Search Results (1,904)

Background: Emergency obstetric situations require rapid clinical decision-making, technical competence, and emotional preparedness to ensure safe and compassionate care for both mother and newborn. However, nursing students often have limited opportunities to experience such high-risk, low-frequency events during clinical placements. Simulation-based education has emerged as an effective strategy to prepare future nurses for caring in emergency contexts, allowing them to develop both technical and non-technical skills in a safe learning environment. This study aimed to evaluate the effects of a high-fidelity obstetric emergency simulation program on nursing students’ knowledge, perceived safety, and learning experience. Methods: A mixed-methods design was employed, combining a quasi-experimental pretest–posttest assessment without a control group and qualitative analysis of open-ended reflections. Eighty-two third-year nursing students participated in two simulation sessions addressing obstetric emergencies such as breech birth, shoulder dystocia, out-of-hospital delivery, eclampsia, postpartum hemorrhage, and maternal cardiac arrest. Data were collected using validated instruments measuring knowledge, perceived safety, and satisfaction and self-confidence in learning, and were analyzed using Wilcoxon signed-rank tests and thematic analysis. Results: Significant improvements were observed in specific knowledge areas related to complex obstetric maneuvers and in their perceived safety when managing emergency situations (p < 0.001, r > 0.40). Participants reported high levels of satisfaction and confidence in learning. Qualitative findings highlighted increased emotional preparedness, improved clinical reasoning, and recognition of the importance of teamwork and reflective debriefing in emergency care contexts. Conclusions: High-fidelity simulation appears to be an effective educational strategy for preparing nursing students to provide safe and confident care in obstetric emergencies. Integrating simulation into nursing curricula can strengthen both technical competence and the emotional readiness required for caring in urgent and high-pressure clinical situations. Full article

Parenting research and policy increasingly emphasize visible practices, measurable outcomes, and parental effort as indicators of competence. Across welfare, education, and family intervention contexts, “good parenting” is often evaluated through intensive doing: monitoring, documenting, optimizing development, and managing risk. While these frameworks foreground parental responsibility, they frequently obscure the relational dimensions of care and intensify existing classed, gendered, and racialized inequalities. Building on feminist scholarship that has long conceptualized parenting as relational, ethical, and socially situated, this paper develops a theoretical framework for rethinking parenting by integrating family studies scholarship on intensive parenting, emotional labor, and inequality with Hannah Arendt’s distinctions among labor, work, and action. Parenting is commonly framed as labor, the daily work of sustaining children’s lives, or as work, the longer-term project of producing competent future adults. Drawing on Arendt’s concept of action, the paper reinterprets parenting as a relational practice grounded in presence, responsiveness, and mutual recognition. Using illustrative examples from diverse family contexts, including Indigenous and immigrant communities, the analysis shows how privatized and performance-oriented models of care place strain on families while rendering collective forms of support less visible. The paper concludes by outlining implications for family research and policy, including a shift from outcome-based evaluation toward relational engagement and from individualized responsibility toward strengthened social infrastructures of care, arguing for greater attention to relational care, shared responsibility, and the structural conditions that shape parenting practices and family well-being. Full article

The increasing prevalence of work-related stress requires advanced, non-intrusive physiological monitoring solutions. As conventional methods are often impractical for continuous, real-world applications, this study investigates the deployment of artificial intelligence models on embedded systems for real-time emotion recognition from physiological signals. The study identified critical constraints for embedded implementation, including model size and memory capacity. An evaluation of various machine learning algorithms revealed that, while models like K-Nearest Neighbors (KNN) achieve high accuracy (88.8%), their excessive memory footprints make them unsuitable for resource-constrained hardware. Consequently, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and recurrent neural network (RNN) architectures were deployed on an STM32F411 microcontroller, for which model compression proved essential. An experimental study validated the approach, achieving high recognition rates for pronounced emotions such as hatred (91%) and anger (85%), though with a lower accuracy for more subtle states. These results confirm the potential of embedded AI systems for physiological monitoring, highlighting the critical importance of feature selection and model compression for practical implementation. Full article

Architectural heritage plays a central role in shaping visitors’ emotional experiences within cultural tourism contexts. However, empirical research examining how specific architectural styles evoke emotional responses remains limited, particularly when using objective measurement techniques. This study investigates emotional reactions to architectural heritage by applying the Stimulus–Organism–Response (SOR) theoretical framework. In this model, architectural styles act as environmental stimuli, emotional processing represents the organismic state, and the resulting emotional activation constitutes the response. An experimental protocol was conducted with a sample of 645 participants exposed to a series of standardized architectural heritage images representing different architectural styles and infrastructure types. Emotional reactions were captured in real time through facial emotion recognition technology, enabling the objective measurement of eight basic emotions: neutral, happiness, sadness, surprise, fear, disgust, anger, and contempt. The collected emotional data were statistically analyzed using Analysis of Variance (ANOVA) to identify significant differences in emotional responses across architectural styles, heritage typologies, and gender. When significant differences were detected, Tukey’s HSD post hoc tests were applied to determine specific group contrasts. The findings reveal that different architectural styles generate distinct emotional patterns, highlighting the role of architectural aesthetics as a powerful mediator of affective engagement with heritage environments. From a theoretical perspective, this research contributes to heritage tourism and environmental psychology by integrating the SOR framework with real-time emotion detection technologies, providing a novel methodological approach for analyzing emotional responses to architectural heritage. Full article

Background: Well-being in the elderly is shaped by complex emotional and social factors. Early identification of individuals at risk for reduced well-being may support timely preventive or supportive interventions. This study examined whether emotional intelligence indicators collected at baseline can predict well-being status 5 months later using explainable machine learning models. Methods: A cohort of elderly participants aged 60 to 89 years completed emotional intelligence measures at baseline, and well-being was assessed 5 months later using the POMS questionnaire. Four machine learning algorithms, Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were developed using 5-fold stratified cross-validation. Model performance was evaluated through accuracy, precision, recall, F1-score, ROC AUC, and normalized confusion matrices. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution and directionality of each predictor. Results: XGBoost achieved the highest predictive performance (accuracy = 0.789; F1 = 0.778) and demonstrated balanced classification across well-being categories. SVM also performed robustly (accuracy = 0.760), while LR showed reduced sensitivity for detecting those with poorer well-being. SHAP analysis identified self-control, emotionality, sociability, self-motivation, and well-being components as the most influential predictors. Lower emotionality, higher sociability, and higher self-control scores were linked to a greater probability of favorable well-being outcomes. Conclusions: The findings demonstrate the feasibility of using explainable machine learning models to predict 5-month well-being status within this sample of older adults using emotional intelligence indicators. XGBoost provided the strongest and most balanced performance, while SHAP analysis clarified how specific emotional intelligence dimensions influenced predictions. These findings suggest that interpretable machine learning approaches may support future efforts toward early recognition of older adults who may be at risk for reduced well-being and guide personalized intervention strategies. Full article

Real-time facial emotion recognition remains challenging due to the high dimensionality and computational cost of dense facial representations, which limit their applicability in resource-constrained and real-time scenarios. This study proposes a compact, anatomically informed angular facial representation for efficient, interpretable emotion recognition under real-time constraints. Facial landmarks are first extracted using a standard landmark detection framework, from which a reduced facial mesh of 27 anatomically selected points is defined. Internal geometric angles computed from this mesh are analyzed using temporal variability and redundancy criteria, resulting in a minimal set of eight angular descriptors that capture the most expressive facial dynamics while preserving geometric invariance and computational efficiency. The proposed representation is evaluated using multiple supervised machine learning classifiers under two complementary validation strategies: stratified frame-level cross-validation and strict Leave-One-Subject-Out evaluation. Under mixed-subject stratified validation, the best-performing model (MLP) achieved macro-averaged F1-scores exceeding 0.95 and near-unity ROC–AUC values. However, subject-independent evaluation revealed reduced generalization performance (average accuracy ≈55%), highlighting the influence of inter-subject morphological variability embedded in absolute angular descriptors. These findings indicate that a minimal angular geometric encoding provides strong intra-subject discriminative capability while transparently characterizing its cross-subject generalization limits, offering a practical and interpretable alternative for data- and resource-constrained real-time scenarios. Full article

This study investigated neural oscillatory dynamics underlying visual word recognition in university students with dyslexia using a portable brain–computer interface (BCI) EEG system. The sample included university students with dyslexia (N = 12) and matched controls (N = 14) who completed auditory discrimination and visual word recognition tasks, with and without musical accompaniment. Through these experimental conditions, the researchers assessed (a) the cortical activation across frequency bands, (b) the modulatory effect of background music, and (c) the relationship between emotional states and brain activity. Results revealed significant group differences in oscillatory patterns, with reduced β- and γ-band activity in the left occipito-temporal cortex among participants with dyslexia, confirming disrupted temporal coordination in posterior reading networks. Compensatory right-hemisphere activation was observed, particularly under musical conditions, accompanied by increased α-band power and reduced δ activity, indicating enhanced attentional engagement and reduced cognitive fatigue. Emotional assessment using the DASS-21 revealed higher stress and anxiety scores in the dyslexic group, suggesting that affective factors may modulate oscillatory dynamics. The presence of background music appeared to attenuate these effects, supporting improved emotional regulation and cognitive focus. These findings demonstrate that dyslexia reflects a distributed disruption in neural synchrony and cross-frequency coupling, influenced by both cognitive and affective mechanisms. The integration of portable EEG technology with rhythmic auditory stimulation offers new insights into the neurophysiological and emotional aspects of dyslexia, highlighting the potential of rhythm- and music-based approaches for both diagnostic and therapeutic applications. Full article

Canine cognitive dysfunction (CCD) is a progressive neurodegenerative condition affecting aging dogs, characterized by impairments in learning, memory, spatial orientation, and behavior. Despite its substantial negative impact on dogs’ quality of life and owners’ emotional well-being, CCD is frequently underrecognized or diagnosed at a late stage. This study explored how challenges in CCD recognition and veterinary communication influence dog owners’ ability to identify symptoms and make informed decisions about care. Semi-structured interviews were conducted with 22 dog owners whose dogs were suspected of having CCD, based on elevated scores on the Canine Cognitive Dysfunction Rating Scale (CCDR) and owner-reported behavioral changes. Interview data were analyzed using reflexive thematic analysis. Four main themes emerged: (1) difficulties in recognizing CCD-related symptoms, (2) communication challenges between owners and veterinarians, (3) owners’ adaptation to gradually emerging symptoms, and (4) the emotional and practical burden of caregiving. Owners frequently interpreted behavioral changes as normal aging or other health problems, which delayed the recognition of cognitive decline. Participants also described limited guidance from veterinary professionals regarding CCD, contributing to uncertainty, emotional distress, and challenges in end-of-life decision-making. Together, these findings suggest that owners’ experiences follow a progressive caregiving trajectory, from initial symptom uncertainty to increasing emotional and practical burden. Improving awareness of CCD, strengthening veterinary communication, and providing targeted support for caregivers may facilitate earlier recognition and more effective management of cognitive decline, ultimately benefiting both dogs and the people who care for them. Full article

Human-centered entrepreneurship considers employee well-being and uses the Sustainable Development Goals as its fundamental pillars. However, existing research predominantly focuses on institutional interventions and fails to provide integrated intelligent solutions for tackling human–machine collaboration issues in the context of digital transformation. Large language models (LLMs) offer potential for affective computing and personalized support, but face critical gaps in ethical governance, privacy protection, and real-time risk intervention in sensitive entrepreneurial contexts. Our proposed Human-Centered Entrepreneurial Intelligent Agent (HCEA) framework achieves the unified optimization of task utility, empathetic expression, and ethical security by integrating a large language model core fine-tuned via a multi-objective hybrid loss function and a cluster of task-specialized intelligent agents. HCEA integrates retrieval-enhanced generation to ensure suggestion accuracy, a hierarchical data governance system for sensitivity-based privacy protection, and an independent risk detection module for real-time intervention and referral. We build the framework by constructing a hybrid entrepreneurial dataset, design the multi-agent architecture of decision support, emotion understanding and ethical risk tracking, and empirically evaluate both comparisons and ablation experiments. The results demonstrate that HCEA outperforms five baseline models across six key metrics, including entrepreneurship guidance relevance, emotion recognition, and high-risk recall. This study contributes to the intersection of digital transformation and sustainable entrepreneurship by providing a technically feasible, ethically grounded intelligent framework that empowers enterprises to reconcile efficiency with human-centric values, advancing SDG 8 (decent work and economic growth) and SDG 9 (industry, innovation, and infrastructure). Full article

There are still challenges in speech emotion recognition, as the representation capability of single-modal information is limited, there are difficulties in capturing continuous emotional transitions in discrete emotion annotations, and the issues of modal structural differences and cross-sample alignment in multimodal fusion methods persist. To address these, this study undertakes work from both data and model perspectives. For data, a Chinese multimodal database STEM-E²VA was constructed, synchronously collecting four modalities of data: articulatory kinematics, acoustics, glottal signals, and videos. This covers seven discrete emotion categories and employs PAD continuous annotation. By integrating discrete and continuous dimensional annotations, it better represents the distinction between strong and weak emotions under the same discrete emotion label. Concurrently, to process the biases in PAD annotations, we employed the SCL-90 psychological questionnaire to analyze annotators’ cognitive and emotional perceptions, thereby ensuring data reliability. For model, this paper proposes a multimodal supervised contrastive fusion network incorporating PAD perception. It employs a PAD-enhanced hybrid contrastive loss function to optimize intra-model and inter-modal feature alignment. Utilizing a cross-attention mechanism combined with a GRU–Transformer network for temporal feature extraction, it achieves deep fusion of multimodal information, reducing inter-modal discrepancies and cross-class confusion. Experiments demonstrate that the proposed method achieves 85.47% accuracy in discrete sentiment recognition on STEM-E²VA, with a substantial reduction in RMSE for PAD dimension prediction. It also exhibits excellent generalization capability on IEMOCAP, providing a novel framework for integrating discrete and continuous sentiment representations. Full article

Existing research confirms associations between Big Five personality traits and emotional states, yet investigations into how personality traits modulate emotional dynamics and their gender-specific patterns remain limited. The present study developed a TFace-Bi-GRU-SE deep learning model that achieved a weighted accuracy of 63.50 ± 0.98% (peak single-run: 64.96%) and an F1 score of 65.21% in performance testing, with a single-inference time of 14.1 s, outperforming traditional methods. The model processed 10 min video recordings from 30 participants (19,262 observations), generating time-series data for valence (P) and arousal (A). Combined with Big Five personality assessments, continuous-time structural equation modeling (CTSEM) revealed distinct emotional dynamics: both P and A exhibited significant negative autoregression (−0.056 and −0.558, p < 0.001), with A reverting to baseline substantially faster (half-life: 1.2 s) than P (half-life: 12.3 s); cross-lagged effects were nonsignificant (P_A: 0.007; A_P: −0.026, p > 0.05). Arousal demonstrated greater instantaneous volatility (=0.339) than valence (=0.286, p < 0.001), with positive covariation between dimensions (0.218, p = 0.006). Exploratory analyses (N = 30) indicated that higher neuroticism and openness scores were associated with elevated arousal (Cohen’s d > 0.8), whereas higher agreeableness and conscientiousness scores were associated with elevated valence (d > 0.8). Gender moderated the neuroticism–arousal relationship, with more potent effects in females (r = 0.746, p = 0.008). Robustness analyses demonstrated high stability of core DRIFT parameters (P_P, A_A): bootstrap resampling (n = 50) yielded coefficients of variation < 0.35 with 100% directional consistency; subgroup validation confirmed cross-sample invariance. Sensitivity analyses revealed that an additional 8% measurement error induced less than 9% bias (8.3% for both P_P and A_A) in autoregressive parameters while preserving half-life ratios, confirming CTSEM’s capacity to extract reliable dynamics from moderately accurate AI outputs. Bootstrap and Bayesian analyses identified ten personality–DRIFT associations with directional consistency ≥ 70%; these constitute preliminary hypotheses for adequately powered future studies (N ≥ 61). This study provides methodological foundations for personalized affective intervention research. Data and code are publicly available (see Data Availability Statement). Full article

The effectiveness of social engineering schemes, such as phishing, depends significantly on the victim’s emotional state, which is intentionally moved by the attacker toward fear, sadness, and disgust through time pressure, threats, or messages about potential losses, which weaken cognitive control. EEG datasets that simultaneously contain basic emotions and realistic phishing scenarios are lacking. Therefore, in some cases, stress-based biophysiological datasets obtained using the Trier Social Stress Test (TSST) are used for neurophishing modeling. The TSST exhibits phasic dynamics: a transition from a neutral state to a peak in fear, followed by an increase in sadness and a partial recovery to a neutral state, highlighting fear and sadness as key components of social stress. The interval of maximum fear probability is interpreted as the window of greatest vulnerability to phishing, when it is critical to consciously pause, verify information across independent channels, and avoid impulsive actions. The suggested hybrid neural network model, WS-KAN-EEGNet, is trained on five emotions and applied to these recordings, generating temporal trajectories of state probabilities with high accuracy, forming a reliable basis for future industrial solutions to ensure a secure digital space. Full article

This study explores the intricate behavioral consumer psychology dynamics of how certain elements—color, price, gender differences, and the concept of the frequency illusion—affect emotions, brand awareness, and consumer decision-making in a digital environment. Going beyond conventional analyses, this study also explores the intersection of sustainable business practices, elucidating the potential for ethical, environmentally conscious, and business-sustainable decision-making. Utilizing a quantitative method and survey data from 207 respondents, this research contributes to a more profound level of understanding of consumer decision-making in the Lebanese retail sector, offering strategic insights for organizations seeking to enhance brand recognition, while aligning with responsible and sustainable practices in today’s dynamic and competitive environment. The study found that psychological cues—color, price, gender differences, and frequency illusion—significantly influence emotions, brand awareness, and consumer decision-making in retail. Future research should examine the tensions in consumer decision-making, where brand awareness and emotional cues can simultaneously facilitate and bias choices, with effects contingent on exposure, demographic characteristics, digital fluency, and cultural context. Full article

Emotion recognition plays a crucial role in human–computer interaction and mental health applications. Traditional Electroencephalogram (EEG)-based emotion recognition methods are limited in classification accuracy due to their neglect of the spatiotemporal characteristics of the signals and individual differences. This study proposes a novel EEG emotion recognition framework that integrates spatiotemporal features to enhance performance through the following innovations: (1) the use of a Recurrence Plot (RP) to transform one-dimensional EEG signals into two-dimensional images, enhancing the representation of nonlinear dynamic features; (2) the design of a Spatiotemporal Channel Attention Module (TCSA), which combines temporal convolution, channel, and spatial attention mechanisms to optimize the capture of complex patterns; and (3) the integration of the lightweight and efficient network Efficientnet to construct the TCSA-Efficientnet classification model. On the Database for Emotion Analysis using Physiological Signals (DEAP) dataset, the proposed method achieves accuracy rates of 99.11% and 99.33% for valence and arousal classification tasks, respectively. On the Database for Emotion Recognition Using EEG and Physiological Signals (DREAMER) dataset, the method achieves accuracy rates of 98.08% and 97.49%, outperforming other EEG-based emotion classification models on both datasets. This demonstrates its advantages in accuracy, robustness, and generalization. Full article

Background: Adolescence is a sensitive developmental period marked by heightened emotional reactivity and increasing demands on emotion recognition and regulation. Although alexithymia has been associated with less adaptive and avoidant coping tendencies in adolescents, most prior research has relied on descriptive or bivariate approaches, leaving the underlying processes and model-based pathways insufficiently clarified. In particular, the explanatory role of difficulties in emotion regulation in the association between alexithymia and coping strategies remains underexplored. This study aimed to address this gap by examining whether difficulties in emotion regulation mediate the relationship between alexithymia and coping strategies in adolescents. Methods: In this cross-sectional study, 1415 adolescents (13–17 years) from public high schools in Central Anatolia, Türkiye, completed the Toronto Alexithymia Scale (TAS-20), the Difficulties in Emotion Regulation Scale (DERS-16), and the Coping Strategies Indicator (CSI). Pearson correlations were calculated. Mediation analyses were conducted using PROCESS Macro (Model 4) with 5000 bootstrap samples, adjusting for age, gender, academic achievement, and family type. Results: Alexithymia was moderately associated with emotion regulation difficulties (r = 0.49, p < 0.001). Mediation analyses revealed significant indirect effects for seeking social support (B = −0.068, 95% CI [−0.087, −0.051]) and problem solving (B = −0.067, 95% CI [−0.086, −0.049]), with direct effects remaining significant, indicating inconsistent (competitive) mediation patterns. For avoidance coping, the indirect effect was significant (B = −0.072, 95% CI [−0.090, −0.055]), whereas the direct effect became non-significant, consistent with an indirect-only mediation pattern. Correlations involving coping outcomes were small in magnitude. According to Cohen’s criteria, the association between alexithymia and emotion regulation difficulties was moderate in magnitude, whereas correlations involving coping outcomes were small. Conclusions: Difficulties in emotion regulation emerged as a statistical mediator within the proposed model, demonstrating systematic associations between alexithymia and distinct coping patterns in adolescents. These findings underscore the relevance of emotion regulation–focused prevention and intervention efforts in school settings. By examining multiple coping outcomes simultaneously within a covariate-adjusted mediation framework in a large community adolescent sample, this study offers an integrative, model-based perspective on how alexithymic traits are linked to coping through regulatory difficulties. Full article