The Impact of Big Five Personality Traits on Older Europeans’ Physical Health

Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .

Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

Correlation of personality with individual reproductive success in shrub-nesting birds depends on their life history style

Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.

Cognitive impairment in patients with bipolar disorder alone versus those with bipolar disorder comorbid with borderline personality disorder

BACKGROUND Bipolar disorder(BD)is a severe mental illness.BD often coexists with borderline personality disorders,making the condition more complex.AIM To explore the differences in cognitive impairment between patients with BD and those with BD comorbid with borderline personality disorder.METHODS Eighty patients with BD and comorbid borderline personality disorder and 80 patients with BD alone were included in groups A and B,respectively,and 80 healthy volunteers were included as controls.Cognitive function in each group was evaluated using the Chinese version of the repeatable battery for the assess-ment of neuropsychological status(RBANS),the Stroop color-word test,and the Wechsler intelligence scale-revised(WAIS-RC).RESULTS The indices of the RBANS,Stroop color-word test,and WAIS-RC in groups A and B were significantly lower than those of the control group(P<0.05).Group A had significantly longer Stroop color-word test times for single-character,single-color,double-character,and double-color,lower scores of immediate memory,visual breadth,verbal function dimensions and total score of the RBANS,as well as lower scores of verbal IQ,performance IQ,and overall IQ of the WAIS-RC compared with group B(P<0.05).Compared to group B,group A exhibited significantly longer single-character time,single-color time,double-character time,and double-color time in the Stroop color-word test(P<0.05).CONCLUSION The cognitive function of patients with BD complicated with borderline personality disorder is lower than that of patients with BD.

多主体协同开发社区教育课程的PERSONAL模式探索——基于“我爱我家”的个案研究

通过对“我爱我家”这一社区自发课程的跟踪调查,发现社区自发课程开发实质上是一个组织学习过程,经过个体直觉、知识解释、知识整合等环节实现组织的制度化学习,在推动学习型无边界组织生成的同时,生成社区教育课程。在这一过程中,政策(policies)支持、专家(expert)指导、区域需求和区域特征(regional)、社区系统(system)、组织化(organization)等多要素叠加发挥作用,通过不断拓展新鲜(new)领域的积极行动(action)整合所有资源,并推动跨越边界的学习(learning)贯穿始终。因此,多主体协同的社区自发课程可以不完全地概括为PERSONAL模式。要以PERSONAL模式发展社区自发课程,需要以学习者为中心,注重发展人际互动,营造有利于跨组织交互学习的教育生态系统。

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Big Five Personality Factors and Library Anxiety

Library anxiety is an unpleasant feeling that is experienced in a library location;it has behavioral, psychological, emotional and cognitive effect, which can be harmful for students’ academic career. The purpose of current study was to investigate the relationship between Library anxiety and the Big Five personality factors (neuroticism, extraversion, openness-to-experience, agreeableness, and conscientiousness) using a multivariate approach among students in Ardabil university. The participants were students of Ardabil University of Medical Sciences of which a sample of 580 students was randomly selected. And the assessment methods were revised. The short form of NEO Inventory [1] and the library anxiety questionnaire [2] were used to gather the data. The results showed that Neuroticism increased library anxiety in students, and with increasing the level of education, library anxiety is reduced, whereas by increasing the semester the library anxiety of students increases.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

Model considering panic emotion and personality traits for crowd evacuation

Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.

The Relationship between Mental Disorders and Personality of Outpatients in a Psychiatric Clinic in Nanjing, China

Psychosis has increasingly become a social problem,emphasizing the need to understand the relationship between mental disorders and personality.This study aimed to investigate the relationship between mental disorders and personality among psychiatric outpatients based on real-world data.Symptom Checklist 90(SCL-90)and Eysenck Personality Questionnaire(EPQ)were used to evaluate the personality and psychopathological symptoms of patients(n=8409)in the Psychiatric Outpatient Department at Nanjing Drum Tower Hospital.t-test was used to compare scores between patients and national norms.Pearson’s correlation coefficient and path analysis were used to explore the relationship between mental health status and personality.The correlation coefficient between the neuroticism(N)score and each factor score of the SCL-90 test,as well as the correlation between psychoticism(P)and hostility and paranoia,exceeded 0.4.Path analysis revealed that the standardized path coefficients of N score and SCL-90 were all higher than 0.4.In addition,the standardized path coefficient of hostility and paranoia on P score were 0.313 and 0.280,respectively.Interpersonal sensitivity,depression and obsessive-compulsive symptoms were affected by extraversion(E)score,with standardized path coefficients of-0.149,-0.138,and-0.105,respectively.The path analysis also showed the direct and indirect effects of age,gender,education,and marital status on SCL-90.Patients characterized as melancholic had higher scores in all factors of SCL-90.In conclusion,mental health was related to personality traits of neuroticism,psychoticism and introversion.

Personality and Nomophobia: A Moderated Mediation Model of Self-Esteem and Non-Self-Determined Solitude

Background:With the development of information technology,mobile phone has brought much convenience to people’s lives but also caused many negative consequences due to excessive use,such as mobile phone addiction and nomophobia.Previous studies have explored the relationship between the Big Five Personality and proble-matic mobile phone use(PMPU).However,they focus on mobile phone addiction.Although there is a correlation between nomophobia and mobile phone addiction,the psychological structure is different.Therefore,it is neces-sary to explore the relationship between personality and nomophobia and the underlying mechanism.This study aims to examine the relationship between Big Five Personality and nomophobia,then construct a moderated mediation model to explore the mediation effect of solitude between Big Five Personality and nomophobia,as well as the moderation effect of self-esteem.Method:Data from 678 college students(351 females,51.77%)were collected.Participants completed the Big Five Personality Inventory,Solitude Behavior Scale,Nomophobia Scale and Self-esteem Scale.Analyses were conducted via mediation and moderated mediation.Results:Structural equation models revealed that solitude mediated the relationship between neuroticism and nomophobia.The results showed that neuroticism positively predicted solitude,which in turn positively predicted nomophobia.Four types of solitude partially mediated the relationship between neuroticism and nomophobia.We also found that self-esteem moderated the association between neuroticism and non-self-determined solitude.It is note-worthy that high self-esteem cannot protect people from negative factors.However,because of its characteristics,it is easy to receive more social information,and people high in neuroticism are sensitive to negative social infor-mation.It may cause maladaptive behavior.Conclusion:Findings demonstrated a process through which neu-roticism relates to nomophobia and a context under which these relationships may have occurred.

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral pattern.It has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become biased.They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves.Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,etc.Stream of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator.More-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding techniques.This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents.Bidirectional Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.

Who Benefits More from Physical Exercise?On the Relations between Personality,Physical Exercise,and Well-Being

Although employers believe that encouraging and supporting physical exercise activities by purchasing fitness equipment and building sports venues can improve employees’well-being,the utilization rate is rather low.Since most of the evidence of the well-being promotion in the workplace concentrated on the perspectives of organizational factors and psychosocial factors and focused on the reduction of the negative affect of well-being,it is still an open question whether physical exercise has benefits on both negative and positive affect of well-being and who benefits more from physical exercise.Thus,the purpose of this study is to investigate the impact of physical exercise on occupational well-being(job burnout and work engagement)and examine whether effectiveness depends on personality traits.Online questionnaires were distributed.The sample included 671 participants from different enterprises in China.Results showed that the effectiveness of physical exercise was also applicable to well-being in the workplace.Physical exercise was negatively correlated with job burnout and positively correlated with work engagement.The effectiveness was different among employees with different personality traits.Contrary to our expectation,individuals with neuroticism were more likely to improve their work engagement through physical exercise.Extroversion and conscientiousness weakened the benefits of physical exercise.Therefore,differences of effectiveness among different personality traits emphasize the need for a more personalized strategy in physical exercise interventions.

An analysis of association of personality traits with job burnout among nurses working in hospital

Objective:The objective of the present study is to explore the effects of personality traits on job burnout among hospital nurses.Materials and Methods:This cross-sectional research was done during 2019-2020 at Kashan Shahid Beheshti Hospital.The data analysis procedures included descriptive statistics and the partial least squares-based structural equation modeling.The participants were 150 nursing professionals.A questionnaire indicating information on demographics,burnout(measured using the Maslach Burnout Inventory with three dimensions of depersonalization,emotional exhaustion,and personal accomplishment),and personality profile(measured employing the neuroticism extraversion openness five-factor inventory including extroversion,conscientiousness,agreeableness,neuroticism,and openness to experience dimensions)was used to gather the required data.Results:The results of the study showed that the validity and reliability of the measurement model were desirable(factor load higher than 0.5,the Cronbach’s alpha value and the composite reliability are>0.7).Structural model showed statistically drastic,negative relationship between the nurses’burnout levels and neuroticism(β=0.722)and openness to experience(β=0.437).However,the relationship was significantly positive between the nurses’burnout levels and conscientiousness(β=0.672),agreement(β=0.594),and extraversion(β=0.559)(P<0.03).Conclusions:The present study helped the recognition of burnout among nurses working in hospitals and approved the effects of personality features on the burnout experience.

Constitutional Nature of Personality Rights and Its Manifestation in Civil Law

The formation of personality comes from people’s choices and pursuit of self-realization,which is influenced by objective factors but not determined by them,so personality does not belong to the domain of objectivity.The concept of general personality rights in the German Constitution was initially premised on the objective determinability in the field of personality,but in constitutional jurisprudence,it gradually shifted to something with individual autonomy as the core and personal self-realization as the goal,and the scope of relevant rights expanded accordingly,so that they could not be clearly distinguished from general freedom of action and thus became the general principle of constitutional rights.The protection of constitutional personality rights in the United States and Japan can also confirm this process,providing evidence for the constitutional nature of personality rights.Deeper research shows that constitutional personality rights actually manifest the highest value of modern constitutions—human dignity.In contrast,the theoretical justification of personality rights in civil law just lies in the objectivity and defensive nature of personality elements.

On the Construction and Application Scenarios of——Analysis of Environmental Personality Interests Based on Human Rights

Environmental personality interests based on human rights reflect the multiple values of ecological order,ecological justice,and ecological freedom,and are closely linked to the protection of the right to life and the right to health.They are also related to human dignity and the personal freedom of civil subjects and conform to formal and essential standards of personality rights,which should be included in the scope of personality rights for protection.The construction and application of environmental personality rights faces bottlenecks such as the partiality of subjects,limitation of objects,and hysteresis of responsibilities in the protection of environmental personality rights.Environmental personality rights are supposed to reflect the needs of the development of modern human rights.We should expand the scope of its connotative power and function based on the Green Principle of the Civil Code,and follow a networked,typified,and systematic path of protection,so as to manifest the people-centered philosophy of the Civil Code and the Environmental Protection Law.

Deep Bimodal Fusion Approach for Apparent Personality Analysis

Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.