Individual Attributes Research Articles

Time Framing and SDGs: Can Imaginary Future Generations Alter People’s Perceptions and Attitudes?

This study verifies the effectiveness of Imaginary Future Generation (IFG) in promoting the Sustainable Development Goals (SDGs). Using data from an online survey, we calculated sustainable development component measurements to assess the understanding of SDGs and demonstrated whether IFG can influence futurability components. Statistical analysis shows that knowledge significantly impacts people’s attitudes towards SDGs, with informed individuals more likely to cooperate in addressing these issues. Comparing the treatment and control groups revealed that participants asked to envision themselves as residents of 2050 (IFG) better grasped the essence of Agenda 2030 and exhibited more cooperative attitudes toward sustainability. Regression analysis confirmed these effects after controlling for individual attributes and SDG knowledge. The study finds that while raising awareness is crucial, IFG makes people perceive sustainability problems as personal concerns. IFG enhances respondents’ futurability, encouraging positive attitudes and long-term thinking. Additionally, IFG promotes participation, inclusive ideas, and increased SDG knowledge.

Computed tomography study of cranial vault thickness in Malaysian subadult population.

Cranial vault thickness (CVT) and its variations provide valuable insights into an individual's biological attributes such as age and sex. This paper aimed to assess the correlations of CVT with age and sex and develop a regression model for age estimation in Malaysian subadults using computed tomography (CT) images. A total of 521 CT images (male/female: 279/242; age range: 0-20 years; Malay/Chinese/Indian: 221/145/155) were included in the study. Correlations of CVT measurements with age and sex, including frontal bone thickness (FBT), occipital bone thickness (OBT), left parietal bone thickness (LPBT), and right parietal bone thickness (RPBT) were assessed and regression formulae were developed for age estimation in subadults. A significant correlation between CVT measurements and age was demonstrated (p < 0.001). Age estimation was most accurate in the younger age group (< 2 years) at frontal and occipital, and accuracy decreases in the older age groups. Additionally, sexual dimorphism was evident in the frontal and parietal bone thickness within the age range of 3-6 years and 16-20 years, respectively. In conclusion, the findings suggested CVT measurements could be used to corroborate other age estimation methods for subadults.

Cortical Alterations Associated with Executive Function Deficits in Youth with a Congenital Heart Defect

Abstract Adolescents and young adults born with a complex congenital heart defect (CHD) are at risk for executive function (ExF) impairments, which contribute to the psychological and everyday burden of CHD. Cortical dysmaturation has been well described in fetuses and neonates with CHD and early evidence suggests that cortical alterations in thickness, surface area, and gyrification index are non-transient and can be observed in adolescents with CHD. However, cortical alterations have yet to be correlated with ExF deficits in youth with CHD. This study aims to use a data-driven approach to identify the most important cortical features associated with ExF deficits in adolescents and young adults with CHD. To do so, we combined two comparable datasets acquired at the Research Institute of the McGill University Health Centre and the University Children’s Hospital Zurich each including both youth with CHD and healthy controls. For each participant, a high-resolution T1-weighted magnetic resonance image, a self-reported ExF assessment (the Behaviour Rating Inventory of Executive Function— Adult Scale), and their clinical and demographic characteristics were available. Corticometric Iterative Vertex-Based Estimation of Thickness (CIVET) was used to extract cortical thickness, cortical surface area, and local gyrification index measures. Using orthogonal projective non-negative matrix factorization (OPNMF), we identified non-overlapping spatial components that integrate cortical thickness, cortical surface area, and local gyrification index and capture structural covariance across these features. Behavioural partial least squares correlation (bPLS) analysis was then used to compute correlations between the individual variability in the OPNMF covariance patterns and ExF outcomes for each subject. A total of 56 youth with CHD who underwent cardiopulmonary bypass surgery before three years of age and 56 age- and sex-matched healthy controls were included in our analyses. Cortical grey matter volume, cortical thickness, and cortical surface area were found to be significantly reduced in CHD patients compared to controls. OPNMF identified 12 stable cortex-wide components summarizing the inter-subject variability in cortical thickness, cortical surface area, and local gyrification index. bPLS revealed two significant latent variables (LV) accounting for a total of 82.8% of the variance in the sample, each describing distinct patterns between the brain and cognitive data. LV1 summarized a pattern of belonging to the CHD group, worse scores on most BRIEF-A scales, younger age at MRI, and female sex. This pattern was associated with increased cortical thickness, local gyrification index, and decreased cortical surface area in several OPNMF components. Finally, we identified a positive relationship between the LV1 brain-behaviour pattern and total aortic cross-clamp time in the CHD group, indicating that longer aortic cross-clamp time was associated with worse neurodevelopmental outcomes. In this study, we uncover novel multivariate relationships between ExF and alterations in cortical thickness, surface area, and local gyrification index in adolescents and young adults with CHD using a data-driven approach. Although our findings highlight the important role played by the cortex in higher order cognitive processes, future studies are needed to elucidate the individual contribution of individual and clinical attributes into the deficits observed in this population.

Children's friendship stability in the United States, China, and Indonesia: Associations with individual attributes and dyadic similarity.

Predictors of friendship stability from individual attributes and dyadic similarities were assessed using cross-classified multilevel analyses in this 6- to 8-month longitudinal study of 10-year-old US (White, Black, Asian, other; n = 477, 50% girls), Chinese (n = 467, 59% girls), and Indonesian (Sudanese, Javanese, other; n = 419, 45% girls) children with complete participation and reciprocated baseline friendships. Across countries, individual attributes of social preference, popularity, and academic achievement and dyadic social preference similarity positively predicted friendship stability. Dyadic similarity of popularity, academic achievement, and aggression respectively predicted friendship stabilities of US, Chinese, and Indonesian children. Both individual attributes and dyadic similarity predicted friendship stability, with results that varied across countries consistent with attributes' reputational salience.

Exploring economic assimilation of immigrants and their descendants in France: recent insights

ABSTRACT This paper examines the labour market performances of immigrants and their descendants in France by applying a series of empirical Tobit II models on a recent TeO2 database. Our work questions the labour market participation and wage disparities of these groups compared to native-born individuals by controlling different individual attributes, such as gender and educational level, and job characteristics, such as the firm size or the type of job. It also introduces information on ethnic origins and location. Our study provides crucial insights into the labour market performances of immigrants and their descendants in France. It reveals that disparities manifest primarily in labour market participation rather than wages. It also underscores that high-skilled workers’ wages do not converge as immigrants consistently yield lower returns on education than natives. Our findings underscore the heterogeneity within the immigrant group based on origins criteria. The performances of immigrants with African and Northern-African origins are lower than those of other groups of immigrants.

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM.

Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a sparse dynamic causal model (sDCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. sDCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range (31 females), providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model predicting the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects (173 females) selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.Significance statement By inferring region-wise hemodynamic profiles at the individual level, this is the first study providing an exhaustive whole-brain characterization of the hemodynamic sensitivity to healthy aging, reporting further evidence of the vascular changes across the adult lifespan. Using a predictive framework, we analysed the statistical influence of advancing age on individual regional hemodynamic attributes, offering a quantitative evaluation of the diverse hemodynamic bias across different brain regions. We then unveiled a specific set of hemodynamic predictors to discriminate young from elderly people, mainly describing vascular properties of right-hemispheric areas. This suggests the asymmetric nature of vascular degeneration processes affecting the human brain at the latest stage of life, other than a potential biomarker that could be relevant for brain-age prediction.

Exploring Individual Factors Associated with the Prevalence of Cybercrime Victimization Among Students at Egerton University, Kenya

In the modern digital epoch, identifying the factors linked to cybercrime victimization is crucial, particularly among the youth, who are often more exposed to such risks. This study examined the role of individual attributes, namely gender, age, and academic level, in influencing university students’ vulnerability to seven distinct cybercrimes. Originating from Egerton University’s primary campus in Njoro sub-county, Nakuru County, Kenya, the research harnessed structured questionnaires to collect quantitative data. Participants’ ages varied from 18 to over 26 years. The gender distribution comprised 57.2% males and 42.8% females. Regarding academic engagement, 4.5% were diploma students, a majority of 92.6% were undergraduate students, and a smaller 2.9% were master’s students. Notably, our analyses indicated that while gender did not significantly influence cybercrime victimization, age, and academic level emerged as influential factors. This paper further explores these findings, highlighting their implications in the broader cybersecurity awareness and education framework.

Stacked-gait: A human gait recognition scheme based on stacked autoencoders.

Human gait recognition (HGR) is the mechanism of biometrics that authors extensively employ to recognize an individuals based on their walking traits. HGR has been prominent for the past few years due to its surveillance capability. In HGR, an individual's walking attributes are utilized for identification. HGR is considered a very effective technique for recognition but faces different problematic factors that degrade its performance. The major factors are variations in clothing, carrying, walking, etc. In this paper, a new hybrid method for the classification of HGR is designed called Stacked-Gait. The system is based on six major steps; initially, image resizing is performed to overcome computation problems. In the second step, these images are converted into grey-scale to extract better features. After that, the dataset division is performed into train and test set. In the next step, the training of the autoencoders and feature extraction of the dataset are performed using training data. In the next step, the stacking of two autoencoders is also performed. After that, the stacked encoders are employed to extract features from the test data. Finally, the feature vectors are given as input to various machine learning classifiers for final classification. The method assessment is performed using the CASIA-B dataset and achieved the accuracy of 99.90, 98.10, 97.20, 97.20, 96.70, and 100 percent on 000, 180, 360, 540, 720, and 900 angles. It is pragmatic that the system gives promising results compared to recent schemes.

Resilience of nursing students: A concept analysis study

ObjectivesThis study aimed to identify attributes of resilience among nursing students. DesignThe concept analysis method was used. Data sourcesLiterature reviews from the past 10 years were searched in Science Direct, Cumulative Index to Nursing and Allied Health Literature, PubMed, and the Wiley online library. The literature search database and review period were determined after verification of validity by five experts (nursing professors). The inclusion criteria for the literature review were that the paper must be written in English, and the abstract or title of the paper should have included attributes of nursing students' resilience. Exclusion criteria were papers written in languages other than English, papers that did not include the attributes of resilience of nursing students, and duplicate papers. Seventeen studies were included in the analysis. ResultsResilience among nursing students comprised three attributes and 28 sub-attributes. The three attributes were individual, educational, and situational. Individual attributes included personal competency characteristics (e.g., self-confidence and self-efficacy) and consisted of 15 sub-attributes. Educational attributes included characteristics related to academic and clinical practicum (e.g., coping with academic work pressure) and consisted of seven sub-attributes. Situational attributes comprised characteristics that changed according to circumstances (e.g., coping with increased levels of patient acuity during the crisis), such as the COVID-19 pandemic and social support from significant others, and consisted of six sub-attributes. ConclusionsThis study identified resilience attributes reflecting the characteristics of nursing students and recent situational changes. Consequently, tools to assess resilience should be developed based on study findings and utilized in various studies aimed at enhancing the resilience of nursing students.

This Is an Eventful Era: Exploring Event-Oriented Approaches to Organizational Research

This special issue explores the transformative role of discrete events in fostering changes at different organizational levels, challenging traditional feature-oriented approaches that focus on stable attributes of individuals, groups, and organizations. Joining the growing body of event-oriented research in diverse settings, the nine published articles evoke a novel theoretical lens (i.e., Event System Theory) to examine a number of discrete events (e.g., everyday change events, organizational downsizing, merger, corporate scandal, technology implementation, the U.S.–China trade war, the Black Lives Matter movement). Their findings demonstrate the interesting ways discrete events disrupt routines, prompt adaptation, and impact individual and collective behaviors across various levels within organizations. Our further analysis underscores the importance of adopting an event-oriented perspective for a better understanding of management issues, offering new insights and directions for future research. We hope this special issue provides a robust foundation for integrating event-centric research approaches into organizational theory, emphasizing the need for continued empirical investigation and theoretical refinement in a variety of management research domains, such as strategic management, organizational behavior, entrepreneurship, and human resource management.

Factors Influencing Self-Wound Care Adoption in Singaporean Communities: A Cross-Sectional Survey.

Managing wounds often requires frequent clinic visits, posing a burden on both patients and the healthcare system. Shared wound care, which encourages patients to manage dressings at home, has emerged as a potential solution. This study investigates factors influencing self-wound care adoption in Singapore. A cross-sectional survey involving 328 participants from six primary healthcare centers was conducted. The survey examined demographics, wound characteristics, and perceptions towards shared wound care using a locally validated questionnaire. Parametric tests and logistic regression were used to analyze the data. Unlike older participants, younger adults valued time saved through self-care and found clinic visits inconvenient. Participants in the younger age group, those in fixed-schedule employment, and students were more likely to opt for the self-wound care program and adopt self-wound care. Strong social support (p=0.034) and wound location on the head, neck, or face were the most significant factors (p=0.023) favoring self-care. Interestingly, participants with no formal education were significantly more likely to embrace self-wound care (p=0.006 to 0.012). Using individual attributes to select participants for self-wound care adoption may be more effective than relying on educational level. Strong social networks and encouragement from family and community can play a crucial role in promoting self-care practices. However, the generalizability of the findings may be limited, as the study was conducted solely within a Singaporean primary healthcare setting. All the same, while wound care practices may vary across countries, the foundational concepts of wound care are universal in the world. Insights about self-wound care can therefore be valuable and informative on a global scale, not just within Singapore.

Analysis of causes of traffic accidents for taxi drivers based on MIMIC and machine learning

Through questionnaire survey, information on individual attributes, physical fatigue perception, work pressure, risky driving behavior and traffic accident experience of 2391 taxi drivers was collected. The multi-indicator multi-cause (MIMIC) model was used for path analysis to explore the inducing effect of physical fatigue perception and risky driving behavior on traffic accidents, and to verify the influence of gender, age and work pressure on physical fatigue perception and distracted driving behavior. Four machine learning algorithms, logistic regression, naive Bayes, support vector machine and random forest, were selected to predict taxi accidents. The results show that the increase of physical fatigue perception, negligent driving behavior, aggressive driving behavior and distracted driving behavior can lead to an increase in accident rate, and gender, age and work pressure will affect the frequency of physical fatigue perception and distracted driving behavior. The accident prediction model based on machine learning has excellent effect, among which the random forest has the best prediction effect. The prediction accuracy of using single feature variables "risky driving behavior", "distracted driving behavior" and "physical fatigue perception" is acceptable. When the personal attribute indicators of "work pressure", "age" and "gender" are introduced, the prediction accuracy is further improved.

Potential risk and efficiency analysis of decision-making dilemmas in connected dual-vehicle interactions at uncontrolled intersections

In dual-vehicle interactive scenarios, drivers often encounter decision-making dilemmas, even with connected information, thus posing significant risk challenges to traffic operations. This study explored the impact of such dilemmas on decision-making and traffic operations for drivers involved in straight-going (SG) and left-turning (LT) vehicles at uncontrolled intersections. Employing driving simulation technology, a decision-making experiment incorporating preemptive (P) and yielding (Y) strategies was conducted with 72 S G and 72 L T drivers under connected information provision. By analyzing joint decisions (P-P, Y-P, and Y-Y) made by drivers on both sides, we investigated implications for traffic operational safety and efficiency. Furthermore, recognizing the diversity in individual characteristics and time pressure, we developed a random parameters multinomial logit model to examine the relationship between potential traffic operational performance (both safety and efficiency) and various contributing factors. Moreover, we calculated the marginal effects of these contributing factors. By assessing the probability change of Y-P joint decisions balancing safety and efficiency, we identified reference values for each contributing factor that promote effective interactive operations. The findings suggest that: (1) Regarding interactive dilemmas conditions, Conditions where the SG vehicle had higher speed and farther distance than the LT vehicle were more likely to result in heterogeneous Y-P joint decisions, in which one driver chooses to yield and the other party passes quickly, thereby improving traffic performance. (2) Time pressure increased the likelihood of heterogeneous Y-P joint decisions but also increased P-P joint decisions. (3) heterogeneous individual attributes did not always lead to higher heterogeneous decisions. Overall, this study provides insights into factors influencing joint decisions and traffic operational performance under connected information provision, considering individual heterogeneity and time pressure, aiming to the advancement of more efficient and safer vehicle-connected transportation systems.

Statistical Law between Areas and Perimeters Created by a Moving Trajectory

Based on our interest in properties of human movement, we investigated Japanese GPS data, and arrived at the following three observations: (1) there is a strong correlation between the area of polygons created by human movement trajectories and their perimeters; (2) short-distance movement trajectories less than 5 km tend to enclose a large area like a circle; and (3) long-distance movement trajectories over 5 km tend to be straight. We also clarified the following four observations on individual attributes and external factors related to long-distance movements: (1) women tend to travel more linearly than men; (2) linearity is stronger on weekends and national holidays in areas with a large theme park; (3) linearity is weaker on weekends and holidays in areas with many historical tourist attractions; and (4) these variations are created by people visiting such areas. These properties should be incorporated when modeling the movement trajectories of humans.

Resilience Mechanisms and Coping Strategies for Forcibly Displaced Youth: An Exploratory Rapid Review.

Context: The global escalation of conflict, violence, and human rights violations sets a pressing backdrop for examining the resilience of forcibly displaced youth (FDY) in Canada. This study aims to unpack the multifaceted challenges and resilience mechanisms of FDY, focusing on their health, well-being, and integration into host communities. It seeks to identify current models of resilience, understand the factors within each model, and highlight gaps and limitations. Methodology: Using a university librarian-supported structured search strategy, this exploratory rapid review searched literature from Ovid Medline and open-source databases, published in English between January 2019 and January 2024, that fit specific inclusion criteria. The eligible articles (N = 12 out of 4096) were charted and analyzed by two student researchers with the Principal Investigator (PI). Charted data were analyzed thematically. Results: The selected studies captured diverse geographical perspectives, resilience models (such as Ungar's ecological perspective and Masten's resilience developmental models), as well as protective and promotive frameworks. Key findings indicate the complexity of resilience influenced by individual, familial, societal, and cultural factors. Each model offers insights into the dynamic interplay of these influences on FDY's resilience. However, these models often fall short of addressing the nuances of cultural specificity, the impact of trauma, and the intersectionality of FDY's identities. Conclusions: Recognizing the diverse and evolving nature of FDY's coping mechanisms, this study advocates for a culturally appropriate approach to resilience that integrates an intersectionality framework of individual attributes and culturally sensitive models.

Comparative Analysis of Different Transport Layer Protocol Techniques Incognitive Network

Most of the networks employ TCP protocol for transmission control in transport mechanism. Although it offers numerous services such as reliability, end-to-end delivery, secure transmission of data, and so on to applications functioning across the World Wide Web, TCP needs to have effective congestion management techniques in order to handle traffic with a lot of data. Still, TCP have poor performance during data transmission in the network. The network community continues conducting research to develop a method that should provide a fair and effective transmission bandwidth distribution. Numerous congestion control strategies have been developed based on previous research in this area. This work discusses, identifies, compares, and analyses the behaviour of a few network congestion control strategies to determine their benefits and limitations. The widely known network simulator ns2 is employed for the simulation. The performance metrics for QTCP, TCP new Reno, TCP- Hybla, L-TCP and RL-TCP are throughput, average delay, PDR, packet loss, average jitter, latency and fairness are considered. The RL-TCP exhibits superior performance in multiple measures when it is compared to alternative TCP protocols, as indicated by simulation results. These metrics encompass throughput, average delay, packet delivery ratio (PDR), packet loss, jitter, latency, and fairness. Furthermore, several TCP protocols, such as L-TCP, TCP-Hybla, QTCP, and TCP-New Reno, have undergone evaluation, uncovering disparities in their individual performance attributes. Nevertheless, the RL-TCP regularly demonstrates superior performance in all aspects.

Career trajectories in retail and wholesale –turnover, retention, and labor status

ABSTRACT This study evaluates career trajectories in the retail and wholesale sectors, focusing on individual attributes and the underlying factors influencing diverse career outcomes. Employing quantitative (sequence analysis and logit estimations) and qualitative (interview study) methodologies, we examine the pre-pandemic era to systematically map prevalent career trajectories in retail and wholesale. We identify personal traits associated with each career outcome, examine the reasons behind these trajectories, and explore strategies businesses can implement to retain their workforce. Our findings indicate that retail and wholesale sector employees typically experience either a prolonged career or a transient one, where initial jobs serve as a springboard to other industries or higher education. In retail, the likelihood of industry switching is lower among males, older individuals, those without children, and individuals with less education. In wholesale, a similar trend is observed, albeit without notable gender disparities. A key factor for employee turnover is the absence of skills development opportunities.

Criminalising survivors of modern slavery: the United Kingdom’s National Referral Mechanism as a border-making process

Abstract The United Kingdom’s National Referral Mechanism (NRM) is a framework for identifying potential victims of modern slavery (slavery, servitude, forced labour or human trafficking) and ensuring that they receive adequate care. This research explores differences in referrals and outcomes of potential modern slavery victims within the NRM on the basis of individual attributes, geography and first responder. Findings are based on exploratory analysis of data on 55,000 cases released by the Home Office in spring 2022 plus data from four Freedom of Information requests. Findings confirm that there are significant differences in rates of positive outcomes between native and immigrant groups, with native populations more likely to receive positive conclusive grounds (CG) decisions. Our key contribution is in identification of the role of the first responder in negatively influencing outcomes for victims of particular forms of exploitation. We suggest the differences in outcomes may be explained by the dual role played by first responders within the immigration system in identifying victims and implementing immigration control measures. We situate this finding within a broader critical migration literature on polymorphous borders pointing to the NRM as one mechanism through which bodies are differentially excluded from territorial access and associated rights or benefits.

Gender differences in the perception of the indoor environment: Findings from residential buildings in a nordic climate

Current research suggests gender (“sex assigned at birth”) differences with respect to indoor environmental conditions, indicating females are more critical and demanding towards satisfaction and preferences. The present paper analyzes the results of a subsample of a national survey completed in 375 representative dwellings in Sweden. The survey collected information on twelve comfort factors and satisfaction aspects concerning thermal comfort, indoor air quality, acoustic comfort, satisfaction with daylight, size, standard, layout, appearance, well-being, cost, and neighborhood. Advanced statistical analyses were used to investigate whether the responses of occupants experiencing similar indoor conditions (cohabitation) were different with respect to gender. The analysis did not observe any significant gender differences with respect to the ratings of indoor environments in dwellings. Males reported slightly higher satisfaction, while thermal and acoustic comfort exhibited the highest gender variability. Satisfaction aspects were generally very high, with the costs and acoustic comfort registering the highest levels of dissatisfaction. Additional analyses across various building characteristics and individual attributes (variables levels) confirmed no differences. Several hypotheses were put up to explain these results, including the extreme climatic conditions and the collaborative use of dwellings. The findings suggest that Swedish dwelling designs can provide some best practice guidance for stakeholders and practitioners elsewhere in similar climatic conditions. Future studies should confirm the present observations and the social and cultural aspects of the findings.

Application of machine learning classification algorithms to predict probability of channel presence from seismic data

The article analyzes the application of various classification algorithms (Random Forest, Extra Trees, Gradient Boosting, Multi-layer Perceptron, Gaussian Naive Bayes, Voting Classifier) based on supervised machine learning, both for individual seismic attributes (amplitude, phase, frequency) and for a set of seismic attributes calculated in the interval of channel system development for probabilistic assessment of presence of channels. The object of the study is the deposits of the Tyumen suite of a group of deposits located in the Khanty-Mansiysk Autonomous Okrug. The purpose of this work is to find the most effective seismic attributes and machine learning classification methods to optimize the methodology and improve the accuracy of forecasting the probability of channel system presence.