienced by user, to catch the bus and/or metro. A questionnaire was submitted to an appropriate sample of Palermo’s population. A Latent Class Logit model was calibrated, taking into account attributes of cost, information inaccuracy, travel time, waiting time, and cut-offs in order to reveal preference heterogeneity in the perceived information. The calibrated model showed various sources of preference heterogeneity in the perceived information of public transport users as highlighted by the analysis reported. Finally, the willingness to pay was estimated, confirming a great sensitivity to the perceived information, provided by ATIS.展开更多
The standard ordered response model (SORM) is a common disaggregate approach with ordered outcomes in which the effects of various exogenous attributes are assumed constant across ordinal choices. In this study, an in...The standard ordered response model (SORM) is a common disaggregate approach with ordered outcomes in which the effects of various exogenous attributes are assumed constant across ordinal choices. In this study, an innovative latent class based generalized ordered response model (LC-GORM) is formulated and used to assess the effects of various factors on respondents’ choice behavior with respect to congestion charge proposal for Jakarta, Indonesia. The proposed model probabilistically assigns respondents into selfish and altruistic class memberships (latently) based on their knowledge of the proposed scheme and their specific attributes. Aiming to capture observable preference heterogeneity across ordinal choices and allow the thresholds to be varied across observations, we parameterize the thresholds as a linear function of the exogenous variables for each ordinal preference. Using stated preference data collected in Jakarta in December 2013, we incorporate the influence of a comprehensive set of explanatory variables into four categories: charges, latent variables related to respondent’s psychological motivations, mobility attributes and socio-demographic characteristics. Empirical results obviously verify the existence of preference heterogeneity across outcomes. The findings confirm that the altruistic class are more sensitive with respect to acceptance of the scheme, while the selfish class are more sensitive with respect to rejection. The key factors influencing public acceptability include the charge level and respondent variables such as car dependency, awareness of the problem of cars in society, frequency of visits to the city center and frequency of private mode usage.展开更多
Background:There is little literature describing the artificial intelligence(AI)-aided diagnosis of severe pneumonia(SP)subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy.Th...Background:There is little literature describing the artificial intelligence(AI)-aided diagnosis of severe pneumonia(SP)subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy.The aim of our study is to illustrate whether clinical and biological heterogeneity,such as ventilation and gas-exchange,exists among patients with SP using chest computed tomography(CT)-based AI-aided latent class analysis(LCA).Methods:This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1,2015 to May 30,2020.AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population.The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models,and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods.Results:The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes.Patients with subphenotype-1 had milder infections(P<0.001)than patients with subphenotype-2 and had lower 30-day(P<0.001)and 90-day(P<0.001)mortality,and lower in-hospital(P=0.001)and 2-year(P<0.001)mortality.Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume(used to quantify ventilation)and oxygen saturation(used to reflect gas exchange),compared with patients with subphenotype-2.There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes(P<0.001).Compared with patients with subphenotype-2,those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation,and their clinical outcomes were effectively improved after receiving invasive ventilation treatment.Conclusions:A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function.Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.展开更多
Drivers on long interstate bridges often encounter unique challenges,including restricted lane widths,inadequate shoulders,and a lack of clear zones for safe recovery.Studies on understanding the factors that contribu...Drivers on long interstate bridges often encounter unique challenges,including restricted lane widths,inadequate shoulders,and a lack of clear zones for safe recovery.Studies on understanding the factors that contribute to crash severity on such high-risk sections of interstates are limited.This research study applies latent class clustering(LCC)to detect homogeneous clusters while accounting for unobserved heterogeneity in a dataset of 10036 crashes that occurred over a 6-year period(2015–2020)on eight selected bridges.Utilizing the LCC method,the research identifies four optimal clusters in bridge crashes,characterized by attributes such as 04-lane0,06-lane0,0single-vehicle crashes0,and 0 unknown driver0.The association rule mining(ARM)approach is used to identify the important col-lective factors to visible injury(KAB–fatal,severe,and moderate)and property damage only(PDO or no injury).In Cluster 1(4-lane),KAB and PDO crashes differ in collision type and visibility conditions,with rear-end crashes linked to KAB and sideswipe crashes to PDO.Cluster 2(6-lane)shows similar distinctions but lacks specific lighting associations for PDO.In Cluster 3(single-vehicle crashes),KAB involves moderate traffic and low visi-bility,while PDO has lower speed limits and non-dry surfaces.Cluster 4(unknown driver),despite overrepresenting hit-and-run cases,underscores challenges in injury crash data collection in high-volume mobility scenarios.The discussions of the findings on the sever-ity factors in this study are expected to help traffic safety engineers,policymakers,and planners to identify effective safety countermeasures on major elevated sections.展开更多
This study develops a flexible latent class model(LCM)to investigate the electric vehicle(EV)type choice decisions of Halifax residents.It utilizes cross-sectional data from the 2022 Halifax Travel Activity(HaliTRAC)s...This study develops a flexible latent class model(LCM)to investigate the electric vehicle(EV)type choice decisions of Halifax residents.It utilizes cross-sectional data from the 2022 Halifax Travel Activity(HaliTRAC)survey,which includes questions related to EV adoption.This study also analyzes eight attitudes and lifestyle preferences related state-ments using the principal component analysis(PCA)technique,and finally extracts three components labeled as“EV enthusiasts”,“sustainable travellers”,and“remote work arrangement admirers”.This paper explores the heterogeneity between two classes for dif-ferent alternative vehicle type choices,e.g.,battery electric vehicle(BEV),plug-in hybrid electric vehicle(PHEV),hybrid electric vehicle(HEV),and regular internal combustion engine(ICE)vehicle.Based on class membership attributes,class-1 can be identified as those who live in suburban areas,have a large family with high vehicle ownership,and are interested in travelling with their family members,especially with their children and vice-versa for class-2.Results suggest that variables across two classes portray heterogene-ity,e.g.,full-time worker portray positive correlation for class-1 and negative to class-2;high annual household income group(more than$200000)exhibit high propensity to choose BEV in class-2 and vice-versa for class-1.Sustainable travelers emphasize the adverse connection towards regular vehicles,while EV enthusiasts demonstrate a favorable association with embracing any type of EV(e.g.,BEV,PHEV,or HEV).Furthermore,the find-ings from this analysis provide guidance for policy measures such as offering purchase incentives,expanding charging infrastructure,and implementing tax rebates to promote the uptake of EVs among the residents of Halifax.展开更多
Sweden has witnessed an increase in the rates of sexual crimes including rape.Knowledge of who the offenders of these crimes are is therefore of importance for prevention.We aimed to study characteristics of individua...Sweden has witnessed an increase in the rates of sexual crimes including rape.Knowledge of who the offenders of these crimes are is therefore of importance for prevention.We aimed to study characteristics of individuals convicted of rape,aggravated rape,attempted rape or attempted aggravated rape(abbreviated rape+),against a woman≥18years of age,in Sweden.By using information from the Swedish Crime Register,offenders between 15 and 60years old convicted of rapeþbetween 2000 and 2015 were included.Information on substance use disorders,previous criminality and psychiatric disorders were retrieved from Swedish population-based registers,and Latent Class Analysis(LCA)was used to identify classes of rapeþoffenders.A total of 3039 offenders were included in the analysis.A major-ity of them were immigrants(n=1800;59.2%)of which a majority(n=1451;47.7%)were born outside of Sweden.The LCA identified two classes:Class A-low offending class(LOC),and Class B—high offending class(HOC).While offenders in the LOC had low rates of previous criminality,psychiatric disorders and substance use disorders,those included in the HOC had high rates of previous criminality,psychiatric disorders and substance use dis-orders.While HOC may be composed by more“traditional”criminals probably known by the police,the LOC may represent individuals not previously known by the police.These two separated classes,as well as our finding in regard to a majority of the offenders being immi-grants,warrants further studies that take into account the contextual characteristics among these offenders.展开更多
Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid con...Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.展开更多
Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategie...Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.展开更多
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Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.
文摘The aim of analysis is to understand how unreliable information influences user behaviour and how much it discourages public transport use. For this purpose, a Stated Preference Survey was carried out in order to know the preferences of public transport users relating to information needs and uncertainty on the information provided by Advanced Traveller Information System (ATIS). The perceived uncertainty is defined as information inaccuracy. In our study, we considered the difference between forecasted or scheduled waiting time at the bus stop and/or metro station provided by ATIS, and that experienced by user, to catch the bus and/or metro. A questionnaire was submitted to an appropriate sample of Palermo’s population. A Latent Class Logit model was calibrated, taking into account attributes of cost, information inaccuracy, travel time, waiting time, and cut-offs in order to reveal preference heterogeneity in the perceived information. The calibrated model showed various sources of preference heterogeneity in the perceived information of public transport users as highlighted by the analysis reported. Finally, the willingness to pay was estimated, confirming a great sensitivity to the perceived information, provided by ATIS.
文摘The standard ordered response model (SORM) is a common disaggregate approach with ordered outcomes in which the effects of various exogenous attributes are assumed constant across ordinal choices. In this study, an innovative latent class based generalized ordered response model (LC-GORM) is formulated and used to assess the effects of various factors on respondents’ choice behavior with respect to congestion charge proposal for Jakarta, Indonesia. The proposed model probabilistically assigns respondents into selfish and altruistic class memberships (latently) based on their knowledge of the proposed scheme and their specific attributes. Aiming to capture observable preference heterogeneity across ordinal choices and allow the thresholds to be varied across observations, we parameterize the thresholds as a linear function of the exogenous variables for each ordinal preference. Using stated preference data collected in Jakarta in December 2013, we incorporate the influence of a comprehensive set of explanatory variables into four categories: charges, latent variables related to respondent’s psychological motivations, mobility attributes and socio-demographic characteristics. Empirical results obviously verify the existence of preference heterogeneity across outcomes. The findings confirm that the altruistic class are more sensitive with respect to acceptance of the scheme, while the selfish class are more sensitive with respect to rejection. The key factors influencing public acceptability include the charge level and respondent variables such as car dependency, awareness of the problem of cars in society, frequency of visits to the city center and frequency of private mode usage.
基金supported by the National Natural Science Foundation of China(Nos.82172138 and 81873947).
文摘Background:There is little literature describing the artificial intelligence(AI)-aided diagnosis of severe pneumonia(SP)subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy.The aim of our study is to illustrate whether clinical and biological heterogeneity,such as ventilation and gas-exchange,exists among patients with SP using chest computed tomography(CT)-based AI-aided latent class analysis(LCA).Methods:This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1,2015 to May 30,2020.AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population.The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models,and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods.Results:The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes.Patients with subphenotype-1 had milder infections(P<0.001)than patients with subphenotype-2 and had lower 30-day(P<0.001)and 90-day(P<0.001)mortality,and lower in-hospital(P=0.001)and 2-year(P<0.001)mortality.Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume(used to quantify ventilation)and oxygen saturation(used to reflect gas exchange),compared with patients with subphenotype-2.There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes(P<0.001).Compared with patients with subphenotype-2,those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation,and their clinical outcomes were effectively improved after receiving invasive ventilation treatment.Conclusions:A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function.Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
基金funded by the Louisiana Department of Transportation(DOTD)and Development and the Louisiana Transportation Research Center(LTRC),Grant number DOTLT1000341.
文摘Drivers on long interstate bridges often encounter unique challenges,including restricted lane widths,inadequate shoulders,and a lack of clear zones for safe recovery.Studies on understanding the factors that contribute to crash severity on such high-risk sections of interstates are limited.This research study applies latent class clustering(LCC)to detect homogeneous clusters while accounting for unobserved heterogeneity in a dataset of 10036 crashes that occurred over a 6-year period(2015–2020)on eight selected bridges.Utilizing the LCC method,the research identifies four optimal clusters in bridge crashes,characterized by attributes such as 04-lane0,06-lane0,0single-vehicle crashes0,and 0 unknown driver0.The association rule mining(ARM)approach is used to identify the important col-lective factors to visible injury(KAB–fatal,severe,and moderate)and property damage only(PDO or no injury).In Cluster 1(4-lane),KAB and PDO crashes differ in collision type and visibility conditions,with rear-end crashes linked to KAB and sideswipe crashes to PDO.Cluster 2(6-lane)shows similar distinctions but lacks specific lighting associations for PDO.In Cluster 3(single-vehicle crashes),KAB involves moderate traffic and low visi-bility,while PDO has lower speed limits and non-dry surfaces.Cluster 4(unknown driver),despite overrepresenting hit-and-run cases,underscores challenges in injury crash data collection in high-volume mobility scenarios.The discussions of the findings on the sever-ity factors in this study are expected to help traffic safety engineers,policymakers,and planners to identify effective safety countermeasures on major elevated sections.
文摘This study develops a flexible latent class model(LCM)to investigate the electric vehicle(EV)type choice decisions of Halifax residents.It utilizes cross-sectional data from the 2022 Halifax Travel Activity(HaliTRAC)survey,which includes questions related to EV adoption.This study also analyzes eight attitudes and lifestyle preferences related state-ments using the principal component analysis(PCA)technique,and finally extracts three components labeled as“EV enthusiasts”,“sustainable travellers”,and“remote work arrangement admirers”.This paper explores the heterogeneity between two classes for dif-ferent alternative vehicle type choices,e.g.,battery electric vehicle(BEV),plug-in hybrid electric vehicle(PHEV),hybrid electric vehicle(HEV),and regular internal combustion engine(ICE)vehicle.Based on class membership attributes,class-1 can be identified as those who live in suburban areas,have a large family with high vehicle ownership,and are interested in travelling with their family members,especially with their children and vice-versa for class-2.Results suggest that variables across two classes portray heterogene-ity,e.g.,full-time worker portray positive correlation for class-1 and negative to class-2;high annual household income group(more than$200000)exhibit high propensity to choose BEV in class-2 and vice-versa for class-1.Sustainable travelers emphasize the adverse connection towards regular vehicles,while EV enthusiasts demonstrate a favorable association with embracing any type of EV(e.g.,BEV,PHEV,or HEV).Furthermore,the find-ings from this analysis provide guidance for policy measures such as offering purchase incentives,expanding charging infrastructure,and implementing tax rebates to promote the uptake of EVs among the residents of Halifax.
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme[grant number 787592].
文摘Sweden has witnessed an increase in the rates of sexual crimes including rape.Knowledge of who the offenders of these crimes are is therefore of importance for prevention.We aimed to study characteristics of individuals convicted of rape,aggravated rape,attempted rape or attempted aggravated rape(abbreviated rape+),against a woman≥18years of age,in Sweden.By using information from the Swedish Crime Register,offenders between 15 and 60years old convicted of rapeþbetween 2000 and 2015 were included.Information on substance use disorders,previous criminality and psychiatric disorders were retrieved from Swedish population-based registers,and Latent Class Analysis(LCA)was used to identify classes of rapeþoffenders.A total of 3039 offenders were included in the analysis.A major-ity of them were immigrants(n=1800;59.2%)of which a majority(n=1451;47.7%)were born outside of Sweden.The LCA identified two classes:Class A-low offending class(LOC),and Class B—high offending class(HOC).While offenders in the LOC had low rates of previous criminality,psychiatric disorders and substance use disorders,those included in the HOC had high rates of previous criminality,psychiatric disorders and substance use dis-orders.While HOC may be composed by more“traditional”criminals probably known by the police,the LOC may represent individuals not previously known by the police.These two separated classes,as well as our finding in regard to a majority of the offenders being immi-grants,warrants further studies that take into account the contextual characteristics among these offenders.
基金funded by University of Transport and Commu-nications (UTC) (Grant No.T2019-CT-06TD).
文摘Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.
基金funding from the China National Key Research and Development Program(No.2023YFC3603104)the National Natural Science Foundation of China(Nos.82472243 and 82272180)+6 种基金the Fundamental Research Funds for the Central Universities(No.226-2025-00024)the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LHDMD24H150001)the Key Research&Development Project of Zhejiang Province(No.2024C03240)a collaborative scientific project co-established by the Science and Technology Department of the National Administration of Traditional Chinese Medicine and the Zhejiang Provincial Administration of Traditional Chinese Medicine(No.GZY-ZJ-KJ-24082)he General Health Science and Technology Program of Zhejiang Province(No.2024KY1099)the Project of Zhejiang University Longquan Innovation Center(No.ZJDXLQCXZCJBGS2024016)Wu Jieping Medical Foundation Special Research Grant(No.320.6750.2024-23-07).
文摘Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.
