Swedish rape offenders——a latent class analysis

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.

gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

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.

Will the perceptions to incentive policies have any effect on users' willingness to participate in Mobility-as-a-Service?

With the increasing severity of urban traffic congestion and environmental pollution issues,Mobility-as-a-Service(MaaS)has garnered increasing attention as an emerging mode of transportation.Thus,how to motivate users to participate in MaaS has become an important research issue.This study first classified the incentive policies into four aspects:financial incentive policy,non-financial incentive policy,information policy,and convenience policy.Then,through online questionnaires and field interviews,456 sets of data were collected in Beijing,and the data were analyzed by the structural equation model and latent class model.The results show that the four incentive policies are positively correlated with users'participation in MaaS,among which financial incentive policy and information policy have the greatest impact,that is,they can better encourage users by increasing direct financial subsidies and broadening the information about MaaS.In addition,Latent Class Analysis was performed to class different users and it was found that the personal characteristics of users had some influence on willingness to participate in MaaS.Therefore,incentive policies should be designed to consider the needs and characteristics of different user groups to improve their willingness to participate in MaaS.The results can provide theoretical suggestions for the government to promote the widespread application of MaaS in urban transportation.

Simulation of critical transitions and vulnerability assessment of Tibetan Plateau key ecosystems

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.

Rethink Left-Behind Experience: New Categories and Its Relationship with Aggression

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.

Identification of subgroups of self-reported outcomes among breast cancer patients undergoing surgery and chemotherapy:A crosssectional study

Objectives:To identify the subgroups of self-reported outcomes and associated factors among breast cancer patients undergoing surgery and chemotherapy.Methods:A cross-sectional study was conducted between January and November 2021.We recruited patients from two tertiary hospitals in Shanghai,China,using convenience sampling during their hospitalization.Patients were assessed using a questionnaire that included sociodemographic and clinical characteristics,the Patient Reported Outcomes Measurement Information System profile-29(PROMIS-29),and the PROMIS-cognitive function short form 4a.Latent class analysis was performed to examine possible classes regarding self-reported outcomes.Multiple logistic regression analysis was used to determine the associated factors.Analysis of variance(ANOVA)was conducted for symptoms across the different classes.Results:A total of 640 patients participated in this study.The findings revealed three subgroups in terms of self-reported outcomes among breast cancer patients undergoing surgery and chemotherapy:low physical-social-cognitive function,high physical-low cognitive function,and high physical-socialcognitive function.Multivariable logistic regression analysis showed that age(≥60 years old),menopause,the third chemotherapy cycle,undergoing simple mastectomy and breast reconstruction,duration of disease 3-12 months,stageⅢ/Ⅳcancer,and severe pain were associated factors of the functional decline groups.Besides,significant differences in depression and sleep disorders were observed among the three groups.Conclusions:Breast cancer patients receiving surgery and chemotherapy can be divided into three subgroups.Aging,menopause,chemotherapy cycle,surgery type,duration and stage of disease,and severe pain affected the functional decline groups.Consequently,healthcare professionals should make tailored interventions to address the specific functional rehabilitation and symptom relief needs.