Personality Features Research Articles

A NOVEL SLEEP STAGE CLASSIFICATION METHOD FOR ELDERLY PEOPLE USING THE SLEEP EEG SIGNAL AND SENET MODELS

First, research on sleep stage staging in older individuals is highly valuable because it helps to obtain insights into sleep quality and structure changes that occur in older persons. This approach was used to better understand the correlations with cognitive functioning, the immune system, and mental health, among other factors. Second, identifying the features of older persons in various stages of sleep could lead to more specific guidance for managing and treating personalized sleep to significantly improve the quality of life of these individuals. Finally, in-depth research on sleep in older persons can assist in the development of preventative and intervention measures for older adults, which can help to reduce the negative consequences that age-related sleep issues have on general health. Categorizing sleep EEG data is the focus of this research to present an improved squeeze-and-excitation network (SENet) for application in the classification process. For the purpose of this study, electroencephalogram (EEG) features were extracted using continuous wavelet transform. Subsequently, the lightweight context transform (LCT), which is a combination of normalization, linear transformation, and SENet, is carried out to achieve sleep EEG classification. Extraction using the continuous wavelet transform has the potential to more accurately depict the changes and characteristics of sleep stages. As a result of incorporating LCT, the computational cost of the model is reduced, and the model becomes more applicable to real-world situations. Experiments conducted on two public datasets, Sleep-EDF-20 and Sleep-EDF-78, demonstrated that the strategy presented in this study can achieve the requisite classification performance and the model converges faster.

Qualitative interviews with young adults at risk for psychosis and who use Cannabis: Informing the development of a mobile intervention

Cannabis use disorder is common among young adults in psychiatric care, particularly those at risk for psychosis. Yet, interventions tailored to address this issue are limited. The goal of this qualitative study was to determine barriers and facilitators for reducing/quitting cannabis use, and to obtain end-user perspectives to inform a mobile app-based intervention. Twenty young adults (60% female) at high-risk for psychosis who use cannabis completed a brief survey and individual qualitative interview. Results of thematic analysis revealed five barriers (i.e., cannabis to cope, social influences, dependence symptoms, easy access, ambivalence about change) and four facilitators (e.g., experienced/feared consequences, focusing on motivation, social support, coping skills) to cutting down/quitting cannabis. Regarding mobile intervention development, participants recommended high-level personalization and content features such as goal setting, self-monitoring, coping skills, and affirming messages. Additionally, they suggested the intervention be visually appealing and interactive. To assist high-risk youth in reducing/quitting cannabis use, it is important to consider the unique barriers they face in doing so, as well as their specific preferences for the development of a mobile intervention.

Alteration of prefrontal cortex and its associations with emotional and cognitive dysfunctions in adolescent borderline personality disorder.

The neurobiological mechanism of borderline personality disorder (BPD) in adolescents remains unclear. The study aimed to assess the alterations in neural activity within prefrontal cortex in adolescents with BPD and investigate the relationship of prefrontal activity with emotional regulation and cognitive function. This study enrolled 50 adolescents aged 12-17 years with BPD and 21 gender and age-matched healthy control (HC) participants. Study assessment for each participant included a brain resting-state functional MRI (rs-fMRI), clinical assessment questionnaires such as Borderline Personality Features Scale (BPFS), Difficulties in Emotion Regulation Scale (DERS), Ottawa Self-Injury Inventory and Childhood Trauma Questionnaire (CTQ) and cognitive testing with Stroop Color-Word Test (SCWT). Fractional amplitude of low-frequency fluctuations (fALFF) and seed-based functional connectivity (FC) were obtained from rs-fMRI analysis. Correlation analysis was also performed to evaluate the associations of the neuroimaging metrics such as fALFF and FC with clinical assessment questionnaire and cognitive testing scores. Adolescents with BPD showed increased fALFF values in the right inferior frontal gyrus and decreased activity in the left middle frontal gyrus as compared to the HC group (p < 0.05, cluster size ≥ 100, FWE correction). In adolescents with BPD, increased fALFF in the right inferior frontal gyrus was related to the BPFS (emotional dysregulation), DERS-F (lacking of emotional regulation strategies) and Ottawa Self-Injury Inventory-4C scores (internal emotional regulation function of self-injurious behavior). The reduced fALFF in the left middle frontal gyrus was associated with the SCWT-A (reading characters) and the SCWT-B (reading color) scores. Additionally, the fALFF values in the left middle frontal gyrus and the right inferior frontal gyrus were related to the CTQ-D (emotional neglect) (p < 0.05). The left middle frontal gyrus exhibited increased FC with the right hippocampus, left inferior temporal gyrus and right inferior frontal gyrus (voxel p < 0.001, cluster p < 0.05, FWE correction). The increased FC between the left middle frontal gyrus and the right hippocampus was related to the SCWT-C (cognitive flexibility) score. We observed diverging changes in intrinsic brain activity in prefrontal cortex, and neural compensatory changes to maintain function in adolescents with BPD. In addition, decreased neural function was closely associated with emotional dysregulation, while increased neural function as indicated by brain activity and FC was associated with cognitive dysfunction. These results indicated that alterations of intrinsic brain activity may be one of the underlying neurobiological markers for clinical symptoms in adolescents with BPD.

Investigating the transdiagnostic potential of rumination in relation to Cluster B personality disorder symptoms.

This study examined several forms of repetitive negative thinking in relation to measures of Cluster B disorders to identify unique and co-occurring cognitive mechanisms contributing to these symptom presentations. MTurk participants (N = 725; 53% men; 76% White) completed categorical and dimensional trait-based measures of Cluster B disorders alongside six measures of rumination and worry. Oversampling strategies were used during participant recruitment to obtain a sample with clinically relevant personality features. Path analyses examined each form of repetitive negative thinking in relation to Cluster B measures while controlling for shared variance between each construct. Anger rumination demonstrated notable relations across all four Cluster B personality disorders. Similarly, sadness rumination was positively associated with all four Cluster B personality disorders. Self-critical rumination and depressive rumination were uniquely associated with borderline symptoms. Worry demonstrated negative associations with measures of narcissistic, histrionic, and antisocial/psychopathy symptoms. Findings expand previous research by highlighting probable transdiagnostic and distinguishing cognitive process contributing to the presentation of Cluster B personality pathology. However, partialling the influence of overlapping constructs presents notable challenges in identifying and interpreting unique relations between various forms of repetitive negative thinking and Cluster B symptoms. Future research investigating these relations within outpatient and inpatient samples may provide avenues for the development of effective cognitive-based interventions for treating these symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

First psychometric evaluation of the Level of Personality Functioning Scale-Brief Form 2.0 in adolescents.

Reflecting the recent consensus that challenges in personality functioning often onsets in adolescence, and the move toward dimensional models of personality pathology such as the level of personality functioning (LPF) of the alternative model for personality disorders, it is important to have validated measures that can assess LPF in young people. The Level of Personality Functioning Scale-Brief Form 2.0 (LPFS-BF 2.0) is the briefest measure of LPF and may be particularly well suited for assessing LPF in youth; however, it has yet to be formally validated in youth. Therefore, the current investigation evaluated the psychometric properties of the LPFS-BF 2.0 in adolescents drawn from a community sample of ethnically diverse North American youth (N = 194, age 12-18; 58% female). Factor structure, gender invariance, reliability, convergent validity, incremental validity, and criterion validity were evaluated. Results demonstrated support for the LPFS-BF 2.0's unidimensional factor structure, as well as high internal consistency. Configural, metric, and scalar measurement invariance was supported across male and female genders, as well as convergent validity. Relative to the Personality Inventory for the DSM-5 Brief Form and Levels of Personality Functioning Questionnaire 12-18, the LPFS-BF 2.0 demonstrated additional variance in predicting borderline personality features, and internalizing and externalizing problems. Study findings support the English version of the LPFS-BF 2.0 as a brief and psychometrically sound instrument for assessing LPF in youth and adolescents. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Subtypes of borderline personality features in adolescence: Insights from cross-lagged panel network analysis.

Borderline personality disorder (BPD) is a complex and severe psychiatric condition characterized by emotional, self-image, behavior, and relational instability. While adult BPD heterogeneity has been extensively studied, the phenomenological borderline personality features (BPFs) in adolescence remain uninvestigated. This study aimed to explore the potentially dynamic causal relationships between BPFs in adolescence and identify the subtypes through cross-lagged panel network (CLPN) analysis. Two independent Chinese adolescent samples were followed over 18 months (N₁ = 1,056, M1age = 15.37, SD1 = 1.86) and 6 months (N₂ = 723, M2age = 16.84, SD₂ = 0.48) to track BPFs. CLPN modeling was employed to investigate the stability, potential causal relations, and subtypes of adolescent BPFs. The results revealed a relatively stable overall adolescent BPF network structure with some subtle changes over time. Impulsivity emerged as the BPF with the highest out-expected influence, indicating its predictive role for other BPFs. A strong reciprocal causal relationship was observed between impulsivity and affective instability. Based on the CLPN estimation, two distinct BPFs subgroups were spontaneously clustered: externalized-dysregulation subtype (impulsivity, affective instability, and self-harm/suicide) and introjective-disturbance subtype (identity disturbance, chronic emptiness, and stress-related dissociation). The present study tentatively explores a potential typology for adolescent BPF based on these two clusters, which possibly have different pathological mechanisms, and moreover offer insights into the essential construct and clinical intervention of adolescent BPD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Ideology: Psychological Similarities and Differences Across the Ideological Spectrum Reexamined.

A key debate in the psychology of ideology is whether leftists and rightists are psychologically similar or different. A long-standing view holds that left-wing and right-wing people are meaningfully different from one another across a whole host of basic personality and cognitive features. Scholars have recently pushed back, suggesting that left-wing and right-wing people are more psychologically similar than distinct. We review evidence regarding the psychological profiles of left-wing and right-wing people across a wide variety of domains, including their dispositions (values, personality, cognitive rigidity, threat-sensitivity, and authoritarianism), information processing (motivated reasoning and susceptibility to misinformation), and their interpersonal perceptions and behaviors (empathy, prejudice, stereotyping, and violence). Our review paints a nuanced picture: People across the ideological divide are much more similar than scholars sometimes appreciate. And yet, they differ-to varying degrees-in their personality, values, and (perhaps most importantly) in the groups and causes they prioritize, with important implications for downstream attitudes and behavior in the world.

Early remission of deliberate self-harm predicts emotion regulation capacity in adulthood: 12.4 years follow-up of a randomized controlled trial of adolescents with repeated self-harm and borderline features

AbstractEmotion regulation capacity, critical for adult functioning and mental health, develops strongly during adolescence in healthy individuals. Deficits in emotion regulation is often referred to as emotion dysregulation [ED] and is associated with various mental health problems, including repeated deliberate self-harm [DSH] which peaks in adolescence. Dialectical Behaviour Therapy for adolescents [DBT-A] systematically targets ED through strategies such as changing coping behaviours and has previously been shown to effectively induce DSH remission in adolescents. However, whether such remission is associated with improved emotion regulation capacity in adulthood, and whether this effect is mediated by changes in use of coping strategies has not been previously studied. Prospective long-term follow-up study of an RCT comparing DBT-A with enhanced usual care [EUC] for adolescents presenting to community child and adolescent psychiatric outpatient clinics with borderline personality features and repeated self-harm. Assessments included both structured interviews and self-report at baseline and 1.6, 3.1 and 12.4 years follow-up. In the final follow-up, adult ED was measured and data were collected for 61 (80%) of the original 77 participants. DSH remission was assessed at 1.6 years follow-up, and use of coping strategies at 3.1 and 12.4 years follow-ups. A mediation analysis was conducted within a causal inference framework. Both treatment groups increased their use of functional coping skills from adolescence to adulthood, while only DBT-A was associated with decreases in dysfunctional coping. There was a direct effect of DSH remission 1 year after treatment on adult ED, particularly for participants who did not receive DBT-A. There was a negative association between reductions in dysfunctional coping and adult ED, however this did not mediate the effect of DSH remission. This is the first study to report that early DSH remission in adolescence predicted lower ED in adulthood. These results highlight the importance of early DSH remission and provides new insight into the long-term relationship between DSH and ED. Clinical trial registration information: “Treatment for Adolescents with Deliberate Self-harm”; http://ClinicalTrials.gov/;NCT00675129

Can mobile apps facilitate more sustainable and healthier diets? A systematic review

Abstract Background One third of all greenhouse gas emissions come from the food system, and current diets are insufficient to meet environmental and public health goals. Digital health apps offer promise to change behaviour, but little is known about the effects of different types of apps. This systematic review assessed the effectiveness of mobile apps in facilitating dietary changes across adults from high-income countries. Methods Seven electronic databases (MEDLINE, EMBASE, PsycINFO, CINAHL, Global Health, GreenFILE, Web of Science), two trial registries (Cochrane Trials and Clinicaltrials.gov), and Google were systematically searched for intervention studies aiming to facilitate dietary change. Interventions were characterised by their use of messaging (e.g., motivational statements) and personalised features (e.g., tailored nutrition advice). Outcomes for three food groups were synthesised through meta-analysis. Results Of the 5261 identified records, 21 met the eligibility criteria and were included. Interventions, recruiting 73,973 participants in total, lasted from three days to six months. Overall positive changes were observed in daily servings of fruit and vegetables (0.59, 95% CI 0.20, 0.99), legumes (0.05, 95% CI 0.01, 0.08), and meat (-0.14, 95% CI -0.23, -0.05), remaining significant after removing non-randomised controlled trials. Changes in fruit and vegetable intake were more pronounced for users of apps with messaging components (0.75, 95% CI 0.18, 1.32) and personalisation (0.67, 95% CI 0.16, 1.19), and meat intake changes were larger for messaging-based apps (-0.21, 95% CI -0.27, -0.15) than apps without these features. Conclusions Our findings suggest to practitioners and policymakers that mobile apps could be a useful and innovative way to change behaviour. Within a wider behaviour change approach, apps could help facilitate diets that meet food-based dietary guidelines, are predominantly plant-based, and are protective of environmental and human health. Key messages • Overall, the results from 21 studies showed positive dietary changes towards more fruits, vegetables, and legumes and less meat consumption following the use of mobile apps. • Apps including messaging and personalised elements appeared most effective and may be important for driving changes in fruits, vegetables, and meat consumption.

Double-finite Serial Verbs in Sanskrit and later Indo-Aryan

Abstract Steever (1988. The serial verb formation in the Dravidian languages. Delhi: Motilal Banarsidass) introduced to South Asian linguistics the notion “Serial Verb” – a Dravidian construction in which two morphologically finite verbs agree in person and number features in the same sentence. I show that similar “double-finite” structures are found in Sanskrit/Indo-Aryan, although details differ. One double-finite construction, inherited from Proto-Indo-European, does not seem to have continued in the post-Vedic language, but others do, and similar structures occur in Modern Indo-Aryan languages. While the historical developments are relatively clear for early Indo-Aryan, from Vedic down to Pāli, there are questions about whether the double-finite constructions of Modern Indo-Aryan are inherited , since comparable structures have not been noted in late Middle Indo-Aryan Apabhraṁśa, the near-ancestor of (peninsular) Modern Indo-Aryan. In the conclusion I discuss alternatives to Modern Indo-Aryan inheritance, including areal and typological factors and the proposal by Milizia (2018. Sul presente neoindoarico a doppio verbo finito. Atti del Sodalizio Glottologico Milanese 12(n.s.). 93–111) that the perhaps most widespread Modern Indo-Aryan double-finite construction results from analogical developments. (Unfortunately, the latter proposal does not account for the other Modern Indo-Aryan double-finite constructions.)

Schizotypy 17 years on: Prediction of schizotypic individual differences in midlife.

The picture for the long-term prediction of schizotypic individual difference features in relation to schizotypy assessed earlier in life remains opaque. Whereas schizotypy assessed earlier in life, typically during the late teen years, has been shown to predict nonaffective psychotic illness as well as the presence of nonaffective psychotic features (Chapman et al., 1994; Lenzenweger, 2021), the presence in midlife (mid-30s) of nonpsychotic schizotypic features in those assessed for schizotypy earlier in life remains to be demonstrated. The current study, which reports on a 17-year follow-up study, addresses this void in the schizotypy literature. Seventeen years after an initial psychometric assessment for schizotypy, in a sample of emerging adults (age = 18) with no prior history of psychotic illness, Perceptual Aberration Scale scores predicted elevated schizotypal personality features, increased schizophrenia-related personality disorder features (particularly schizotypal and paranoid), and elevated schizophrenia proneness scores at age 35. This pattern of associations was maintained even after the removal of participants with a diagnosis of nonaffective psychosis. The associations also remained largely unchanged net of state anxiety levels at initial and later assessments. These results support the emergence or maintenance of schizotypic psychopathology features consistent with a model that views schizotypy as the underlying liability for schizotypic psychopathology phenotypes. The results also provide additional support for both the construct validity of the initial psychometric schizotypy measure (Perceptual Aberration Scale) as well as the validity of the psychometric high-risk paradigm. Longitudinal research remains an illuminating and informative approach to understanding the nature of schizophrenia-related psychopathology by utilizing time as an essential scientific lever. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Relationship of Tetrad Dark Personality Traits and Delinquent Behavior among Adolescents: Moderating Role of Social Support

The current research studies the relationship between Dark Tetrad personality dimensions: Machiavellianism, narcissism, psychopathy, sadism, and delinquency in adolescence while considering the role of social support as the moderator. For this study the cross-sectional research design was used and the sample included 300 adolescents comprising 150 males and 150 females in the age group of 10 to 19 years randomly selected from twin cities of Islamabad and Rawalpindi. The data were collected using the Short Dark Tetrad (SD4) self-reported delinquency – Problem Behavior Frequency Scale and Child and Adolescent Social Support Scale (CASSS). The analyses conducted found positive and significant correlations between the dark personality traits and the delinquent behaviors with each of the dark personality traits being positively correlated with the manifests of delinquent behaviour. Furthermore, the study revealed that social support acted as a moderator that could help decrease the delinquency rate among the adolescents who possess a high level of dark personality traits. These imply that dark personality facets and insufficient social support should be targeted to reduce delinquency among adolescents. The results imply that any prevention or treatment programs targeted at decreasing delinquency must take into account the presence of dark personality features in adolescents, especially through modulating the perceived usefulness of social support. The insights from the research can be quite useful for schools, families, and community-oriented programs to design effective interventions not only to address behavior management but also to address the potentially detrimental effects associated with these personality traits.

Goal-Setting, Active Practice, Self-Monitoring: a Web-Based System to Improve Programming Proficiency

Mastering programming fundamentals poses a significant challenge for students, especially in demanding higher education environments. This pilot study investigates the effectiveness of a web-based system, SoftwareSkills, designed to enhance programming proficiency among second-year bachelor students. SoftwareSkills leverages, active practice, and self-monitoring to support programming practice. Thirty-six students (n=36) participated in a quasi-experimental design, divided into independent exploration and instructional session groups. User interaction data, exercise performance, and behavioural patterns were analysed. The study revealed four distinct learning strategies: reviewing skill masteries, dedicating sufficient time to each question, striving for mastery, and adapting spaced practice. However, some students exhibited rapid responses and minimal practice, suggesting a need for deeper learning strategies. These findings highlight the suitability of self-regulated learning and deliberate practice in programming education. Future work will involve first and second-year programming students and integrate AI-driven personalised learning features to optimise the learning experience. This research contributes insights into effective practices and the potential of technology-aided learning to support student success in programming education.

Learning personalized drug features and differentiated drug-pair interaction information for drug–drug interaction prediction

Multi-drug combination therapies are increasingly used for complex diseases but carry risks of harmful drug interactions. Effective drug–drug interaction prediction (DDIP) is essential for assessing risks among numerous drug pairs. Most DDIP methods involve two main steps: drug representation and drug pair interaction extraction, respectively challenged by the loss of personalized drug information and the need for differentiated interaction data, and are rarely studied. Specifically, personalized drug information refers to the distinct features of each drug. These properties can be easily confused by neighboring information during graph propagation. This issue is especially prominent in drug interaction graphs with long-tail distributions, which poses challenges for personalized drug information learning. Furthermore, it is crucial to learn interactions with differentiation in order to identify diverse drug relationships. Some methods simply concatenate drug features, often ignoring the differences of different drug relationships, while other methods based on substructures rely on professional pharmacological knowledge and are computationally complex. To address these issues, we propose a novel method, learning personalized Drug Features and differentiated Drug-Pair interaction information for drug–drug interaction prediction (DFPDDI). This approach employs a contrastive learning network with edge-aware augmentations and mutual information estimators to capture personalized drug features across various graph distributions. Furthermore, it applies a mutual information constraint to drug-pair representations, enhancing the accuracy of interaction predictions by better distinguishing between different types of drug relationships. The results evaluated on three public datasets demonstrate competitive performance compared to baselines. It also shows potential for accurate predictions, particularly in imbalanced-distribution graphs.

To the problem of digital immortality

The idea of reproducing personality by means of digital (neural network) technologies (“digital immortality”), together with the concept of Digital Twin (DT), still attracts great attention. Recent advances in the DT industry permit to expect the production of perfect “mirror” DT in the near future. We argue that the “immortality in the memory of other people” could be approached quite closely due to creating an analogue of personal DT by simulating the personality. For this purpose, it is necessary to compose the “constructive portrait” of a chosen person (by extracting the key features and traits of personality) and try to reproduce it by means of a chosen model. We are developing an original model Natural Constructive Cognitive Architecture (NCCA) that inherently provides the interpretation of logical and intuitive thinking, subconscious, etc. This model should be adjusted to specific set of knowledge inherent in a particular person (books, films, photographs, etc.), with an emphasis on personal lexicons (verbal, emotion, behavioral). NCCA contains a large set of free model parameters, which enables us to reproduce a wide range of personality features, from thinking style to temperament. It is shown that popular Generative Pre-trained Transformers (GPTs) have much in common with NCCA and could be adapted and used as an analog of DT of a specific person. We argue that the proposed program would provide the possibility to create an analog of DT, which could give an impression (at least, an illusion) of communication with the desired specific person.

Effects of childhood adversities on alexithymia features vary between sexes. Results of a prospective population study

ABSTRACT Introduction: Adverse childhood experiences (ACEs) associate with various mental disorders, including personality features. Our understanding of how ACEs influence alexithymia features in the general population is limited. In a prospective population setting, we studied whether ACEs associate with alexithymia, and the role of sex and emotional symptoms in this association. Methods: In a Finnish population-based prospective study, 3,142 individuals aged between 30 and 64 years completed eleven ACE questions and the Toronto Alexithymia Scale in 2000 and 2011, and the Hopkins Symptoms Checklist in 2011. The effect of ACEs on alexithymia and its subdomains – difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and externally oriented thinking (EOT) in 2000 and 2011 – was analysed using repeated measures ANOVA. Results: The number of ACEs and their main component, childhood social disadvantage, associated positively with total alexithymia scores and its subdomains DIF and DDF, and negatively with EOT. After controlling for the effect of depression and anxiety, the strength of these associations was reduced, but the effect of social disadvantage on DIF and EOT remained significant in females. Childhood family conflicts associated positively with DIF in males and negatively with EOT in females. Additionally, maternal mental problems associated positively with DIF and DDF in females. Discussion: In the general population, ACEs, particularly social disadvantage, are associated with adult alexithymia features. Alexithymia features, detectable from youth, may predispose individuals to emotional disturbances caused by childhood adversities. The effect of family conflicts and maternal mental problems on alexithymia features varies between sexes.

Multi-branch Collaboration Based Person Re-identification

As computer vision algorithms have advanced rapidly, the widespread deployment of video surveillance systems has enhanced traffic safety and propelled the growth of intelligent highway systems. However, the intricate nature of real-world scenarios, particularly the presence of occlusions, introduces noise that can cause the loss of critical feature information for identified individuals or objects. This poses significant challenges to current person re-identification algorithms. In response, this study introduces an innovative person re-identification approach that leverages a hybrid network architecture. The method performs feature extraction across four collaborative branches: a local branch, a global branch, a global contrast pooling branch, and an associative branch. This comprehensive approach yields a robust and diverse representation of person features, addressing the limitations posed by occlusion and noise in the environment. The neural network presented in this study is versatile and can be integrated with various backbone architectures. Empirical evaluations demonstrate that our proposed algorithm outperforms state-of-the-art techniques, while deep ablation studies substantiate the efficacy of the network’s design. This suggests that the architecture contributes significantly to the performance gains observed.

When great places to work aren't for everyone: How top employer awards can backfire for narcissistic applicants

Drawing on signaling theory, the current experimental study (N = 593) investigated the influence of top employer awards on the connections between the full spectrum of narcissistic personality features (i.e., agentic, antagonistic, neurotic, and communal) and perceived company prestige. The results of our experiment show that employer awards enhance prestige perceptions and that all narcissism facets are positively related to perceptions of prestige. However, the positive associations between narcissistic personality features and company prestige were less pronounced in the award condition than in the no-award condition. Our study contributes to the recruitment and employer branding literature by demonstrating that, while employer awards generally enhance an organization’s prestige, they may not do so for individuals with particular personality features.

Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health

A person's Mental Health (MH) dramatically influences their complete evolution in life, including their cognitive, emotional, and psychomotor components. A person with good MH is content with life and can be creative, learn new things, and take risks to accomplish more significant objectives. Currently, college students are dealing with MH concerns for various causes, which affect their academic performance and significantly contribute to poor academic results. Therefore, encouraging MH in college students presents a significant problem for educators, parents, teacher educators, and governments. Adolescence is a crucial and delicate time characterized by considerable physical, emotional, social, and religious changes. The physical, social, and psychological facets of an individual's growth are laid out in this period, with mental health as a crucial factor in promoting these gains. Therefore, it becomes crucial for researchers to use Deep Learning (DL) algorithms to study the association between MH and vital psychological characteristics, such as emotional intelligence, personality traits, and intelligence. The personal aspects, namely personality, emotional intelligence, and MH are all related ideas that influence one another. Individuals must have mental well-being and emotional harmony to have a good personality. The current study uses DL techniques to investigate the relationship between college students' MH, emotional intelligence, and personality features. To perform a thorough study on emotion identification and Mental Health Prediction (MHP) among college students, this project investigates the integration of edge computing enabled by the Internet of Things (IoT) in the context of intelligent systems. Innovative treatments are urgently needed due to this population's rising prevalence of MH issues. This paper aims to continuously monitor and predict college students' MH using Edge Computing (EC) and IoT technology.

Qualifications of the Ideal Tourism School Administrator: A RepGrid Review

The school administrator has one of the most important roles in the success of an institution and in the execution of processes, especially in some fields like tourism including practical parts. For this success and effective management, there are certain qualities that the school administrator should have. The aim of this study is to examine the cognitive constructs of teachers and to reveal the qualities that the administrator working in tourism should have. Repertory Grid Technique was used in the study, which was prepared according to qualitative research patterns. The study group consisted of 40 teachers working in tourism schools, who worked with at least 6 different administrators. As a result of the study, honesty (η=8, 20.0%), (2) fairness (η=6, 15.0%), (3) personal feature; emotional intelligence; sensitivity (η=5, 12.5%) are the most important qualifications. When these results are taken into consideration, it becomes clear that ideal school administrators should pay attention to these qualifications. It is an important suggestion to pay attention to these qualities in the selection of tourism school administrators.