Search Results (2,277)

An important objective of teacher education is to encourage students to reflect on teaching practices. Analyzing video scenarios from classroom settings is a commonly used method for achieving this. This study examines the impact of different video types on the reflective and emotional processes of teacher education students and explores the relationships between these processes. In a randomized experimental study, 129 students analyzed a video of either a dysfunctional or a functional video scenario as part of a video-based intervention. Data were collected through written reflections, self-assessments of reflection, and ratings of emotional valence and arousal. The results revealed that students who analyzed the dysfunctional scenario demonstrated greater levels of reflection and experienced more negative emotions than those who analyzed the functional scenario. No significant differences were found in terms of self-assessed reflection and positive emotions. However, a significant relationship was found between positive emotions and self-assessed reflection. This study contributes to the literature by differentiating between distinct facets of reflection and emotion, thus enabling a more nuanced understanding of how specific video characteristics influence reflective engagement. Full article

Background/Objectives: Electroencephalography (EEG)-based emotion recognition plays an important role in affective computing and brain–computer interface applications. However, existing methods often face the challenge of achieving high classification accuracy while maintaining physiological interpretability. Methods: In this study, we propose a convolutional neural network (CNN) model with a simple architecture for EEG-based emotion classification. The model achieves classification accuracies of 95.21% for low/high arousal, 94.59% for low/high valence, and 93.01% for quaternary classification tasks on the DEAP dataset. To further improve model interpretability and support practical applications, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to identify the EEG electrode regions that contribute most to the classification results. Results: The visualization reveals that electrodes located in the right prefrontal cortex and left parietal lobe are the most influential, which is consistent with findings from emotional lateralization theory. Conclusions: This provides a physiological basis for optimizing electrode placement in wearable EEG-based emotion recognition systems. The proposed method combines high classification performance with interpretability and provides guidance for the design of efficient and portable affective computing systems. Full article

Turning-point memories, experiences that impact personal development, may be interpreted in ways that emphasize positive, negative, or mixed development because the memory prompt is open-ended with regard to event valence (i.e., it does not elicit ‘high’-point or ‘low’-point life events). Broadly, narratives that articulate how one has grown or changed for the better over time are positively associated with beneficial psychological characteristics and well-being, and are thought to be a cultural master narrative template in the United States (US). Recent work suggests cultural differences in the narration of adversity. Our mixed-methods study expands the literature on cultural comparisons of turning-point autobiographical memories by comparing themes in turning-point memory narratives of US and UK college-going emerging adults and by assessing whether or not narrative differences relate to changes in well-being and emotions after narration. Results suggest that turning points are characterized by memories of adversity and that redemptive narration is similar across samples in its frequency and associations with well-being and emotions. Discussion explores when and why redemptive narration may be beneficial for people from broad backgrounds. Full article

Amid concurrent pressures on water and material resources, recovering valuable ions like lithium and nutrients from brines and wastewater is a critical tenet of the circular economy. This review provides a critical assessment of electro-driven membranes (EDMs) as a key technology platform for achieving this goal with high energy efficiency. A comprehensive synthesis and analysis of the current state-of-the-art of core EDM technologies, including electrodialysis (ED) and membrane capacitive deionization (MCDI), is presented, focusing the analysis on the performance metrics of specific energy consumption and ion selectivity. The findings reveal that the optimal EDM technology is highly application-dependent, with MCDI excelling for dilute streams and ED for concentrated ones. While significant advances in monovalent selective membranes have enabled lithium recovery, achieving high selectivity between ions of the same valence (e.g., Li⁺/Na⁺) remains a fundamental challenge. Moreover, persistent issues of membrane fouling and scaling continue to inflate energy consumption and represent a major bottleneck for industrial-scale deployment. While EDMs are a vital technology for ion resource recovery, unlocking their full potential requires a dual-pronged approach: advancing materials science to design novel, highly selective membranes, while simultaneously developing intelligently integrated systems to surmount existing performance and economic barriers. Full article

Altered facial expressivity is frequently recognized in cognitively impaired individuals. This makes facial emotion identification a promising tool with which to support the diagnostic process. We propose a novel, non-invasive approach for detecting cognitive impairment based on facial emotion analysis. We design a protocol for emotion elicitation using visual and auditory standardized stimuli. We collect facial emotion video recordings from 32 cognitively impaired and 28 healthy control subjects. To track the evolution of emotions during the experiment, we train a deep convolutional neural network on the AffectNet dataset for emotion recognition from facial images. Emotions are described using a dimensional affect model, namely the continuous dimensions of valence and arousal, rather than discrete categories, enabling a more nuanced analysis. The collected facial emotion data are used to train a classifier to distinguish cognitively impaired and healthy subjects. Our k-nearest neighbors model achieves a cross-validation accuracy of 76.7%, demonstrating the feasibility of automatic cognitive impairment detection from facial expressions. These results highlight the potential of facial expressions as early markers of cognitive impairment, which could enhance non-invasive screening methods for early diagnosis. Full article

Green food serves as a bridge connecting healthy lifestyles with environmental values, particularly in the context of sustainable consumption transitions. However, existing research lacks a systematic understanding of how consumers negotiate cognitive evaluations and emotional responses when forming green food purchase intentions. This study addresses that gap by exploring the cognitive–affective negotiation process underlying consumers’ green food choices. Based on 26 semi-structured interviews with Chinese consumers across diverse socio-economic backgrounds, the grounded theory methodology was employed to inductively construct a conceptual model. The coding process achieved theoretical saturation, while sentiment analysis was integrated to trace the emotional valence of key behavioral drivers. Findings reveal that external factors—including price sensitivity, label ambiguity, access limitations, social influence, and health beliefs—shape behavioral intentions indirectly through three core affective mediators: green trust, perceived value, and lifestyle congruence. These internal constructs translate contextual stimuli into evaluative and motivational responses, highlighting the dynamic interplay between rational judgments and symbolic–emotional interpretations. Sentiment analysis confirmed that emotional trust and psychological reassurance are pivotal in facilitating consumption intention, while price concerns and skepticism act as affective inhibitors. The proposed model extends the Theory of Planned Behavior by embedding affective mediation pathways and structural constraint dynamics, offering a more context-sensitive framework for understanding sustainable consumption behaviors. Given China’s certification-centered trust environment, these findings underscore the cultural specificity of institutional trust mechanisms, with implications for adapting the model in different market contexts. Practically, this study offers actionable insights for policymakers and marketers to enhance eco-label transparency, reduce structural barriers, and design emotionally resonant brand narratives that align with consumers’ identity aspirations. Full article

In this study, cerium with different ratios (x = 0 (zero), 0.1, 0.15, 0.5) was added to the PrMn structure as an A-site material to evaluate characteristic behavior as a potential cathode material for solid oxide fuel cells. The Pr_xCe_1−xMnO_3−δ electrocatalysts were synthesized using the sol–gel combustion method and were assessed for their electrochemical, phase, and structural properties, as well as desorption and reducibility capabilities. Phase changes, from orthorhombic to cubic structures observed upon cerium additions, were evaluated via the X-Ray diffraction method. X-Ray photoelectron spectroscopy (XPS) showed the valence states of the surface between the Ce⁴⁺/Ce³⁺ and Pr⁴⁺/Pr³⁺ redox pairs, while oxygen temperature programmed desorption (O₂-TPD) analysis was used to evaluate the oxygen adsorption and desorption behavior of the electrocatalysts. Redox characterization, evaluated via hydrogen atmosphere temperature-programmed reduction (H₂-TPR), revealed that a higher cerium ratio in the structure lowered the reduction temperature, suggesting a better dynamic oxygen exchange capability at a lower temperature for the Pr_0.5Ce_0.5MnO_3−δ catalyst compared to the electrochemical behavior analysis by the electrochemical impedance spectroscopy method. Moreover, the symmetrical cell tests with Pr_0.5Ce_0.5MnO_3−δ electrodes showed that, when combined with scandia-stabilized zirconia (ScSZ) electrolyte, the overall polarization resistance was reduced by approximately 28% at 800 °C compared to cells with yttria-stabilized zirconia (YSZ) electrolyte. Full article

Vanadium-based electrode materials are limited in practical applications, due to their low energy density, cycling instability, and poor electrochemical stability. To address these limitations, a wood-derived vanadium oxide (VO_x) electrode was developed through sol–gel assembly followed by thermal annealing, in which VO_x aerogel formed within the vertically aligned wood channels, resulting in a continuous porous network to mitigate particle aggregation and enhance ion diffusion. After thermal annealing at 800 °C, V⁵⁺ partially converts to V⁴⁺, forming a mixed-valence heterostructure that significantly increases the density of redox-active sites and facilitates efficient charge transfer. The optimized VO_x@Wood-800 °C (VOW-800) electrode exhibits a high specific capacitance of 317.8 F g⁻¹ at 2 mA cm⁻² and a specific surface area of 111.22 m⁻² g⁻¹, attributed to the synergistic effects of the mixed-valence structure and the enhanced ion accessibility provided by the wood-derived porous framework. This approach offers a promising pathway for developing vanadium-based electrodes with improved charge storage capacity and interface stability. Full article

The menstrual cycle affects the autonomic nervous system (ANS), cognition, and emotional valence in all biological women. There exists a complex relationship between hormonal fluctuations, ANS, cognition, and emotional valence during the different phases of the menstrual cycle, which includes menstruation, the follicular phase, ovulation, and the luteal phase. Hence, this narrative review is an attempt to comprehensively understand the effects of the menstrual cycle on the structural and functional integrity of the ANS. In order to provide a comprehensive understanding of the complex relationship between the menstrual cycle, hormonal fluctuations, and ANS function in biological women, this review examines key parameters, including heart rate variability (HRV), baroreflex sensitivity (BRS), muscle sympathetic nerve activity (MSNA), and pupillary light reflex (PLR), to investigate how these physiological systems are dynamically influenced by the cyclical changes in hormone levels and how these fluctuations impact various physiological and psychological outcomes, such as mood, cognition, and emotional regulation. There have been several studies previously performed to assess these parameters during different phases of the menstrual cycle. However, the results have been contradictory; therefore, this review explores possible reasons behind these inconsistent results, with likely reasons including irregularity in the menstrual cycles and differences in hormonal fluctuations between different women during similar phases of the menstrual cycle. Overall, there appears to be evidence to suggest that the menstrual cycle has both direct and indirect effects on ANS, cognition, and emotional valence, whilst measures of ANS may provide a means for assessing the effect of the menstrual cycle. Full article

Cognitive flexibility, the ability to adapt behavior to changing environmental demands, is a core deficit in schizophrenia (SZ), that predicts disease progression. This review synthesizes findings on the neural substates of cognitive flexibility by using a framework that distinguishes animal model tasks by their motivational valence: aversive versus appetitive. While human studies using tasks like the Wisconsin Card Sorting Test (WCST) reveal significant cognitive inflexibility in SZ, particularly in set shifting, rodent models provide important mechanistic insights. The current literature suggests that aversive tasks, such as water mazes, and appetitive tasks, such as the Birrel–Brown discrimination task, engage distinct neural circuits, despite assessing supposedly similar cognitive processes. Aversive paradigms primarily rely on hippocampal–medial prefrontal cortex (mPFC) pathways, whereas appetitive tasks heavily involve orbitofrontal cortex (OFC)–striatal circuits, with significant modulation by dopamine and serotonin. Both valences seem to require an intact balance of glutamate and GABA transmission within prefrontal regions. This framework helps clarify inconsistencies in the literature and underscores how motivational context shapes the neural substrates of cognitive flexibility. Full article

Ferroelectric semiconductors, with their inherent spontaneous polarization, present a promising approach for efficient charge separation, making them attractive for photovoltaic applications. The potential of β-AgGaO₂, a polar ternary oxide with an orthorhombic Pna2₁ structure, as a light-absorbing material is evaluated. First-principles computational analysis reveals that β-AgGaO₂ possesses an indirect bandgap of 2.1 eV and exhibits pronounced absorption within the visible spectral range. Optical simulations suggest that a 300 nm thick absorber layer could theoretically achieve a power conversion efficiency (PCE) of 20%. Device-level simulations using SCAPS-1D evaluate the influence of hole and electron transport layers on solar cell performance. Among the tested hole transport materials, Cu₂FeSnS₄ (CFTS) achieves the highest PCE of 14%, attributed to its optimized valence band alignment and reduced recombination losses. In contrast, no significant improvements were observed with the electron transport layers tested. These findings indicate the potential of β-AgGaO₂ as a ferroelectric photovoltaic absorber and emphasize the importance of band alignment and interface engineering for optimizing device performance. Full article

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

Mixing enthalpy (ΔH_mix), mixing entropy (ΔS_mix), atomic-size difference (δ), and valence electron concentration (VEC) are the indicators determining the phase structures of multi-principal element alloys. Exploring the relationships between the structures and properties of multi-principal element films is a fundamental study. TiZrHf films with a ΔH_mix of 0.00 kJ/mol, ΔS_mix of 9.11 J/mol·K (1.10R), δ of 3.79%, and VEC of 4.00 formed a hexagonal close-packed (HCP) solid solution. Exploring the characterization of TiZrHf films after solving Ta, Y, and Cr atoms with distinct atomic radii is crucial for realizing multi-principal element alloys. This study fabricated TiZrHf, TiZrHfTa, TiZrHfY, and TiZrHfCr films through co-sputtering. The results indicated that TiZrHfTa films formed a single body-centered cubic (BCC) solid solution. In contrast, TiZrHfY films formed a single HCP solid solution, and TiZrHfCr films formed a nanocrystalline BCC solid solution. The crystallization of TiZrHf(Ta, Y, Cr) films and the four indicators mentioned above for multi-principal element alloy structures were correlated. The mechanical properties and thermal stability of the TiZrHf(Ta, Y, Cr) films were investigated. 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

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article