Individual Faces Research Articles

Inter-Role Conflict and Mental Health in Early Adulthood Females

Inter-role conflict is one of the most common and challenging realities. It occurs when an individual faces dual demands from different roles, and the situation is likely to cause a lot of psychological strains. This kind of conflict was most prevalent among females in the early adulthood age of development, a period that involves a turning point to other important developmental tasks in life such as procurement of employment, further educational pursuits, and formation of settlement or marriage. Work and family responsibilities become intertwined during this phase, as the drive for career advancement merges with other obligations, potentially impacting one's health. The effects of inter-role conflict on mental health can be significant, impacting such outcomes as anxiety, depression, and stress. Anxiety might manifest itself through constant apprehension or a low probability of performing well in one or more positions. At the same time, depression can cause a lack of self-worth and a negative outlook on life. Such mental health issues can lead to maladjustment in behavior, which means that one can become less productive at work as well as seclusion in their relationships. Inter-role conflict, if persistent, causes chronic stress, which worsens these concerns and may lead to irreversible mental health problems. This review also explores the impact of inter-role conflict on mental health outcomes and explores potential strategies to mitigate these effects. Some realistic approaches to reducing the psychological load include time management, role prioritization, and seeking social support. This literature review emphasizes the importance of psychological strategies from existing literature to help young women manage their roles more effectively. Therefore, by integrating literature from the existing research, this study illuminates how inter-role conflict affects early adulthood females’ mental health with a focus on coping strategies.

No sensitive period for the development of individual face learning in polistes fuscatus wasps

No sensitive period for the development of individual face learning in polistes fuscatus wasps

Investigating Digital Illiterate Classification Techniques Based on DeepFace Technology

This paper presents an algorithm to identify digital illiterates by analyzing age and emotions from facial recognition. In this paper, digital illiterates refer to people who struggle in using digital devices. The study assumed that older individuals who display surprised or angry facial expressions while using digital devices are more likely to be identified as digitally illiterate. For age detection, the study used MTCNN (Multi-task Cascaded Convolutional Networks), and for emotion detection, it employed the VGG-Face model. MTCNN detects facial features and landmarks to preprocess images and distinguish facial characteristics. The VGG-Face model uses convolution operations to analyze facial images and classify emotional states. The dataset used in the study consisted of 3,000 facial images collected from the internet. The research team categorized the images into faces of individuals aged over 50, angry expressions, and surprised expressions. The dataset included 411 individuals (13.7%) aged over 50, 163 individuals (5.4%) with angry expressions, and 145 individuals (4.8%) with surprised expressions. Accuracy was estimated by comparing the results from the DeepFace algorithm with those from the research team’s classifications. The DeepFace algorithm achieved 95.77% accuracy in detecting individuals aged over 50, 83.45% accuracy for surprise, and 76.07% for anger. The results demonstrate that it is possible to identify digital illiterates based on their age and emotional expressions and could enable the development of personalized services to directly or indirectly support digital illiterates, potentially improving digital accessibility.

The impact of a customized aesthetic prosthetic leg cover on social interaction cues and attitudes in the general UK population: Two experimental studies.

To explore the impact of an aesthetic prosthetic leg cover on attitudes toward individuals with lower-limb amputation and associated social interaction cues among the general UK population. Two novel experimental methodologies. In study 1, participants (n = 188) viewed 1 of 3 images of an individual: (1) wearing a traditional stem prosthetic, (2) wearing an aesthetic leg cover, or (3) as a nonamputee. They then completed an attitude scale and rated the personality of the individual using the 10-item Big Five Inventory. Study 2 (n = 31) used eye tracking and facial expression software to explore gaze and facial arousal when viewing 1 of 9 manipulated videos of the same individual talking about 3 different scenarios. In study 1, the aesthetic leg cover led to significantly higher ratings of agreeableness than stem and nonamputee images and significantly higher ratings of extraversion than the nonamputee image. Attitudes toward an individual with a prosthesis did not significantly differ depending on which image they viewed and were generally positive. In study 2, all participants focused mainly on the individual's face regardless of scenario topic or leg condition, although participants focused more around the leg cover in more active contexts. Customized aesthetic leg covers may help individuals living with amputation to be perceived more positively in social situations. These experimental methodologies could be extended to explore the differential impact of context, functionality, and activity of specific types of customized aesthetic prosthetics and could help inform shared decision-making processes in clinical settings.

High-resolution regional sea-ice model based on the discrete element method with boundary conditions from a large-scale model for ice drift

Abstract Understanding sea-ice dynamics at the floe scale is crucial to comprehend polar climate systems. While continuum models are commonly used to simulate large-scale sea-ice dynamics, they have limitations in accurately representing sea-ice behaviour at small scales. DEMs, on the other hand, are well-suited for modelling the behaviour of individual ice floes but face limitations due to computational constraints. To address the limitations of both approaches while combining their strengths, we explored the feasibility of using a DEM within a continuum model, where the latter provides boundary conditions for a rectangular high-resolution DEM domain. This paper presents a feasibility study where a discrete model of a 100 × 100 km2 icefield was created using high-resolution optical satellite imagery. Sea-ice dynamics were simulated in the DEM considering environmental forces and integrating large-scale ice-drift velocities as boundary conditions. Model predictions were compared with satellite observations for ice drift and deformation parameters. This numerical approach showed potential for offering accurate, high-resolution predictions of sea ice, particularly in coastal areas and near islands, and may find applications in ice navigation and climate studies. However, further development of the DEM, along with upgrades to the coupled ocean models providing data for the ice component, may be necessary. Additionally, challenges remain to develop a two-way coupling between the DEM and a continuum model, which may be needed to improve the accuracy of large-scale simulations.

DeepFaceReshaping: Interactive deep face reshaping via landmark manipulation

Deep generative models allow the synthesis of realistic human faces from freehand sketches or semantic maps. However, although they are flexible, sketches and semantic maps provide too much freedom for manipulation, and thus, are not easy for novice users to control. In this study, we present DeepFaceReshaping, a novel landmark-based deep generative framework for interactive face reshaping. To edit the shape of a face realistically by manipulating a small number of face landmarks, we employ neural shape deformation to reshape individual face components. Furthermore, we propose a novel Transformer-based partial refinement network to synthesize the reshaped face components conditioned on the edited landmarks, and fuse the components to generate the entire face using a local-to-global approach. In this manner, we limit possible reshaping effects within a feasible component-based face space. Thus, our interface is intuitive even for novice users, as confirmed by a user study. Our experiments demonstrate that our method outperforms traditional warping-based approaches and recent deep generative techniques.

Flexible use of facial features supports face identity processing.

People prioritize diagnostic features in classification tasks. However, it is not clear whether this priority is fixed or is flexibly applied depending on the specific classification decision, or how feature use behavior contributes to individual differences in performance. Here we examined whether flexibility in features used in a face identification task supports face recognition ability. In Experiment 1, we show that the facial features most useful for identification vary-to a surprising degree-depending on the specific face identity comparison at hand. While the ears and eyes were the most diagnostic for face identification in general, they were the most diagnostic feature for just 22% and 14% of identity decisions, respectively. In three subsequent experiments, we find that flexibility in feature use contributes to an individual's face identity matching ability. Higher face identification accuracy was associated with being aware of (Experiments 2 and 4) and attending to (Experiments 3 and 4) the most diagnostic features for a specific facial comparison. This conferred an enhanced benefit relative to focusing on features that were diagnostic of face identity decisions in general (Experiment 4). We conclude that adaptability in information sampling supports face recognition ability and discuss theoretical and applied implications. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Conducting fieldwork amidst earthquakes in Nepal: insights from a tourism researcher

This study presents a comprehensive account of the first-hand experiences encountered by a doctoral researcher during fieldwork conducted in Kathmandu, Nepal, in 2015, where two significant earthquakes struck. While existing literature extensively examines the vulnerability of participants, little attention has been given to the challenges faced by researchers themselves within such disaster settings. To address this gap, this study employs an autoethnographic approach to provide a personal and experiential narrative of the researcher’s time in the field. The results of this study shed insight on the difficulties that individual researchers face when working in disaster-affected areas, which is a major contribution to the field of tourism and disaster research. Moreover, it unveils the interplay between individual experiences, logistical intricacies and psychological well-being. This study delves into the practical implications associated with conducting research amidst volatile and uncertain conditions, emphasizing the pivotal role of robust disaster preparedness and response strategies in regions affected by calamities.

La educación física, el deporte y la actividad física a lo largo del curso de vida: implicaciones para la salud y el bienestar

Dear Readers: The research journal Cuerpo, Cultura y Movimiento, in its purpose of disseminating scientific knowledge resulting from research and reflection exercises at national and international level, on this occasion is aimed at those interested in deepening the theory and practice on issues related to physical education aimed at improving the quality of life of children and adolescents, physical activity and health, and sport and its planning processes. In this editorial it is important to understand that life is a continuum of interactions that occur over time and at specific moments; however, human development and health outcomes depend on the experiences and situations that each individual faces in the family, social, economic, environmental and cultural context. In this sense, sport, physical activity, physical exercise, physical education and health become fundamental factors to improve the quality of life in differential populations; this combination of elements throughout all stages of life creates a solid foundation for the integral development of the human being. Hence, health promotion is crucial for individual and collective wellbeing, necessary for the construction of healthier communities.

Wartości i przekonania. Ile etyki jest w etykach stosowanych?

The measure of the usefulness and credibility of applied ethics is its ability to counteract conflicts dividing social and professional groups. To achieve this goal, representatives of applied ethics must meet a basic condition by providing an epistemological justification of the claim to the validity of the judgments they formulate. Yet it is here that they encounter obstacles recognized (earlier) by ethical philosophers and sociologists of knowledge. The problem is not only the variety of moral challenges that an individual faces, but also the way in which their moral attitude is influenced by the group to which they belong. This article attempts to resolve both of these difficulties in a way that is consistent with the goals of applied ethics.

Label distribution learning for compound facial expression recognition in‐the‐wild: A comparative study

AbstractHuman emotional states encompass both basic and compound facial expressions. However, current works primarily focus on basic expressions, consequently neglecting the broad spectrum of human emotions encountered in practical scenarios. Compound facial expressions involve the simultaneous manifestation of multiple emotions on an individual's face. This phenomenon reflects the complexity and richness of human states, where facial features dynamically convey a combination of feelings. This study embarks on a pioneering exploration of Compound Facial Expression Recognition (CFER), with a distinctive emphasis on leveraging the Label Distribution Learning (LDL) paradigm. This strategic application of LDL aims to address the ambiguity and complexity inherent in compound expressions, marking a significant departure from the dominant Single Label Learning (SLL) and Multi‐Label Learning (MLL) paradigms. Within this framework, we rigorously investigate the potential of LDL for a critical challenge in Facial Expression Recognition (FER): recognizing compound facial expressions in uncontrolled environments. We utilize the recently introduced RAF‐CE dataset, meticulously designed for compound expression assessment. By conducting a comprehensive comparative analysis pitting LDL against conventional SLL and MLL approaches on RAF‐CE, we aim to definitively establish LDL's superiority in handling this complex task. Furthermore, we assess the generalizability of LDL models trained on RAF‐CE by evaluating their performance on the EmotioNet and RAF‐DB Compound datasets. This demonstrates their effectiveness without domain adaptation. To solidify these findings, we conduct a comprehensive comparative analysis of 12 cutting‐edge LDL algorithms on RAF‐CE, S‐BU3DFE, and S‐JAFFE datasets, providing valuable insights into the most effective LDL techniques for FER in‐the‐wild.

Contribution of laboratory medicine and emerging technologies to cardiovascular risk reduction via exposome analysis: an opinion of the IFCC Division on Emerging Technologies.

This opinion article highlights the critical role of laboratory medicine and emerging technologies in cardiovascular risk reduction through exposome analysis. The exposome encompasses all external and internal exposures an individual faces throughout their life, influencing the onset and progression of cardiovascular diseases(CVD). Integrating exposome data with genetic informationallows for a comprehensive understanding of the multifactorial causes of CVD, facilitating targeted preventive interventions. Laboratory medicine, enhanced by advanced technologies such as metabolomics and artificial intelligence (AI), plays a pivotal role in identifying and mitigating these exposures. Metabolomics provides detailed insights into metabolic changes triggered by environmental factors, while AI efficiently processes complex datasets to uncover patterns and associations. This integration fosters a proactive approach in public health andpersonalized medicine, enabling earlier detection and intervention. The article calls for global implementation of exposome technologies to improve population health, emphasizing the need for robust technological platforms and policy-driven initiatives to seamlessly integrate environmental data with clinical diagnostics. By harnessing these innovative technologies, laboratory medicine can significantly contribute to reducing the global burden of cardiovascular diseases through precise and personalized risk mitigation strategies.

Kierkegaard's Spirituality of Mortality: Beyond God as an Ultimate Rescuer

Abstract: This essay explores the criticism that belief in the God of Christianity is an escape from facing death. Irvin Yalom claims that the faithful use God as an "ultimate rescuer" from death to avoid facing their mortality. This essay argues that Søren Kierkegaard's God is not an ultimate rescuer because the Kierkegaardian individual faces death head-on with earnestness. Kierkegaard's account of mortality actually lends more urgency to finite life because it provides a pressing task to be completed this very day.

Judging emotion in natural images of crowds.

It has been suggested that humans use summary statistics such as the average of the emotion of individual faces when they rapidly judge group emotion. Previous studies have mainly used faces of actors posing basic emotions, and morphed versions of these faces, against a plain background. In the present study, photographs taken in real-world settings were used to investigate the influence of mean facial emotion, maximal facial emotion, and background context on judgments of group emotion, assessed using dimensional ratings of valence, arousal, and dominance. Background context explained a significant amount of unique variance in group ratings for each dimension. Mean emotion explained additional unique variance for valence ratings, whereas maximal emotion explained additional unique variance for arousal, with dominance showing more mixed results. Removing background context and disrupting the contextual and spatial relationship between faces by randomly replacing faces with ones from other images within the stimulus set increased reliance on mean emotion. However, under all conditions, the maximally arousing face continued to exert an influence on ratings of group arousal, in line with theoretical accounts arguing for a unique bottom-up effect of emotional arousal on attentional competition and postattentive perceptual processing. Together these findings suggest that individuals' reliance on average emotion when judging crowd scenes differs as a function of the dimension of affect. In addition, the presence of background context both directly impacts judgments of crowd emotion and modulates the relative influence of maximal versus mean emotion on these judgments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning

Monitoring animal welfare, disease prevention, vaccination administration, production supply, and ownership management all depend on accurate cattle identification. Ear tag-based cattle identification techniques are frequently used in livestock farm management. These are not used to identify specific cattle on large-scale farms. However, ear tags can come off, which makes it challenging to identify a particular individual. Ear tags are susceptible to fraud, can be copied, and run the risk of being damaged. Long-term animal identification is impossible with lost tags. For this purpose, a data set was created by taking images of cattle in their natural environment. The dataset, which contains 15,000 records from 30 different cattle, was divided into three sections: training, validation, and testing. Deep learning algorithms InceptionResNetV2, MobilenetV2, DenseNet201, Xception, and NasNetLarge were used in this study to identify specific cattle faces. The DenseNet201 algorithm achieved the highest test accuracy of 99.53% with a validation accuracy of 99.83%.

Mapping and monitoring dense non-aqueous phase liquid source zone by fused surface and cross-borehole electrical resistivity tomography

Effective characterization of dense non-aqueous phase liquid (DNAPL) source zones is crucial for remediating polluted sites. DNAPL often reside as residuals or pools within high-permeability lenses and above impermeable layers due to soil heterogeneity, gravity, and capillary barriers. Given the high cost of drilling, electrical resistivity tomography (ERT) techniques—including surface ERT and cross-borehole ERT, are commonly used for DNAPL source zone mapping and monitoring. However, the low spatial resolution of ERT increases uncertainty in source zone investigations. This study proposes a method for improving DNAPL mapping and monitoring by fusing surface and cross-borehole ERT data. Sandbox experiments were conducted to simulate a heterogeneous DNAPL source zone, employing both ERT methods for static mapping and dynamic monitoring. Reflective light imaging (RLM) was used to visualize DNAPL migration and provide saturation data, allowing for the quantification of ERT's effectiveness in characterizing DNAPL distribution. The results indicate that individual ERT methods face significant challenges in DNAPL source zone mapping due to background interference. Surface ERT alone tends to underestimate the extent of deeper DNAPL source zones. However, fusing surface and cross-borehole ERT results in a complementary enhancement of vertical spatial resolution, thereby improving the characterization of DNAPL source zones. The fusion of static and time-lapse ERT data substantially enhances DNAPL source zone mapping and monitoring capabilities. By calculating the ratio of the ERT-monitored area to the actual area using resistivity change contours (5 %, 10 %, 15 %), it was found that fusing surface and cross-borehole ERT data improved monitoring resolution by 50.48 % compared to surface ERT alone and by 22.95 % compared to cross-borehole ERT. Principal component analysis (PCA) was effective in fusing time-lapse data, while the weighted average method (WAM) outperformed PCA for static resistivity data fusion.

Viewpoint independent face recognition via extrapolation in paper wasps.

Visual recognition of three-dimensional signals, like faces, is challenging because the signals appear different from different viewpoints. A flexible, but cognitively challenging solution is viewpoint independent recognition, where receivers identify signals from novel viewing angles. Here, we use same/different concept learning to test viewpoint independent face recognition in Polistes fuscatus, a wasp that uses facial patterns to individually identify conspecifics. We find that wasps use extrapolation to identify novel views of conspecific faces. For example, wasps identify a pair of pictures of the same wasp as the 'same', even if the pictures are taken from different views (e.g. one face 0° rotation, one face 60° rotation). This result is notable because it provides the first evidence of view invariant recognition via extrapolation in an invertebrate. The results suggest that viewpoint independent recognition via extrapolation may be a widespread strategy to facilitate individual face recognition.

Racial ambiguity impairs holistic face processing: Evidence from racially distinctive and racially ambiguous faces

ABSTRACT Humans possess a remarkable ability to efficiently process faces, a skill largely influenced by their experiences with individual faces. However, recent research has challenged the role of experience in holistic face processing. Our study examined the role of face-race experience in holistic face processing among Asian and White adults using Asian, White, and racially ambiguous faces. The findings showed that both Asians and Whites exhibited reliable holistic processing for their own-race faces but failed to exhibit this ability with racially ambiguous faces. Importantly, the failure was not solely due to the specific morphing procedure or response bias. These findings imply that face-race experience plays a crucial role in holistic processing. Notably, Asians maintained holistic processing for both own-race and other-race faces, whereas Whites only showed this for own-race faces, indicating differential impacts of face-race experience on holistic processing and highlight the need for further research across diverse cultural contexts. Highlights Asian adults showed equivalent holistic processing with Asian and Caucasian faces Caucasian adults showed holistic processing with Caucasian faces but not with Asian faces Both Asian and Caucasian adults did not show holistic processing with racially ambiguous faces

Care leavers’ journeys into university: A narrative study examined through a Bourdieusian lens

This paper reports the findings of a narrative study exploring the journeys of nine care leavers into university, answering the research question: How do care leavers explain their journeys into university? Care leavers are one of the least represented groups in the university setting. Therefore, examining the journeys of those who do enter the university field is important for understanding how to help raise aspirations and support other care leavers to follow. Recruitment to the study took place across all five universities in the Northeast of England, with nine students from three of these institutions taking part. Individual face to face and telephone interviews were conducted to capture the narratives and these were examined through a Bourdieusian lens, drawing on Bourdieu’s theory of practice, using an experience centred thematic approach. The findings underline the dominant view that university education is culturally desirable and valuable, but also serve to highlight the diversity and complexity of the aspirations and experiences of care leavers’ journeys into university. This research study contributes to the developing evidence base concerned with the educational experiences and trajectories of care leavers, highlighting the need for further research and review of both policy and practice in this area.

Enhancing Place Emotion Analysis with Multi-View Emotion Recognition from Geo-Tagged Photos: A Global Tourist Attraction Perspective

User−generated geo−tagged photos (UGPs) have emerged as a valuable tool for analyzing large−scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the UGPs. This approach falls short of representing the contextual scene characteristics, such as environmental elements and overall scene context, which may contain implicit emotional knowledge. To address this issue, we propose an innovative computational framework for global tourist place emotion analysis leveraging UGPs. Specifically, we first introduce a Multi−view Graph Fusion Network (M−GFN) to effectively recognize multi−view emotions from UGPs, considering crowd emotions and scene implicit sentiment. After that, we designed an attraction−specific emotion index (AEI) to quantitatively measure place emotions based on the identified multi−view emotions at various tourist attractions with place types. Complementing the AEI, we employ the emotion intensity index (EII) and Pearson correlation coefficient (PCC) to deepen the exploration of the association between attraction types and place emotions. The synergy of AEI, EII, and PCC allows comprehensive attraction−specific place emotion extraction, enhancing the overall quality of tourist place emotion analysis. Extensive experiments demonstrate that our framework enhances existing place emotion analysis methods, and the M−GFN outperforms state−of−the−art emotion recognition methods. Our framework can be adapted for various geo−emotion analysis tasks, like recognizing and regulating workplace emotions, underscoring the intrinsic link between emotions and geographic contexts.