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
Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software pack...Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.展开更多
Critical transitions in ecosystems may imply risks of unexpected collapse under climate changes,especially vegetation often responds sensitively to climate change.The type of vegetation ecosystem states could present ...Critical transitions in ecosystems may imply risks of unexpected collapse under climate changes,especially vegetation often responds sensitively to climate change.The type of vegetation ecosystem states could present alternative stable states,and its type could signal the critical transitions at tipping points because of changed climate or other drivers.This study analyzed the distribution of four key vegetation ecosystem types:desert,grassland,forest-steppe ecotone and forest,in Tibetan Plateau in China,using the latent class analysis method based on remote sensing data and climate data.This study analyzed the impacts of three key climate factors,precipitation,temperature,and sunshine duration,on the vegetation states,and calculated the critical transition tipping point of potential changes in vegetation type in Tibetan Plateau with the logistic regression model.The studied results showed that climatic factors greatly affect the vegetation states and vulnerability of the Tibetan Plateau.In comparison with temperature and sunshine duration,precipitation shows more obvious impact on differentiations of the vegetations status probability.The precipitation tipping point for desert and grassland transition is averagely 48.0 mm/month,70.7 mm/month for grassland and forest-steppe ecotone,and 115.0 mm/month for forest-steppe ecotone and forest.Both temperature and sunshine duration only show different probability change between vegetation and non-vegetation type,but produce opposite impacts.In Tibetan Plateau,the transition tipping points of vegetation and nonvegetation are about 12.1°C/month and 173.6 h/month for the temperature and sunshine duration,respectively.Further,vulnerability maps calculated with the logistic regression results presented the distribution of vulnerability of Tibetan Plateau key ecosystems.The vulnerability of the typical ecosystems in the Tibetan Plateau is low in the southeast and is high in the northwest.The meteorological factors affect tree cover as well as the transition probability that occurs in different vegetation states.This study can provide reference for local government agencies to formulate regional development strategies and environmental protection laws and regulations.展开更多
Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places.Colleg...Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places.College students with left-behind experience showed higher aggression levels.To further explore the relationship between left-behind experience and aggression,the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression.One thousand twenty-eight Chinese college students with left-behind experience were recruited,and their aggression levels were assessed.The results showed that there were four categories of left-behind experience:“starting from preschool,frequent contact”(35.5%),“less than 10 years in duration,limited contact”(27.0%),“starting from preschool,over 10 years in duration,limited contact”(10.9%),and“starting from school age,frequent contact”(26.6%).Overall,college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did.Females were less aggressive than males in the“starting from preschool,frequent contact”left-behind situation,while males were less aggressive than females in the“starting from school age,frequent contact”situation.Thesefindings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience.Meanwhile,gender is an important factor in this relationship.展开更多
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supported by the Yonsei University Research Fund of 2021(2021-22-0060).
文摘Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.
基金supported in part by the National Key R&D Program of China(Grant No.2017YFA0604804)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA20020402)the National Natural Science Foundation of China(Grant NO.42171079)。
文摘Critical transitions in ecosystems may imply risks of unexpected collapse under climate changes,especially vegetation often responds sensitively to climate change.The type of vegetation ecosystem states could present alternative stable states,and its type could signal the critical transitions at tipping points because of changed climate or other drivers.This study analyzed the distribution of four key vegetation ecosystem types:desert,grassland,forest-steppe ecotone and forest,in Tibetan Plateau in China,using the latent class analysis method based on remote sensing data and climate data.This study analyzed the impacts of three key climate factors,precipitation,temperature,and sunshine duration,on the vegetation states,and calculated the critical transition tipping point of potential changes in vegetation type in Tibetan Plateau with the logistic regression model.The studied results showed that climatic factors greatly affect the vegetation states and vulnerability of the Tibetan Plateau.In comparison with temperature and sunshine duration,precipitation shows more obvious impact on differentiations of the vegetations status probability.The precipitation tipping point for desert and grassland transition is averagely 48.0 mm/month,70.7 mm/month for grassland and forest-steppe ecotone,and 115.0 mm/month for forest-steppe ecotone and forest.Both temperature and sunshine duration only show different probability change between vegetation and non-vegetation type,but produce opposite impacts.In Tibetan Plateau,the transition tipping points of vegetation and nonvegetation are about 12.1°C/month and 173.6 h/month for the temperature and sunshine duration,respectively.Further,vulnerability maps calculated with the logistic regression results presented the distribution of vulnerability of Tibetan Plateau key ecosystems.The vulnerability of the typical ecosystems in the Tibetan Plateau is low in the southeast and is high in the northwest.The meteorological factors affect tree cover as well as the transition probability that occurs in different vegetation states.This study can provide reference for local government agencies to formulate regional development strategies and environmental protection laws and regulations.
基金supported by the National Natural Science Foundation of China(Grant No.31800929)Fundamental Research Funds for the Central Universities(Grant No.2020NTSS42).
文摘Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places.College students with left-behind experience showed higher aggression levels.To further explore the relationship between left-behind experience and aggression,the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression.One thousand twenty-eight Chinese college students with left-behind experience were recruited,and their aggression levels were assessed.The results showed that there were four categories of left-behind experience:“starting from preschool,frequent contact”(35.5%),“less than 10 years in duration,limited contact”(27.0%),“starting from preschool,over 10 years in duration,limited contact”(10.9%),and“starting from school age,frequent contact”(26.6%).Overall,college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did.Females were less aggressive than males in the“starting from preschool,frequent contact”left-behind situation,while males were less aggressive than females in the“starting from school age,frequent contact”situation.Thesefindings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience.Meanwhile,gender is an important factor in this relationship.
