Search Results (1,845)

Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. Bi-directional GRUs (BiGRUs) model temporal dependencies in sensor time-series, adept at capturing long-range dynamic patterns. Multi-head self-attention highlights critical time windows and brain regions by assigning adaptive weights to relevant sensor channels, suppressing noise from non-contributory electrodes. Experiments on the DEAP dataset, containing multi-channel EEG sensor recordings, show that MB-MSTFNet achieves 96.80 ± 0.92% valence accuracy, 98.02 ± 0.76% arousal accuracy for binary classification tasks, and 92.85 ± 1.45% accuracy for four-class classification. Ablation studies validate that feature fusion, bidirectional temporal modeling, and multi-scale mechanisms significantly enhance performance by improving feature complementarity. This sensor-driven framework advances affective computing by integrating spatio-temporal dynamics and multi-band interactions of EEG sensor signals, enabling efficient real-time emotion recognition. Full article

Early-career teachers (ECTs) entered the profession during the COVID-19 pandemic, a period that introduced unique stressors to an already-demanding career phase. This systematic review examines empirical studies published between 2020 and February 2025 to explore how the pandemic influenced ECT wellbeing, with particular attention to stressors and protective factors impacting long-term retention and professional sustainability. Guided by PRISMA protocols, databases including Web of Science, ERIC, Google Scholar, and Scopus were searched, screening 470 records and identifying 30 studies that met inclusion criteria: peer-reviewed, empirical, focused on early-career teachers (within the first five years), and situated in or explicitly addressing the pandemic and its ongoing impacts. The results of Braun and Clarke’s thematic analysis (2006) revealed that pandemic-related challenges such as increased workload, professional isolation, disrupted induction processes, and emotional strain have persisted into the post-pandemic era, contributing to sustained risks of burnout and attrition. Regardless, protective factors identified during the pandemic—including high-quality mentoring, structured induction programmes, collegial support, professional autonomy, and effective individual coping strategies—continue to offer essential support, enhancing resilience and professional wellbeing. These findings underscore the necessity of institutionalising targeted supports to address the enduring effects of pandemic-related stressors on ECT wellbeing. By prioritising sustained mental health initiatives and structural supports, education systems can effectively mitigate long-term impacts and improve retention outcomes for early-career teachers in a post-pandemic educational landscape. Full article

Recent advancements in generative artificial intelligence have significantly progressed the field of text-to-music generation, enabling users to create music from natural language descriptions. Despite the success of various models, such as MusicLM, MusicGen, and AudioLDM, the current approaches struggle to capture fine-grained genre-specific characteristics, precisely control musical attributes, and handle underrepresented cultural data. This paper introduces a novel, lightweight fine-tuning method for the AudioLDM framework using low-rank adaptation (LoRA). By updating only selected attention and projection layers, the proposed method enables efficient adaptation to musical genres with limited data and computational cost. The proposed method enhances controllability over key musical parameters such as rhythm, emotion, and timbre. At the same time, it maintains the overall quality of music generation. This paper represents the first application of LoRA in AudioLDM, offering a scalable solution for fine-grained, genre-aware music generation and customization. The experimental results demonstrate that the proposed method improves the semantic alignment and statistical similarity compared with the baseline. The contrastive language–audio pretraining score increased by 0.0498, indicating enhanced text-music consistency. The kernel audio distance score decreased by 0.8349, reflecting improved similarity to real music distributions. The mean opinion score ranged from 3.5 to 3.8, confirming the perceptual quality of the generated music. Full article

Psychological distress is understood to be maintained by attention. We performed two experiments examining the impact of attention training (AT) on psychological distress symptoms. Experiment one (N = 336) investigated what effects might be detected in a simple experimental design with longitudinal measurements, while experiment two (N = 214) examined whether using a different emotional stimulus could induce an immediate anxiolytic effect in response to AT. Attentional biases were operationalized as the target search latency correlated with mood and psychological distress scores. While limited evidence of attentional biases was found in participants with higher mood distress, correlations emerged in the experimental conditions at day thirty, indicating a relationship between task latency, stress, and changes in depression (experimental one). We found no immediate between–within-group differences in outcome when including different emotional stimuli (experiment two). Despite attentional biases being less apparent in community samples, attentional training for bias modification was effective in eliciting positive biases, leading to improved mood. Notably, participants in the control condition reported the greatest mood and psychological distress improvements, whereas changes in the experimental condition primarily pertained to attentional biases. Taken together, these findings suggest that AT tasks can improve distress, but not through changes in attentional biases. Full article

Phubbing, a relatively new phenomenon in the field of digital risks, refers to the act of ignoring someone in favor of focusing on a smartphone during face-to-face interactions. Parental phubbing, a specific form of this behavior, is a prevalent negative parenting practice that can affect parent–child relationships and child development. However, the impact of parental phubbing on the emotional and behavioral development of preschool children remains unclear. This study aims to explore the relationship between parental phubbing and preschoolers’ behavioral problems, as well as test whether parents’ mindful attention awareness (MAA) acts as a mediator between them. Method: A questionnaire was administered to 138 Italian parents (mean age = 38.5, SD = 6.2) of 138 kindergarten preschoolers (mean age = 3.9, SD = 1.03). Questionnaires included the Generic Scale of Phubbing (GSP), the Mindful Attention Awareness Scale (MAAS), and the Strengths and Difficulties Questionnaire (SDQ). Results: Analyses revealed a significant negative correlation between the MAAS and SDQ total scores, a positive correlation between the GSP total score and the SDQ total score, and a negative correlation between the GSP total score and the MAAS total score. The mediation analysis did not show a direct effect of GSP on SDQ, suggesting that parental phubbing did not directly predict children’s behavioral difficulties. Nevertheless, the indirect effect measured by bootstrapping was significant, indicating that parental MAA fully mediated the relationship between parental phubbing and preschoolers’ problematic behaviors. Conclusions: Although further research is needed, parental mindfulness may influence phubbing behaviors in parents providing valuable insights for early interventions aimed at reducing problem behaviors in young children. Full article

Defensive pessimism, an important emotion regulation and motivation strategy, has increasingly attracted scholarly attention in psychology. Recently, sensor-based methods have begun to supplement or replace traditional questionnaire surveys in personality research. However, current approaches for collecting vital signs data face several challenges, including limited monitoring durations, significant data deviations, and susceptibility to external interference. This paper proposes a novel approach using a NiCr/NiSi alloy film temperature sensor, which has a K-type structure and flexible piezoelectric pressure sensor to identify and predict defensive pessimism personality traits. Experimental results indicate that the Seebeck coefficients for K-, T-, and E-type thermocouples are approximately 41 μV/°C, 39 μV/°C, and 57 μV/°C, respectively, which align closely with national standards and exhibit good consistency across multiple experimental groups. Moreover, radial artery frequency experiments demonstrate a strong linear relationship between pulse rate and the intensity of external stimuli, where stronger stimuli correspond to faster pulse rates. Simulation experiments further reveal a high correlation between radial artery pulse frequency and skin temperature, and a regression model based on the physiological sensor data shows a good fit (p < 0.05). These findings verify the feasibility of using temperature and flexible piezoelectric pressure sensors to identify and predict defensive pessimism personality characteristics. Full article

In distance learning environments, learner engagement directly impacts attention, motivation, and academic performance. Signs of fatigue, negative affect, or critical remarks can warn of growing disengagement and potential dropout. However, most existing approaches rely on a single modality, visual or text-based, without providing a general view of learners’ cognitive and affective states. We propose a multimodal system that integrates three complementary analyzes: (1) a CNN-LSTM model augmented with warning signs such as PERCLOS and yawning frequency for fatigue detection, (2) facial emotion recognition by EmoNet and an LSTM to handle temporal dynamics, and (3) sentiment analysis of feedback by a fine-tuned BERT model. It was evaluated on three public benchmarks: DAiSEE for fatigue, AffectNet for emotion, and MOOC Review (Coursera) for sentiment analysis. The results show a precision of 88.5% for fatigue detection, 70% for emotion detection, and 91.5% for sentiment analysis. Aggregating these cues enables an accurate identification of disengagement periods and triggers individualized pedagogical interventions. These results, although based on independently sourced datasets, demonstrate the feasibility of an integrated approach to detecting disengagement and open the door to emotionally intelligent learning systems with potential for future work in real-time content personalization and adaptive learning assistance. Full article

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by inattention, hyperactivity, and impulsivity, frequently observed in juvenile offenders. This systematic review explores the interplay between ADHD and juvenile delinquency, focusing on behavioural, psychological, and social dimensions. Following the PRISMA guidelines, a systematic literature review was conducted using EBSCO Discovery Service, Science Direct, PubMed, and snowballing techniques. Studies meeting specific inclusion criteria, including juvenile offenders diagnosed with ADHD and comparisons to non-offender or non-ADHD control groups, were analysed. The methodological quality of studies was assessed using the Joanna Briggs Institute appraisal tools. A total of 21 studies were included, highlighting significant associations between ADHD and juvenile delinquency. ADHD symptoms, especially impulsivity and emotional dysregulation, were linked to an earlier onset of offending and higher rates of property crimes. Comorbidities such as conduct disorder, substance use disorder, and depression exacerbated these behaviours. Sociodemographic factors like low education levels and adverse family environments were also critical modifiers. Early intervention and tailored treatment approaches were emphasised to address these challenges. The findings underscore the need for early diagnosis, individualised treatment, and integrative rehabilitation programmes within the juvenile justice system to mitigate long-term risks and promote social inclusion. Full article

Among the numerous compounds released as a result of human activities, endocrine-disrupting chemicals (EDCs) have attracted particular attention due to their widespread detection in human biological samples and their accumulation across various ecosystems. While early research primarily focused on their effects on reproductive health, it is now evident that EDCs may impact neurodevelopment, altering the integrity of neural circuits essential for cognitive abilities, emotional regulation, and social behaviors. These compounds may elicit epigenetic modifications, such as DNA methylation and histone acetylation, that result in altered expression patterns, potentially affecting multiple generations and contribute to long-term behavioral phenotypes. The effects of EDCs may occur though both direct and indirect mechanisms, ultimately converging on neurodevelopmental vulnerability. In particular, the gut–brain axis has emerged as a critical interface targeted by EDCs. This bidirectional communication network integrates the nervous, immune, and endocrine systems. By altering the microbiota composition, modulating immune responses, and triggering epigenetic mechanisms, EDCs can act on multiple and interconnected pathways. In this context, elucidating the impact of EDCs on neurodevelopmental processes is crucial for advancing our understanding of their contribution to neurological and behavioral health risks. Full article

Previous studies observed that emotional scenes, presented as distractors, capture attention and interfere with an ongoing task. This behavioral interference has been shown to be elicited by the semantic rather than by the perceptual properties of a scene, as it resisted the application of low-pass spatial frequency filters. Some studies observed that the visual system can adapt to perceptual conditions; however, little is known concerning whether attentional capture by emotional stimuli can also be modulated by the sequential repetition of viewing conditions or of emotional content. In the present study, we asked participants to perform a parity task while viewing irrelevant natural scenes, which could be either emotional or neutral. These scenes could be either blurred (low-pass filter) or perceptually intact, and the order of presentation was balanced to study the effects of sequential repetition of perceptual conditions. The results indicate that affective modulation was most pronounced when the same viewing condition (either intact or blurred) was repeated, with faster responses when perceptual conditions were repeated for neutral distractors, but to a lesser extent for emotional ones. These data suggest that emotional interference in an attentional task can be modulated by serial sensitization in the processing of spatial frequencies. Full article

Multimodal emotion recognition (MER) is essential for understanding human emotions from diverse sources such as speech, text, and video. However, modality heterogeneity and inconsistent expression pose challenges for effective feature fusion. To address this, we propose a novel MER framework combining a Dynamic Weighted Graph Convolutional Network (DW-GCN) for feature disentanglement and a Cross-Attention Consistency-Gated Fusion (CACG-Fusion) module for robust integration. DW-GCN models complex inter-modal relationships, enabling the extraction of both common and private features. The CACG-Fusion module subsequently enhances classification performance through dynamic alignment of cross-modal cues, employing attention-based coordination and consistency-preserving gating mechanisms to optimize feature integration. Experiments on the CMU-MOSI and CMU-MOSEI datasets demonstrate that our method achieves state-of-the-art performance, significantly improving the $AC{C}_7$, $AC{C}_2$, and $F1$ scores. Full article

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

Background/Objectives: Early experiences can significantly influence brain development, particularly when they occur during specific time windows known as sensitive or critical periods. Therefore, the early promotion of neurodevelopmental functions is crucial in children at risk for neurodevelopmental disabilities, such as those with cerebral palsy. This article introduces AMIRA (A Multidimensional and Integrated Rehabilitation Approach), a rehabilitative framework designed for infants at risk of neurodevelopmental disabilities. Methods: AMIRA is intended to guide clinical–rehabilitation reasoning rather than prescribe a rigid sequence of predetermined activities for the child. The theoretical foundation and structure of AMIRA are presented by formalizing its criteria, objectives, tools, and intervention procedures. The framework comprises four distinct sections, each supported by adaptive strategies to facilitate access to materials and to promote play-based interactions among the child, their environment, and communication partners. Particular attention is given to optimizing both micro- and macro-environments for children with, or at risk of, co-occurring visual impairment. Each rehabilitative section includes three progressive phases: an initial observation phase, a facilitation phase to support the child’s engagement, and an active experimentation phase that gradually introduces more challenging tasks. Results: The intervention pathways in AMIRA are organized according to six core developmental domains: behavioral–emotional self-regulation, visual function, postural–motor skills, praxis, interaction and communication, and cognitive function. These are outlined in structured charts that serve as flexible guidelines rather than prescriptive protocols. Each chart presents activities of increasing complexity aligned with typical developmental milestones up to 24 months of age. For each specific ability, the corresponding habilitation goals, contextual recommendations (including environmental setup, objects, and tools), and suggested activities are provided. Conclusions: This study presents a detailed intervention approach, offering both a practical framework and a structured set of activities for use in rehabilitative settings. Further studies will explore the efficacy of the proposed standardized approach. Full article

The impact of soundscapes in religious architecture on public psychology has garnered increasing attention in both research and policy domains. However, the mechanisms by which temple soundscapes influence public emotions remain scientifically unclear. This paper aims to explore how soundscapes in temple architectures designated as World Natural and Cultural Heritage sites affect visitors’ experiences. Considering visitors with diverse social and demographic backgrounds, the research design includes subjective soundscape evaluations and EEG measurements from 193 visitors at two World Heritage temples. The results indicate that visitors’ religious beliefs primarily affect their soundscape perception, while their soundscape preferences show specific correlations with chanting and human voices. Furthermore, compared to males, females exhibit greater sensitivity to emotional variations induced by soundscape experiences. Urban architects can enhance visitors’ positive emotional experiences by integrating soundscape design into the planning of future religious architectures, thereby creating pleasant acoustic environments. Full article

Background/Objectives: Frailty is a multifactorial clinical syndrome characterized by diminished physiological reserves and increased vulnerability to stressors. It is increasingly recognized as a predictor of poor outcomes in cardiac rehabilitation (CR). However, how frailty is defined, assessed, and addressed across outpatient CR programmes remains unclear. This scoping review aimed to map the extent, range, and nature of research examining frailty in the context of outpatient CR, including how frailty is measured, its impact on CR participation and outcomes, and whether sex and gender considerations or participation barriers are reported. Methods: Following the PRISMA-ScR guidelines, we conducted a comprehensive search across six electronic databases (from inception to 15 May 2025). Eligible peer-reviewed studies included adult participants assessed for frailty using validated tools and enrolled in outpatient CR programmes. Two reviewers independently screened citations and extracted data. Results were synthesized descriptively and narratively across three domains: frailty assessment, sex and gender considerations, and barriers to CR participation. The protocol was registered with the Open Science Framework. Results: Thirty-nine studies met inclusion criteria, all conducted in the Americas, Western Pacific, or Europe. Frailty was assessed using 26 distinct tools, most commonly the Kihon Checklist, Fried’s Frailty Criteria, and Frailty Index. The median pre-CR frailty prevalence was 33.5%. Few studies (n = 15; 38.5%) re-assessed frailty post-CR. Sixteen studies reported sex or gender data, but none applied sex- or gender-based analysis (SGBA) frameworks. Only eight studies examined barriers to CR participation, identifying physical limitations, emotional distress, cognitive concerns, healthcare system-related factors, personal and social factors, and transportation as key barriers. Conclusions: The literature on frailty in CR remains fragmented, with heterogeneous assessment methods, limited global representation, and inconsistent attention to sex, gender, and participation barriers. Standardized frailty assessments and individualized CR programme adaptations are urgently needed to improve accessibility, adherence, and outcomes for frail individuals. Full article