Search Results (48)

We report a cross-station replication of endogenous circadian rhythms in plant bioelectric voltage, recorded continuously for 42 days at three independent sensor stations within a public science exhibition (Phänomena, Dietikon, Switzerland; March–April 2026). Three primrose (Primula vulgaris) stations were equipped with custom Biolingo bioelectric sensors (ESP32 + AD8232) and recorded autonomously through approximately 21,000 visitor interactions. We extracted DC-invariant spectral features from 5–10 s voltage windows (n = 78,431 quality-filtered files) and fitted two-stage cosinor models with bootstrap 95% confidence intervals. All three stations show a robust 24 h rhythm in the 1–5 Hz band power (bp_1–5), with peak-to-trough amplitudes between 0.35× and 1.19× of mesor (R²_med 0.72–0.87). Acrophase varies across stations from 05:00 to 11:00 local time. Critically, the rhythm survives an overnight-only restriction (18:00–09:00, no visitors) at all three stations, ruling out visitor presence as the rhythm driver. The most visitor-intensive station (faces of museum visitors triggering an emotion-recognition installation) additionally shows a sharp daytime amplitude collapse coincident with the exhibition opening at 09:00, during the hours of sustained visitor presence. This temporal coincidence is consistent with—though not by itself proof of—the cardiovascular-mechanosensory coupling characterized at single-subject resolution in a companion study. We argue that bp_1–5—the spectral band most directly related to plant action-potential activity—carries an endogenous circadian signal in Primula vulgaris and that this station-level signal co-varies with sustained nearby human presence in a manner consistent with frequency-selective mechanosensory coupling, although the observational design cannot establish this mechanism. From a biomimetic perspective, this suggests that the plant’s evolved bioelectric sensing apparatus might be leveraged as a live ambient biosensor for nearby human activity, complementing the more common biomimetic approach of replicating plant sensing in synthetic devices. Full article

As intelligent cockpits transition into the “third living space”, traditional driver monitoring systems face limitations such as rigid monitoring, computationally intensive algorithms, and insufficient engineering robustness. This paper proposes an edge-computing-based emotion-aware ambient lighting system, forming a complete loop of emotion perception–decision–adaptation. A lightweight emotion recognition network is designed for edge computing: the Mini_XCEPTION architecture is optimized with depthwise separable convolutions to reduce parameters, and a Gaussian-smoothed weighted cross-entropy loss function is used to address class imbalance and ambiguous emotion boundaries. After INT8 quantization, the model achieves 47 FPS real-time inference on a Raspberry Pi (Raspberry Pi Ltd., Cambridge, United Kingdom). A high-concurrency asynchronous software–hardware architecture based on PyQt5 5.15.6 and QThread5.15.6 is built, with a serial communication mechanism featuring fixed-length frames and fault recovery to improve the robustness of the hardware-in-the-loop system. Breaking the rigid alarm mode, an emotion–HSV lighting mapping matrix is established based on the Russell Valence-Arousal model, combined with 0.1 Hz bionic breathing rhythm for non-intrusive feedback. An FSM-controlled HSV lighting policy with 0.1 Hz breathing-light feedback was implemented on an in-cabin HIL platform. In a 12-participant simulated road-rage test, the intervention reduced FER-based anger recovery time by 42.6%; independent physiological validation remains necessary. Full article

Driver emotion recognition constitutes a fundamental pillar of intelligent cockpit systems, playing a pivotal role in enhancing driving safety and optimizing human–machine interaction. Despite the integration of vehicle sensor data in recent multimodal approaches, conventional fusion paradigms frequently encounter performance degradation due to the inherent noise and weak semantic correlation between vehicle telemetry and emotional states. To address these challenges, this study introduces a Dimensional Emotion-Guided Multi-task (DEGM) framework, a novel architecture designed to explicitly formalize the asymmetric roles of visual and vehicular modalities. Rather than employing simplistic feature concatenation, the proposed method maps multivariate vehicle data into a continuous Valence–Arousal–Dominance (VAD) space to characterize latent emotional tendencies within specific driving contexts. These predicted dimensions subsequently serve as semantic priors to conditionally modulate global facial representations through a Feature-wise Linear Modulation (FiLM) mechanism, facilitating robust and interpretable cross-modal interaction. Furthermore, the framework adopts a multi-task learning strategy that jointly optimizes discrete emotion classification and continuous dimension regression, leveraging the latter as a structural regularizer to refine the latent feature space. Comprehensive evaluations on the public PPB driving emotion dataset demonstrate that the proposed DEGM achieves a competitive accuracy of 87.50% and a weighted F1-score of 0.8727. The results validate that our framework provides a lightweight and robust paradigm for context-aware affect sensing, demonstrating strong potential for practical deployment in intelligent transportation systems. Full article

Driver emotion recognition is a crucial method for reducing traffic accidents. Most existing research focuses on experienced drivers as the primary research subjects, overlooking novice drivers, who are inexperienced in driving. However, novice drivers can easily lose control of their emotions due to the high mental load during driving, which can lead to serious traffic accidents. Therefore, to recognize the emotions of novice drivers for timely warnings, we propose an emotion recognition model based on multimodal information. The model consists of a facial feature extraction module, an eye movement feature extraction module and a classifier. The facial feature extraction module uses the ViT-B/16 to extract the facial features of novice drivers. The eye movement feature extraction module is a hybrid network containing Bi-LSTM and Transformer. It extracts eye movement features of novice drivers. Facial features and eye movement features are fused and fed to the classifier. The classifier can output the five major emotion categories of surprise, anger, calm, happy, and other for novice drivers. The experimental results demonstrate that our model accurately recognizes the emotions of novice drivers with an accuracy of 98.72%, surpassing that of other models. Full article

To investigate the regulatory effects of emotional states on the evolution of fatigue in metro drivers, this study conducts an experimental investigation based on an urban rail transit driving simulation platform. A total of 21 participants complete a 90 min simulated driving task, during which electroencephalogram (EEG) and electrocardiogram (ECG) signals are synchronously collected from drivers for fatigue assessment and emotion recognition, respectively. An emotion recognition model based on a multi-scale convolutional neural network (MSCNN) combined with an attention mechanism is constructed. The proposed model uses ECG signals to classify three emotional states—neutral, positive, and negative—where the neutral state is defined as an emotionally undefined baseline that is neither positive nor negative. The model achieves a classification accuracy of 86.96% on the DREAMER dataset. By temporally aligning the emotion recognition results with EEG frequency-domain fatigue indicators, the results show that fatigue exhibits the highest growth and largest fluctuation in amplitude under negative emotions, demonstrating a pronounced fatigue-accelerating effect. Under positive emotions, fatigue decreases considerably and has smaller fluctuations, indicating a certain buffering and restorative effect. In contrast, the neutral emotional state exhibits intermediate and transitional fatigue characteristics. This study innovatively integrates ECG-based emotion recognition with EEG-based fatigue assessment to reveal the mechanisms based on which emotions influence fatigue in metro driving tasks from a physiological perspective. This work provides a basis for emotion-aware fatigue monitoring and safety intervention strategies. Full article

Background: Although burnout among respiratory therapists (RTs) is well documented, qualitative insights into their lived experiences in Saudi Arabia remain limited. This study explored RTs’ experiences of burnout, systemic and organisational drivers of professional strain, and strategies for resilience and retention within Saudi hospitals. Methods: A qualitative descriptive design was employed. Purposive sampling was used to recruit 11 RTs from diverse regions across Saudi Arabia. Semi-structured interviews were conducted in Arabic between September and November 2025, audio-recorded, and transcribed verbatim. Data management and analysis followed a hybrid approach using NVivo 12 software alongside manual coding to support deep immersion in the data. Analysis was guided by Braun and Clarke’s reflexive thematic analysis. Methodological rigour was enhanced through reflexive memoing, peer debriefing, and adherence to a 15-point trustworthiness checklist. Results: Analysis generated one overarching theme, “Caught Between Care and Collapse: The Human Cost of Institutional Burnout,” alongside three interrelated themes. Participants described (1) “Living within a system that drains the self,” highlighting sustained physical and emotional exhaustion driven by understaffing and extended shifts; (2) “Losing meaning and recognition,” illustrating how organisational neglect eroded professional passion and replaced it with obligation and frustration; and (3) “Coping strategies and informal support,” reflecting quiet resilience through self-regulation, peer solidarity, and humane leadership. Many participants framed their endurance as an act of moral defiance rather than passive resignation. Conclusions: These findings suggest that RT burnout reflects not individual failure but a structural outcome of sustained strain and deficits in reciprocity. Burnout emerges as an institutional crisis in which therapists remain deeply committed to patient care while being pushed toward professional collapse by systemic neglect. Culturally informed, system-level interventions are urgently needed to preserve this essential workforce. Full article

Sick leave in physically demanding warehouse logistics poses persistent challenges for employee well-being, operational performance, and sustainable work participation. This study investigates how warehouse employees and supervisors understand drivers of absence and presence, and which workplace resources are perceived as most important for sustaining work happiness and attendance. Using an explanatory sequential mixed-methods design, phase 1 comprised in-depth interviews with warehouse leaders and focus groups with employees (N = 20). Qualitative findings highlight physical strain and sustained pace demands, but also emphasized psychosocial drivers such as emotional exhaustion, limited recognition, insufficient relational support, and a “push-through” culture that normalized strain and hindered recovery. At the same time, collegial support, humor, and everyday recognition were described as critical resources for coping and maintaining presence. Building on these insights, we used a cross-sectional survey (N = 99) to assess work happiness and perceived negative workplace conditions. Exploratory factor analysis identified four work happiness dimensions—supervisor support and recognition; self-development, meaning and autonomy; interpersonal relationships; and collaboration to achieve goals and four dimensions of negative workplace conditions: structural alienation, work-related exhaustion, adverse social climate, and work intensity. Multiple regression analyses showed that interpersonal relationships were the most consistent protective resource, negatively associated with exhaustion, adverse social climate, and work intensity, while supervisor support and recognition primarily reduced structural alienation. Overall, the findings suggest that social relationships constitute a central resource for sustainable well-being and attendance in physically demanding work, offering actionable implications for HRM. Full article

Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis. Full article

In the hospitality industry, corporate social responsibility practices are getting more recognition as a strategic driver of stakeholders’ sustainable behaviors. This study creates and tests a moderated serial mediation model that connects hotel CSR activities to guests’ pro-environmental behavior (PROE). In addition, moral elevation (ME) and pro-social behaviors of guests (PSO) are posited as affective and behavioral mediating mechanisms, whereas the perceived transparency (TRA) of hotel actions is investigated as a moderator. The survey data were collected from 426 hotel guests who had stayed in hotels in the Turkish Republic of Northern Cyprus (TRNC) and used partial least squares structural equation modeling (PLS-SEM) to analyze it. The findings reveal that CSR does have a positive effect on ME, which sequentially makes ME affect PSO and PROE behavior positively. The research shows that the moderator TRA also amplifies the relationship strength between CSR and ME, which suggests that transparent actions of hotels do have a positive emotional impact on guests. The research contributes to hospitality literature and also sustainability literature by identifying ME as an emotional mechanism and TRA as a moderating condition that alter guests’ behaviors. As managerial implications, the research underlines the value of creating CSR practices that are both transparent and authentic to guests and stakeholders to ultimately maximize the engagement of guests in the context of sustainability. Full article

The transition to sustainable food systems requires communication strategies that resonate with consumers’ values, not only technological innovation. This study examines how values-centric communication can shape German consumers’ responses to alternative proteins, focusing on insect-based snacks. A desk-based synthesis of recent studies, guided by Schwartz’s value theory, identified Tradition and Security as dominant drivers of food choice and yielded five communication requirements: Cultural familiarity, Emotional safety, Simplicity and clarity, Trust and credibility, and Routine integration. These were operationalised into communication guidelines and short on-pack claims, which were applied to a refined packaging prototype. An exploratory focus group (N = 7) then compared reactions to the original versus the refined packaging, analysed using McGuire’s communication–persuasion stages. Within this small exploratory group, participants reported that familiar formats, a reassuring tone, clear visual hierarchy, and salient trust cues made them more willing to consider trying the product, whereas information overload, claim–image incongruence, value-incongruent brand naming, and delayed recognition of insect content appeared to impede acceptance. The study contributes an integrative analytic lens combining Schwartz’s value theory with McGuire’s model and a set of testable guidelines for value-aligned food communication. Because the empirical evidence is based on a single small student focus group with fixed presentation order, bundled manipulations, and hypothetical intentions, these results are exploratory and self-reported and should be interpreted cautiously; future research should employ counterbalanced factorial designs with behavioural outcomes. Full article

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices. Full article

(1) Background: Austria’s use of fully mechanized harvesting systems has been continuously increasing. Technical developments, such as traction aid winches, have made it possible to drive on increasingly steep terrain. However, this has led to challenges and potential hazards for the operators, resulting in higher stand damage rates and risks of workplace accidents. Since these systems and working environments involve a highly complex interplay of various parameters, the purpose of this protocol is to propose a new set of methodologies that can be used to obtain a holistic interpretation of the psychophysiological interrelationship between the working conditions and stress of harvester and forwarder drivers. (2) Methods: We developed a research protocol to analyse the (a) environmental and (b) machine-related parameters; (c) psychological and psychophysiological responses of the operators; and (d) technical outcome parameters. Within this longitudinal exploratory field study, experienced drivers were monitored for over an hour at the beginning and the end of their workday while operating in varying steep terrains with and without a traction aid winch. The analysis is based on macroscopic (collected using cameras), microscopic (eye-tracking glasses and AI-driven emotion recognition), quantitative (standardized questionnaires), and qualitative (interviews) data. This multimodal research protocol aims to improve the health and safety of forest workers, increase their productivity, and reduce damage to remaining trees. Full article

Road safety has emerged as a global issue, driven by the rapid rise in vehicle ownership and traffic congestion. Human error, like distraction, drowsiness, and panic, is the leading cause of road accidents. Conventional driver monitoring systems (DMSs) frequently fail to detect these emotional and cognitive states, limiting their potential to prevent accidents. To overcome these challenges, this work proposes a robust deep learning-based DMS framework capable of real-time detection and response to emotion-driven driver behaviors that pose safety risks. The proposed system employs convolutional neural networks (CNNs), specifically the Inception module and a Caffe-based ResNet-10 with a Single Shot Detector (SSD), to achieve efficient, accurate facial detection and classification. The DMS is trained on a comprehensive and diverse dataset from various public and private sources, ensuring robustness across a wide range of emotions and real-world driving scenarios. This approach enables the model to achieve an overall accuracy of 98.6%, an F1 score of 0.979, a precision of 0.980, and a recall of 0.979 across the four emotional states. Compared with existing techniques, the proposed model strikes an effective balance between computational efficiency and complexity, enabling the precise recognition of driving-relevant emotions, making it a practical and high-performing solution for real-world in-car driver monitoring systems. Full article

Driver emotion recognition is vital for intelligent driver assistance systems, where the accurate detection of emotional states enhances both safety and user experience. Current approaches, however, require extensive labeled datasets, perform poorly under real-world conditions, and degrade with class imbalance. To overcome these challenges, we propose the Active Learning and Deep Attention Mechanism (ALDAM) framework. ALDAM introduces three key innovations: (1) an active learning cycle that reduces labeling effort by ~40%; (2) a weighted-cluster loss that mitigates class imbalance; and (3) a deep attention mechanism that strengthens feature selection under occlusion, pose variation, and illumination changes. Evaluated on four benchmark datasets (FER-2013, AffectNet, CK+, and EMOTIC), ALDAM achieves an average accuracy of 97.58%, F1-score of 98.64%, and AUC of 98.76% surpassing CNN-based models and advanced baselines such as SE-ResNet-50. These results establish ALDAM as a robust and efficient solution for real-time driver emotion recognition. Full article

Background/Objectives: Chronic non-malignant pain (CNMP) affects 46.4% of adults in Saudi Arabia and often requires interdisciplinary care, including mental health services. Despite this need, mental health integration remains limited. This study explored healthcare providers’ perceptions of integrating mental health services into CNMP management and identified barriers and facilitators to interdisciplinary collaboration. Methods: A cross-sectional survey was conducted among 114 healthcare providers across Saudi Arabia. Using the Theoretical Domains Framework (TDF), domains such as knowledge, skills, beliefs about capabilities and consequences, reinforcement, and social influences were assessed. Data were analyzed using descriptive statistics, correlation analyses, and multiple regression. Results: Positive perceptions of mental health integration were significantly associated with beliefs about capabilities (r = 0.31, p = 0.001) and beliefs about consequences (r = 0.40, p < 0.001), as well as skills (r = 0.30, p = 0.001) and reinforcement (r = 0.26, p = 0.005). Multiple regression confirmed beliefs about capabilities (B = 0.208, p = 0.001) and consequences (B = 0.237, p < 0.001) as independent predictors, explaining 31.9% of the variance in perceptions (R² = 0.319, adjusted R² = 0.285). Emotional responses, such as stress, were potential barriers but did not independently predict perceptions. Systemic challenges included limited referral pathways and insufficient mental health resources. Conclusion: Confidence in professional abilities and recognition of the benefits of collaboration are key drivers of positive perceptions toward mental health integration in CNMP care. Interventions that enhance provider confidence, emphasize interdisciplinary benefits, and strengthen organizational support may improve engagement with mental healthcare services in Saudi Arabia. Full article