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
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 ...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.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
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 polarizati...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.展开更多
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 knowled...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.展开更多
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...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.展开更多
This paper presents a study that explored the behavioral heterogeneity of changes in peo-ple’s information and communications technology(ICT)usage and travel patterns at the end of the pandemic.A quasi-longitudinal a...This paper presents a study that explored the behavioral heterogeneity of changes in peo-ple’s information and communications technology(ICT)usage and travel patterns at the end of the pandemic.A quasi-longitudinal approach was employed to collect data from Florida residents,capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021.Utilizing the latent class analysis(LCA)approach to identify subgroups based on the online activity durations and trip frequencies,four distinct classes were identified.A little more than one third(35%)of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies.About 33%of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities.About 16%of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities.Another 16%of the respon-dents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities.These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities.The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.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
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-measu...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.展开更多
Introduction:Breast cancer has emerged as the most prevalent cancer among women globally.However,the relationship between individual socioeconomic status(SES)and breast cancer risk remains incompletely understood.Meth...Introduction:Breast cancer has emerged as the most prevalent cancer among women globally.However,the relationship between individual socioeconomic status(SES)and breast cancer risk remains incompletely understood.Methods:This population-based cohort study recruited women aged 30-70 years from Shandong,Hebei,and Jiangsu provinces in China during 2008 and 2018.We developed a composite SES measure through latent class analysis incorporating household income,education level,and health insurance type,stratifying participants into low and high SES groups.Self-perceived SES was evaluated using a Likert scale.We employed Cox proportional hazards regression to estimate hazard ratios(HRs)and 95%confidence intervals(CIs)for the association between SES and breast cancer incidence.Results:Among 62,350 participants followed for an average of 6.1 person-years,we identified 300 incident breast cancer cases.The overall incidence rate was 48.9 per 100,000 person-years.Women with high SES demonstrated significantly elevated breast cancer risk compared to those with low SES(HR=1.42,95%CI:1.05-1.92).Self-perceived SES appeared to modify this relationship,with increased breast cancer risk observed among women categorized as both objectively and subjectively low SES.Conclusions:These findings underscore the need for SES-specific approaches to breast cancer screening programs and targeted health education initiatives.展开更多
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 i...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→+∞.展开更多
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...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<sup class='a-plus-plus'>[3]</sup> 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<sup class='a-plus-plus'>[4]</sup> is given. The stability problem of closed orbits raised in [5] and [6] is resolved.展开更多
Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environ...Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environment.This study develops an assessment framework for evaluating walking satisfaction on sidewalks in commercial districts in Japan according to the built environment and personal attributes.Questionnaire surveys were used to collect data from 963 Japanese residents’impact ratings of built environment variables.Six factors were extracted following exploratory factor analysis.Second-order confirmatory factor analysis was then applied to further explore the relationship between observed variables and latent factors.Latent class analysis was employed to classify the population,and personal attributes served as covariates in the multinomial logistic regression analysis to explore how these attributes affected the latent classes.The results indicated that visual impression,spatial richness,accessibility,comfort,diversity,and security influence pedestrian walking satisfaction on sidewalks in commercial districts.Travel purpose and travel method are important indicators that influence the latent classes of the population.The result presented herein can guide policy makers to optimize the construction of walkable urban environments and enact policies based on the factors and populations that are best suited to urban development.展开更多
Principal component analysis(PCA),factor analysis(FA),latent class analysis(LCA),and latent profile analysis(LPA)are prominent statistical models in the social and behavioral sciences,employed to reveal underlying pat...Principal component analysis(PCA),factor analysis(FA),latent class analysis(LCA),and latent profile analysis(LPA)are prominent statistical models in the social and behavioral sciences,employed to reveal underlying patterns and relationships within extensive datasets.PCA involves reducing observed variables while retaining dataset variance by generating principal components.FA aims to decrease observed variable dimensions by identifying underlying factors.LCA and LPA,respectively,focus on discerning latent classes and profiles based on response patterns to categorical and continuous variables.Recently,latent Dirichlet allocation(LDA)method has gained increasing popularity for its application in analyzing textual data alongside numerical responses,contributing to the extraction of hidden structures within complex textual data.Topic modeling(TM),a broader method,is used to identify underlying themes or topics in text corpora,with LDA representing a specific application of TM.Despite some common goals of uncovering hidden structures,these models vary in statistical theory,algorithms,and applications.This paper seeks to investigate these differences and similarities,facilitating researchers in selecting the appropriate model to address their specific research questions.Each model is introduced first,outlining its statistical form and relevant theory.A comprehensive comparison of these models emphasizing their distinguishing features is provided,followed by the illustration of these models and methods.Finally,practical recommendations are offered to aid researchers in effectively implementing these models in their own research.展开更多
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...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.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 Hospital-level Nursing Research Project of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine(xhhlcx2023-017)the third period of the 14th Five-Year nursing talent project of Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine(Xhlxm014)the Ministry of Education of Humanities and Social Science Project(23YJC630002)and High-level local university construction project founded by Shanghai Municipal Education Commission.
文摘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.
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.
文摘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.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number(DSR2022-RG-0105).
文摘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.
基金sponsored by The National Natural Science Foundation of China(Grant No.71971020).
文摘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.
基金sponsored by the Florida Department of Transportation(BDV29977-59).
文摘This paper presents a study that explored the behavioral heterogeneity of changes in peo-ple’s information and communications technology(ICT)usage and travel patterns at the end of the pandemic.A quasi-longitudinal approach was employed to collect data from Florida residents,capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021.Utilizing the latent class analysis(LCA)approach to identify subgroups based on the online activity durations and trip frequencies,four distinct classes were identified.A little more than one third(35%)of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies.About 33%of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities.About 16%of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities.Another 16%of the respon-dents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities.These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities.The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.
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.
基金Project supported by National Natural Science Foundation of China
文摘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.
基金Supported by the National Key R&D Program of China(2016YFC0901300,2016YFC0901301)IMICAMS Youth Talent Development Fund under Number 2024YT02,and the Strategic Research and Consultancy Project of the Chinese Academy of Engineering(2023-JB-11).
文摘Introduction:Breast cancer has emerged as the most prevalent cancer among women globally.However,the relationship between individual socioeconomic status(SES)and breast cancer risk remains incompletely understood.Methods:This population-based cohort study recruited women aged 30-70 years from Shandong,Hebei,and Jiangsu provinces in China during 2008 and 2018.We developed a composite SES measure through latent class analysis incorporating household income,education level,and health insurance type,stratifying participants into low and high SES groups.Self-perceived SES was evaluated using a Likert scale.We employed Cox proportional hazards regression to estimate hazard ratios(HRs)and 95%confidence intervals(CIs)for the association between SES and breast cancer incidence.Results:Among 62,350 participants followed for an average of 6.1 person-years,we identified 300 incident breast cancer cases.The overall incidence rate was 48.9 per 100,000 person-years.Women with high SES demonstrated significantly elevated breast cancer risk compared to those with low SES(HR=1.42,95%CI:1.05-1.92).Self-perceived SES appeared to modify this relationship,with increased breast cancer risk observed among women categorized as both objectively and subjectively low SES.Conclusions:These findings underscore the need for SES-specific approaches to breast cancer screening programs and targeted health education initiatives.
基金This is a part of my Master thesis under the direction of Professor Li Bingxi.
文摘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→+∞.
文摘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<sup class='a-plus-plus'>[3]</sup> 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<sup class='a-plus-plus'>[4]</sup> is given. The stability problem of closed orbits raised in [5] and [6] is resolved.
基金funding support from the JST SPRING(Grant No.JPMJSP2128).
文摘Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environment.This study develops an assessment framework for evaluating walking satisfaction on sidewalks in commercial districts in Japan according to the built environment and personal attributes.Questionnaire surveys were used to collect data from 963 Japanese residents’impact ratings of built environment variables.Six factors were extracted following exploratory factor analysis.Second-order confirmatory factor analysis was then applied to further explore the relationship between observed variables and latent factors.Latent class analysis was employed to classify the population,and personal attributes served as covariates in the multinomial logistic regression analysis to explore how these attributes affected the latent classes.The results indicated that visual impression,spatial richness,accessibility,comfort,diversity,and security influence pedestrian walking satisfaction on sidewalks in commercial districts.Travel purpose and travel method are important indicators that influence the latent classes of the population.The result presented herein can guide policy makers to optimize the construction of walkable urban environments and enact policies based on the factors and populations that are best suited to urban development.
文摘Principal component analysis(PCA),factor analysis(FA),latent class analysis(LCA),and latent profile analysis(LPA)are prominent statistical models in the social and behavioral sciences,employed to reveal underlying patterns and relationships within extensive datasets.PCA involves reducing observed variables while retaining dataset variance by generating principal components.FA aims to decrease observed variable dimensions by identifying underlying factors.LCA and LPA,respectively,focus on discerning latent classes and profiles based on response patterns to categorical and continuous variables.Recently,latent Dirichlet allocation(LDA)method has gained increasing popularity for its application in analyzing textual data alongside numerical responses,contributing to the extraction of hidden structures within complex textual data.Topic modeling(TM),a broader method,is used to identify underlying themes or topics in text corpora,with LDA representing a specific application of TM.Despite some common goals of uncovering hidden structures,these models vary in statistical theory,algorithms,and applications.This paper seeks to investigate these differences and similarities,facilitating researchers in selecting the appropriate model to address their specific research questions.Each model is introduced first,outlining its statistical form and relevant theory.A comprehensive comparison of these models emphasizing their distinguishing features is provided,followed by the illustration of these models and methods.Finally,practical recommendations are offered to aid researchers in effectively implementing these models in their own research.
文摘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.
