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.

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes.People express their unique ideas and views onmultiple topics thus providing vast knowledge.Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making.Since the proliferation of COVID-19,it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked.The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed.Hence,this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis.This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment.This can be particularly helpful when dealing with smaller datasets.Furthermore,our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms.This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts.The experimental results validate that our model offers excellent results with a validation accuracy of 92.5%and an F1 measure of 0.92.

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.

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.

The Effect of Class and Employment Type on the Awareness of Irregular Work Issues in Korea and ｜apan

Recently, there is greater recognition and increased attempts to protect the rights of irregular workers within Korea and Japan, especially in Korea. This is because of more and more public awareness of the polarization in material conditions between regular workers and irregular workers. So, this study focuses on the main factors explaining awareness of irregular worker issues of each of the classes, and relationship between class consciousness in both countries. The result shows that among factors affecting awareness of irregular work issues, negative effect of subjective employment stability was significant in both countries. In regard of anti-flexibility, while strong class effect was observed in Korea, negative effect of anti-neoliberalism was observed in Japan. This is seemingly contradictory that who opposes neoliberal economic policies agrees with labor market flexibilisation. This phenomenon could be explained by labor market characteristics in Korea and Japan. Japanese labor market is characterized by low flexibility and strong segmentation, while Korean labor market is characterized by high flexibility and strong segmentation. Interaction of these two characteristics increases the labor market inequality in Korea.

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.

ROBUST ANALYSIS AND GLOBAL MINIMIZATION OF A CLASS OF DISCONTINUOUS FUNCTIONS (Ⅱ)

In this Paper we continue to investigate global minimization problems. An integral approach is applied to treat a global minimization problem of a discontinuous function. With the help ofthe theory of measure (Q-measure) and integration, optimality conditions of a robust function over arobust set are derived. Algorithms and their implementations for finding global minima are proposed.Numerical tests and applications show that the algorithms are effective.

Global Analysis of a Class of Cooperative Systems

In this paper we analyze globally the behavior of the solutions of a class of cooperative systems. Our main results is that every orbit of the cooperative system (3.1) either approaches the equilibrium (0, 0, 0), or is unbounded, ast→+∞.

Qualitative Analysis of a Class of Competitive and Cooperative Systems

Hirsch[1,2] studied the limiting behavior of solutions of competitive or cooperative systems, and showed that ifL is an ω-limit set of a three-dimensional cooperative system, which contains no equilibrium, thenL is a nonattracting closed orbit. Smith[3] considered a three-dimensional irreducible competitive system and showed that an ω-limit set containing no equilibrium must be a closed orbit which has a simple Floquet multiplier λ<1, and may be attracting. In this paper we carry out the qualitative analysis of a class of competitive and cooperative systems, and a generalization of the result of Levine[4] is given. The stability problem of closed orbits raised in [5] and [6] is resolved.

The Marxist Theory of the State and National Identity

Nation-state identity has become a focus of theoretical discussion in academia home and abroad in recent years. Under the new historical conditions, the study of national identity should take the Marxist theory of the state as its theoretical basis and stick to the Marxist view of class and class analysis so as to properly understand, guide and enhance national identity. The Marxist theory of the state has analyzed in a scientific way the origin, nature, development, succession and decay of the state, and the innovative and transitional nature of the proletarian state. We should not stop at the level of ＂national identity in general,＂ but should instead use the Marxist view of class to look at and analyze specific people＇s identification with specific states. As far as the developed capitalist states are concerned, the bourgeoisie and the working class differ dramatically in their views, attitudes, emotions and beliefs toward their state. When it comes to China that is still at the primary stage of socialism, national identity needs to be studied in depth and guided in a correct way, especially under the condition of reform and opening-up. The main subjects of national identity should get optimized at all levels.Meanwhile, measures should be taken to enhance conscious awareness of and cultivate right attitudes toward national identity.