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The proliferation of electric and hybrid vehicles has made it possible for people to read and work in a stationary vehicle for extended periods. However, the current commonly used in–vehicle lighting design is still centered around driving and driving safety. Following recommendations from the literature, a neutral white color band (4000 K–5000 K) with 50–100 lx at the vehicle table area is favored. Whether this lighting environment can meet the needs to enhance the reading performance in a modern vehicle was investigated in this presented study. Therefore, in total, 12 lighting settings were designed based on combinations of four illuminance levels (50 lx, 100 lx, 150 lx and 200 lx) and three correlated color temperatures (3000 K, 4000 K and 5000 K); we recruited 19 subjects (12 females, 7 males) and let study participants evaluate each condition based on electronic and paper reading. Next, subjective preferences, positive and negative emotions, feeling of fatigue and sustained attention were tested. We found that higher illuminance and higher CCT (Correlated Color Temperature) can significantly improve the performance of in–vehicle readers in most aspects following Kruithof's law (p < 0.05). Among them, we recommend the combination of 150 lx and 4000 K as the light parameters for in–vehicle reading as a new development guideline. In addition, we also discovered the inconsistency of people's lighting preferences between in–vehicle spaces and conventional spaces. For indoor lighting, illuminance values up to 1000 lx are still favored. For an in–vehicle function, starting with 200 lx, the preference level and reading performance already declined. In comparison between electronic and paper reading, both were similarly evaluated. These results show that a neutral white light color should be chosen with a horizontal illuminance of maximal 150 lx for a reading light function independent of the reading device. Interdisciplinarily speaking, our findings can be applied in similar small spaces or transportation modes with gentle acceleration and deceleration such as small space hotel rooms, trains, airplanes or ships. [ABSTRACT FROM AUTHOR]

Relieving the emotions of the public through government response is an important part of government emergency management. How governments respond in different situations can avoid stimulating negative emotions during emergencies? This paper analyzes the problem from the perspective of configuration; that is, this paper explores the combined effects of multiple factors on emotions. We construct the theoretical framework "Situation-Responder-Content" from situation, responder and response content, and use the government microblogs (n= 1517) from 23 major production accidents in China for the discussion with the use of fuzzy set qualitative comparison analysis (fsQCA). According to the results, the effective response types of different agencies in emergencies are summarized. Local authorities can respond in ways that include "Measures type" and "Measures-Emotion type". Functional agencies can respond through "Measures type", "Measures-Emotion type" and "Government feature-Driven" type. This study emphasizes that government response in emergencies is a systematic process. Responsive agencies need to release effective information on the basis of fully considering the situation and other factors. Configuration analysis should also be an important direction in government response research, which is helpful to the practice of government response in emergencies. [ABSTRACT FROM AUTHOR]

Mauli ola, optimal health and wellbeing from a Hawaiian perspective, is achieved by being pono, or morally just and upright, and maintaining an intricate balance physically, mentally, spiritually, and emotionally through one's relations. Cultural practices, including practices that foster a connection to the water, land, ocean, and natural environment, may serve as protective and resilience factors, thereby promoting health and wellbeing. This paper starts by sharing the genealogical foundations of cultural practices in Hawai'i as the foundations of Native Hawaiian lifestyles and ways of knowing. The paper proceeds with data analyses that aim to better understand the role of cultural practices in relation to connectedness to 'Āina (the land, nature, and the environment, which nourish our bodies) and Native Hawaiian health based on cross-sectional correlations and qualitative data. The findings demonstrate the importance of cultural practices, specifically 'Āina practices, and identify 'Āina protection, restoration, and conservation as major health priorities. The correlations demonstrate statistically significant relationships between cultural practices; a physical, mental, spiritual, and emotional connection to 'Āina; and health outcomes. These findings continue to support literature and other declarations that support healthcare and medicine that are culturally grounded in Indigenous values and traditional systems of medicine. [ABSTRACT FROM AUTHOR]

Countless articles and books have been written about transformational leadership theory since the late 1970s when it first appeared in the literature. The purpose of this conceptual paper is to illustrate that transformational leadership contains both logical and psychological problems when explaining the nature of leadership and as an empirically supportable and verifiable construct. It aims to show that its failure to garner evidence from a scientific methodological analysis may not invalidate its efficacy if it is viewed from an alternative lens, such as aesthetics. An aesthetic frame is one that recognizes sensuous ways of knowing since feelings and emotions are just as important as reason and logic. An aesthetic approach to leadership would see leadership as more of an art than a science. This paper is not arguing for the abandonment of transformational leadership theory; rather it is saying that an aesthetic lens is likely to yield a richer, more artistic, and more nuanced account of what is understood and enacted as transformational leadership. This way, the manifestations of transformational leadership may live on in the arts and continue to inspire and motivate us. [ABSTRACT FROM AUTHOR]

This document discusses the emerging trends in biomedical signal processing for intelligent emotion recognition. It highlights the potential impact of these advancements on various fields, including mental health, neuroscience, robotics, advertising, and marketing. The document also presents six research papers that delve into the world of emotion recognition using EEG signals and advanced artificial intelligence techniques. These papers explore novel approaches to subject-independent emotion recognition, spatial-temporal feature learning, electrode combination, ensemble learning techniques, and the application of neuroscience in the tourism industry. The document emphasizes the importance of interdisciplinary approaches and advanced technologies in enhancing our understanding of emotions and their neural substrates. [Extracted from the article]

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency–space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency–space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence–arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence–arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) plays a crucial role in understanding human emotions and is becoming increasingly relevant in educational contexts, where personalized and empathetic interactions are essential. The problems with existing approaches are typically solved using a single deep learning method, which is not robust with complex datasets, such as FER data, which have a characteristic imbalance and multi-class labels. In this research paper, an innovative approach to FER using a homogeneous ensemble convolutional neural network, called HoE-CNN, is presented for future online learning education. This paper aims to transfer the knowledge of models and FER classification using ensembled homogeneous conventional neural network architectures. FER is challenging to research because there are many real-world applications to consider, such as adaptive user interfaces, games, education, and robot integration. HoE-CNN is used to improve the classification performance on an FER dataset, encompassing seven main multi-classes (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral). The experiment shows that the proposed framework, which uses an ensemble of deep learning models, performs better than a single deep learning model. In summary, the proposed model will increase the efficiency of FER classification results and solve FER2013 at a accuracy of 75.51%, addressing both imbalanced datasets and multi-class classification to transfer the application of the model to online learning applications. [ABSTRACT FROM AUTHOR]

Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and "Other NNs", which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends. [ABSTRACT FROM AUTHOR]

Emotionally intelligent people are thought to be more skilled in recognizing, thinking about, using, and regulating emotions. This construct has garnered considerable interest, but initial enthusiasm has faded and it is time to take stock. There is consensus that ability-related measures of emotional intelligence (EI) can be favored to self-report tests, in part because the resulting scores cannot be equated with personality traits. However, there are questions surrounding measurement as well as predictive value. Experts in the field were encouraged to chart new directions, with the idea that these new directions could reinvigorate EI scholarship. Special Issue papers speak to theory, mechanism, measurement, and training. In addition, these papers seek to forge links with research traditions focused on interpersonal perception, emotional awareness, and emotion regulation. As a result of these efforts, new insights into what EI is and how it works can be anticipated in upcoming years. [ABSTRACT FROM AUTHOR]

The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA's video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson's correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions.

The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expressions, the emotional color of the tone of a conversation and the text transmitted by a person. The paper proposes different neural network structures for emotion recognition based on unimodal flows and models for the margin of the multimodal data. The analysis determined that the best classification accuracy is obtained for systems with data fusion after processing each channel separately and obtaining individual characteristics. The final analysis of the model based on data from a camera and microphone or recording or broadcast of the screen, which were received in the "live" mode, gave a clear understanding that the quality of the obtained results is highly dependent on the quality of the data preparation and labeling. This is directly related to the fact that the data on which the neural network is trained is highly qualified. The neural network with combined data on the penultimate layer allows a psycho-emotional state recognition accuracy of 0.90 to be obtained. The spatial distribution of emotion analysis was also analyzed for each data modality. The model with late fusion of multimodal data demonstrated the best recognition accuracy. [ABSTRACT FROM AUTHOR]

The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and X (formerly Twitter). [ABSTRACT FROM AUTHOR]

Emotions in speech are expressed in various ways, and the speech emotion recognition (SER) model may perform poorly on unseen corpora that contain different emotional factors from those expressed in training databases. To construct an SER model robust to unseen corpora, regularization approaches or metric losses have been studied. In this paper, we propose an SER method that incorporates relative difficulty and labeling reliability of each training sample. Inspired by the Proxy-Anchor loss, we propose a novel loss function which gives higher gradients to the samples for which the emotion labels are more difficult to estimate among those in the given minibatch. Since the annotators may label the emotion based on the emotional expression which resides in the conversational context or other modality but is not apparent in the given speech utterance, some of the emotional labels may not be reliable and these unreliable labels may affect the proposed loss function more severely. In this regard, we propose to apply label smoothing for the samples misclassified by a pre-trained SER model. Experimental results showed that the performance of the SER on unseen corpora was improved by adopting the proposed loss function with label smoothing on the misclassified data.

Human emotions are complex psychological and physiological responses to external stimuli. Correctly identifying and providing feedback on emotions is an important goal in human-computer interaction research. Compared to facial expressions, speech, or other physiological signals, using electroencephalogram (EEG) signals for the task of emotion recognition has advantages in terms of authenticity, objectivity, and high reliability; thus, it is attracting increasing attention from researchers. However, the current methods have significant room for improvement in terms of the combination of information exchange between different brain regions and time-frequency feature extraction. Therefore, this paper proposes an EEG emotion recognition network, namely, self-organized graph pesudo-3D convolution (SOGPCN), based on attention and spatiotemporal convolution. Unlike previous methods that directly construct graph structures for brain channels, the proposed SOGPCN method considers that the spatial relationships between electrodes in each frequency band differ. First, a self-organizing map is constructed for each channel in each frequency band to obtain the 10 most relevant channels to the current channel, and graph convolution is employed to capture the spatial relationships between all channels in the self-organizing map constructed for each channel in each frequency band. Then, pseudo-three-dimensional convolution combined with partial dot product attention is implemented to extract the temporal features of the EEG sequence. Finally, LSTM is employed to learn the contextual information between adjacent time-series data. Subject-dependent and subject-independent experiments are conducted on the SEED dataset to evaluate the performance of the proposed SOGPCN method, which achieves recognition accuracies of 95.26% and 94.22%, respectively, indicating that the proposed method outperforms several baseline methods.

Within the field of research on the promotion of teachers' social and emotional competence, the present paper illustrates preliminary evidence of the efficacy of a new training program named "ME4YOU" aimed at supporting teachers' self-reflexive competences to deal with the emotional and relational dimensions of teaching, with constant and continuous attention towards underlining the close connection between the way teachers perform as professionals at work and the way they function as individuals in their personal life. A total of 109 teachers from kindergarten to primary school took part in the experimental group, while 67 teachers constituted the control group; the two groups were compared using a pre-test/post-test approach with regard to some self-reported variables related to professional and personal aspects. Teachers in the experimental group exhibited increased levels of professional self-efficacy and self-efficacy as emotional socializers toward students' emotions; moreover-although with a more limited impact-they reported benefits with regard to their personal life (i.e., reduced denial of own emotions and improved authenticity). The findings are discussed highlighting that health promotion among teachers is both of value in itself and an investment that can generate health in the whole school system.

Social-emotional learning (SEL) is a rapidly growing field of research that has garnered significant attention in recent years. Each facet of SEL research in fields such as education, mental health, and developmental research has used specific methodologies and terms in their narrow research focus. In education specifically, where the most SEL research has been produced, many frameworks have implementation requirements. The lack of a framework focused on overarching themes without implementation requirements prevents the fields from coming together to compile and compare research and progress to create parent-, adult-, or mental health-specific SEL programs. This paper provides a conceptual analysis of SEL, aimed at clarifying the concept and deconstructing its various facets. This framework is needed to acknowledge the many different terms and skills for the same principle while also narrowing down definitions for clarity. The resulting framework can be used as a basis for future research, practice, and policy discussions in the field.

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

Joy is a central theme in Philippians. Joy is also a central emotional habit Paul deploys and encourages as a strategy for building community. In this paper, the examination of Philippians through recent developments in the neurocognitive study of emotions first illuminates how Paul cultivates emotional habits, like joy. Second, a neurocognitive approach to understanding emotions can explain how repeatedly choosing joy functions to alleviate suffering by restoring balance in the nervous system. Finally, shared emotional habits with the Philippian community, like shared somatic practices, build sustaining connections among the members. Intentional deployment of emotional practices, as Paul demonstrates and encourages in the Philippians, is a strategy for building the body of Christ. Such a neurocognitive understanding of emotional habits and bodily practices among community members in the Christ body points to a corporate experience of shared healing and neurocognitive resilience. Might we consider this somatic embodiment of shared emotions, what Paul calls "the same mind that is in Christ Jesus", also a participation in Christ's soteria (healing/salvation)? [ABSTRACT FROM AUTHOR]

The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data modalities, including acoustic and visual streams. State-of-the-art approaches leverage transformer-based architectures to combine multimodal sources. However, the impressive performance of MLLMs in content retrieval and generation offers new opportunities to extend the capabilities of existing emotion recognizers. This paper explores the performance of MLLMs in the emotion recognition task in a zero-shot learning setting. Furthermore, it presents a state-of-the-art architecture extension based on MLLM content reformulation. The performance achieved on the Hume-Reaction benchmark shows that MLLMs are still unable to outperform the state-of-the-art average performance but, notably, are more effective than traditional transformers in recognizing emotions with an intensity that deviates from the average of the samples. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to explore the difficulties the refugee minors face in the process of seeking inclusion in Portugal. The project conducting this research takes place in a shelter for refugee minors in Portugal, and its inhabitants are the main focus of the analysis. The research is guided by Applied Anthropology which means that, in methodological terms, we use active listening, participant observation, and artistic workshops in drawing and photography in order to get to know them better and establish a closer relationship with the minors. Our interlocutors are refugee minors, boys and girls, aged between 15 and 18, who have been institutionalized by an NGOD responsible for hosting them in Portugal while they await their residence permit. These minors are characterized by a diversity of nationalities, socio-cultural and ethnolinguistic references and different life projects. The artistic focus (drawing and photography) is explored as a non-invasive methodology, a tool that best helps to express emotions, perceptions and desires in a creative and uncompromising way. We chose to focus our analysis not on the traumatic past, as is usually the case when it comes to refugees, but on the future. In other words: we invite these minors to think about their future, what they want for themselves in Portugal and the European Union. As we discuss our difficulties of conducting fieldwork in the institutionalized context, we conclude the article with the idea that working with refugee minors in that environment should not be an end in and of itself, but rather an opportunity to do more profound research. This investigation should continue, preferably outside the institutional gates, on a more personalized basis. As a practical goal, we also propose the idea of organizing an exhibition with the results of the workshops to facilitate the two-way process of inclusion. [ABSTRACT FROM AUTHOR]

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method's scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management's accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations. [ABSTRACT FROM AUTHOR]

Introduction: Evaluation of post-nephrectomy social health in living kidney donors is essential. This systematic review examines their emotional need for social relatedness post-donation. Methods: Following the PRISMA guidelines, we systematically searched Scopus, CINAHL, and PsycINFO. Results: Among the screened records, 32 quantitative and 16 qualitative papers met the inclusion criteria. Quantitative research predominantly utilized questionnaires featuring generic items on social functioning. However, a minority delved into emotional and social dimensions, aligning with qualitative studies emphasizing the importance of social connection and perceived social support post-donation. Specifically, post-donation changes in connecting with others encompass a sense of belongingness, heightened autonomy, shifts in concern for the recipient's health, and continued care by shielding the recipient from personal health issues. Social acknowledgment and social support from both close and extended networks are reported as relevant for recovery after nephrectomy. Discussion: These findings underscore the necessity for targeted measures of emotional needs and social functioning to effectively assess post-donation adjustment. They also inform the identification of key health themes for kidney donor Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) questions. [ABSTRACT FROM AUTHOR]

Online education review data have strong statistical and predictive power but lack efficient and accurate analysis methods. In this paper, we propose a multi-modal emotion analysis method to analyze the online education of college students based on educational data. Specifically, we design a multi-modal emotion analysis method that combines text and emoji data, using pre-training emotional prompt learning to enhance the sentiment polarity. We also analyze whether this fusion model reflects the true emotional polarity. The conducted experiments show that our multi-modal emotion analysis method achieves good performance on several datasets, and multi-modal emotional prompt methods can more accurately reflect emotional expressions in online education data. [ABSTRACT FROM AUTHOR]

In the landscape of cancer treatment, particularly in the realm of breast cancer management, effective communication emerges as a pivotal factor influencing patient outcomes. This article delves into the nuanced intricacies of communication skills, specifically spotlighting the strategies embraced by breast radiologists. By examining the ramifications of communication on patient experience, interdisciplinary collaboration, and legal ramifications, this study underscores the paramount importance of empathetic and comprehensive communication approaches. A special emphasis is placed on the utilization of the SPIKES protocol, a structured method for conveying sensitive health information, and the deployment of strategies for navigating challenging conversations. Furthermore, the work encompasses the significance of communication with caregivers, the integration of artificial intelligence, and the acknowledgement of patients' psychological needs. By adopting empathetic communication methodologies and fostering multidisciplinary collaboration, healthcare practitioners have the potential to enhance patient satisfaction, promote treatment adherence, and augment the overall outcomes within breast cancer diagnosis. This paper advocates for the implementation of guidelines pertaining to psychological support and the allocation of sufficient resources to ensure the provision of holistic and patient-centered cancer care. The article stresses the need for a holistic approach that addresses patients' emotional and psychological well-being alongside medical treatment. Through thoughtful and empathetic communication practices, healthcare providers can profoundly impact patient experiences and breast cancer journeys in a positive manner. [ABSTRACT FROM AUTHOR]

Wide adoption of social media has caused an explosion of information stored online, with the majority of that information containing subjective, opinionated, and emotional content produced daily by users. The field of emotion analysis has helped effectively process such human emotional expressions expressed in daily social media posts. Unfortunately, one of the greatest limitations of popular word-based emotion analysis systems has been the limited emotion vocabulary. This paper presents an attempt to extensively expand one such word-based emotion analysis system by integrating multiple emotion dictionaries and implementing an automatic extraction mechanism for emotive expressions. We first leverage diverse emotive expression dictionaries to expand the emotion lexicon of the system. To do that, we solve numerous problems with the integration of various dictionaries collected using different standards. We demonstrate the performance improvement of the system with improved accuracy and granularity of emotion classification. Furthermore, our automatic extraction mechanism facilitates the identification of novel emotive expressions in an emotion dataset, thereby enriching the depth and breadth of emotion analysis capabilities. In particular, the automatic extraction method shows promising results for applicability in further expansion of the dictionary base in the future, thus advancing the field of emotion analysis and offering new avenues for research in sentiment analysis, affective computing, and human–computer interaction. [ABSTRACT FROM AUTHOR]

Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC. [ABSTRACT FROM AUTHOR]

In the research into semantic sentiment analysis, researchers commonly use some factor rules, such as the utilization of emotional keywords and the manual definition of emotional rules, to increase accuracy. However, this approach often requires extensive data and time-consuming training, and there is a need to make the system simpler and more efficient. Recognizing these challenges, our paper introduces a new semantic sentiment analysis system designed to be both higher in quality and more efficient. The structure of our proposed system is organized into several key phases. Initially, we focus on data training, which involves studying emotions and emotional psychology. Utilizing linguistic resources such as HowNet and the Chinese Knowledge and Information Processing (CKIP) techniques, we develop emotional rules that facilitate the generation of sparse representation characteristics. This process also includes constructing a sparse representation dictionary. We can map these back to the original vector space by resolving the sparse coefficients, representing two distinct categories. The system then calculates the error compared to the original vector, and the category with the minimum error is determined. The second phase involves inputting topics and collecting relevant comments from internet forums to gather public opinion on trending topics. The final phase is data classification, where we assess the accuracy of classified issues based on our data training results. Additionally, our experimental results will demonstrate the system's ability to identify hot topics, thus validating our semantic classification models. This comprehensive approach ensures a more streamlined and effective system for semantic sentiment analysis. [ABSTRACT FROM AUTHOR]

Robots are becoming an increasingly important part of our society and have started to be used in tasks that require communicating with humans. Communication can be decoupled in two dimensions: symbolic (information aimed to achieve a particular goal) and spontaneous (displaying the speaker's emotional and motivational state) communication. Thus, to enhance human–robot interactions, the expressions that are used have to convey both dimensions. This paper presents a method for modelling a robot's expressiveness as a combination of these two dimensions, where each of them can be generated independently. This is the first contribution of our work. The second contribution is the development of an expressiveness architecture that uses predefined multimodal expressions to convey the symbolic dimension and integrates a series of modulation strategies for conveying the robot's mood and emotions. In order to validate the performance of the proposed architecture, the last contribution is a series of experiments that aim to study the effect that the addition of the spontaneous dimension of communication and its fusion with the symbolic dimension has on how people perceive a social robot. Our results show that the modulation strategies improve the users' perception and can convey a recognizable affective state. [ABSTRACT FROM AUTHOR]

This paper researches the classification of human emotions in a virtual reality (VR) context by analysing psychophysiological signals and facial expressions. Key objectives include exploring emotion categorisation models, identifying critical human signals for assessing emotions, and evaluating the accuracy of these signals in VR environments. A systematic literature review was performed through peer-reviewed articles, forming the basis for our methodologies. The integration of various emotion classifiers employs a 'late fusion' technique due to varying accuracies among classifiers. Notably, facial expression analysis faces challenges from VR equipment occluding crucial facial regions like the eyes, which significantly impacts emotion recognition accuracy. A weighted averaging system prioritises the psychophysiological classifier over the facial recognition classifiers due to its higher accuracy. Findings suggest that while combined techniques are promising, they struggle with mixed emotional states as well as with fear and trust emotions. The research underscores the potential and limitations of current technologies, recommending enhanced algorithms for effective interpretation of complex emotional expressions in VR. The study provides a groundwork for future advancements, aiming to refine emotion recognition systems through systematic data collection and algorithm optimisation. [ABSTRACT FROM AUTHOR]

This paper presents a research program called CP2DIMG conducted at the Federation of Environment and Society Research at the University of Corsica. The goal of CP2DIMG is to better understand the influence of emotions on operational personnel's decision-making, aiming to test training systems dedicated to individuals facing high stress during their professional activities. This type of training system is intended to enhance emotional and mental resilience, thereby improving decision-making ability in uncertain situations under the influence of emotions related to the event. For implementation, the method will be tailored to the specificities of two categories of operational personnel: firefighters and municipal police officers. The expected results will address significant demands from operational professionals in the Mediterranean region for firefighting safety but also for large-scale or highly complex interventions. This study fully integrates into the challenges of the Mediterranean region: forest management, risk prevention plans, and preparedness of local actors responsible for crisis management. Furthermore, individuals responsible for crisis management, including local government officials and risk management and security personnel, will be able to use the obtained results for effective decision-making. [ABSTRACT FROM AUTHOR]

Emotion detection holds significant importance in facilitating human–computer interaction, enhancing the depth of engagement. By integrating this capability, we pave the way for forthcoming AI technologies to possess a blend of cognitive and emotional understanding, bridging the divide between machine functionality and human emotional complexity. This progress has the potential to reshape how machines perceive and respond to human emotions, ushering in an era of empathetic and intuitive artificial systems. The primary research challenge involves developing models that can accurately interpret and analyze emotions from both auditory and textual data, whereby auditory data require optimizing CNNs to detect subtle and intense emotional fluctuations in speech, and textual data necessitate access to large, diverse datasets to effectively capture nuanced emotional cues in written language. This paper introduces a novel approach to multimodal emotion recognition, seamlessly integrating speech and text modalities to accurately infer emotional states. Employing CNNs, we meticulously analyze speech using Mel spectrograms, while a BERT-based model processes the textual component, leveraging its bidirectional layers to enable profound semantic comprehension. The outputs from both modalities are combined using an attention-based fusion mechanism that optimally weighs their contributions. The proposed method here undergoes meticulous testing on two distinct datasets: Carnegie Mellon University's Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset and the Multimodal Emotion Lines Dataset (MELD). The results demonstrate superior efficacy compared to existing frameworks, achieving an accuracy of 88.4% and an F1-score of 87.9% on the CMU-MOSEI dataset, and a notable weighted accuracy (WA) of 67.81% and a weighted F1 (WF1) score of 66.32% on the MELD dataset. This comprehensive system offers precise emotion detection and introduces several significant advancements in the field. [ABSTRACT FROM AUTHOR]

The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has progressed by using acoustic and semantic information as input and adopting a classification method to identify the person's ID and emotion for driving co-speech gesture generation. However, this endeavor still faces significant challenges. These challenges go beyond the intricate interplay among co-speech gestures, speech acoustic, and semantics; they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces "DiT-Gestures", a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with the WavLM pre-trained model and a dynamic mask attention network (DMAN). It can produce individual and stylized full-body co-speech gestures by only using raw speech audio, eliminating the need for complex multimodal processing and manual annotation. Firstly, considering that speech audio contains acoustic and semantic features and conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract the style from raw audio information. Secondly, we replace the causal mask by introducing a learnable dynamic mask for better local modeling in the neighborhood of the target frames. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm WavLM's and the model's ability to synthesize natural co-speech gestures with various styles. [ABSTRACT FROM AUTHOR]

In the past, the Romani in Slovakia identified with the prevailing religion, mainly with the Roman Catholic Church. However, the missionary activities of various Christian denominations after 1990 resulted in the conversion of the Romani to Pentecostal Christian communities. This launched a long, creative process of the formation of Pentecostal Romani music. Romani believers consider music and the ability to play and sing to be a gift from God and view these as a form of prayer that should serve for the praise of God. That is why many have given up their worldly music making and now play only praise songs. They gradually modified the hymns they borrowed and replaced them with their own creations. The soundscape of religion does not lie only in religious singing and music, as the emotional sermons and prayers, glossolalia and sounds during the healing and blessing rituals can also be considered religious sounds. During the worship services, this mixture of various sounds leads to the gradual spiritual and emotional unification of the community. The music and the rituals create feelings of intense sensory and emotional character that reflect in bodily expressions. Movements, dance, and the positions of the hands can help glorify God and experience the worship service more intensely. However, under certain circumstances, they might become sources of temptation and sin. This is related to the concepts of "purity" and "impurity". The premises, whether sacral or profane, interior or exterior ones, also play a significant role in creating the sound. In writing this paper, I have also drawn on my own research on Romani Christian songs, which I carried out in (2012–2013 in Eastern Slovakia). [ABSTRACT FROM AUTHOR]

Simple Summary: The vocalisations that an animal produces could provide a window into its emotional state. More knowledge on how emotional states are expressed in the vocalisations of birds could even be used to improve the welfare of farmed poultry. The present study introduced a novel device designed to trigger different emotional states in hens using different physical and chemical stimuli. The final device was able to elicit a broad range of vocalisations without any human interference. It was found that the hens not only actively used the device as a feeder but also responded vocally to both positive and negative stimuli. Preliminary findings indicate that the vocal responses of the hens vary as a function of the intensity of the emotional state they experience. This research contributes to understanding poultry emotions and has the potential to open new opportunities in understanding and improving their welfare in farming environments. Understanding the emotional states of animals is a long-standing research endeavour that has clear applications in animal welfare. Vocalisations are emerging as a promising way to assess both positive and negative emotional states. However, the vocal expression of emotions in birds is a relatively unexplored research area. The goal of this study was to develop an interactive feeding system that would elicit positive and negative emotional states, and collect recordings of the vocal expression of these emotions without human interference. In this paper, the mechatronic design and development of the feeder is described. Design choices were motivated by the desire for the hens to voluntarily interact with the feeder and experience the different stimuli that were designed to induce (1) positive low-arousal, (2) positive high-arousal, (3) negative low-arousal, and (4) negative high-arousal states. The results showed that hens were motivated to engage with the feeder despite the risk of receiving negative stimuli and that this motivation was sustained for at least 1 week. The potential of using the interactive feeder to analyse chicken vocalisations related to emotional valence and arousal is being explored, offering a novel proof of concept in animal welfare research. Preliminary findings suggest that hens vocalised in response to all four stimulus types, with the number of vocalisations, but not the probability of vocalising, distinguishing between low- and high-arousal states. Thus, the proposed animal–computer interaction design has potential to be used as an enrichment device and for future experiments on vocal emotions in birds. [ABSTRACT FROM AUTHOR]

Facial expression is an important way to reflect human emotions and it represents a dynamic deformation process. Analyzing facial movements is an effective means of understanding expressions. However, there is currently a lack of methods capable of analyzing the dynamic details of full-field deformation in expressions. In this paper, in order to enable effective dynamic analysis of expressions, a classic optical measuring method called stereo digital image correlation (stereo-DIC or 3D-DIC) is employed to analyze the deformation fields of facial expressions. The forming processes of six basic facial expressions of certain experimental subjects are analyzed through the displacement and strain fields calculated by 3D-DIC. The displacement fields of each expression exhibit strong consistency with the action units (AUs) defined by the classical Facial Action Coding System (FACS). Moreover, it is shown that the gradient of the displacement, i.e., the strain fields, offers special advantages in characterizing facial expressions due to their localized nature, effectively sensing the nuanced dynamics of facial movements. By processing extensive data, this study demonstrates two featured regions in six basic expressions, one where deformation begins and the other where deformation is most severe. Based on these two regions, the temporal evolutions of the six basic expressions are discussed. The presented investigations demonstrate the superior performance of 3D-DIC in the quantitative analysis of facial expressions. The proposed analytical strategy might have potential value in objectively characterizing human expressions based on quantitative measurement. [ABSTRACT FROM AUTHOR]

Similar to traditional imaging, virtual reality (VR) imagery encompasses nonstereoscopic (VR-2D) and stereoscopic (VR-3D) modes. Currently, Russell's emotional model has been extensively studied in traditional 2D and VR-3D modes, but there is limited comparative research between VR-2D and VR-3D modes. In this study, we investigate whether Russell's emotional model exhibits stronger brain activation states in VR-3D mode compared to VR-2D mode. By designing an experiment covering four emotional categories (high arousal–high pleasure (HAHV), high arousal–low pleasure (HALV), low arousal–low pleasure (LALV), and low arousal–high pleasure (LAHV)), EEG signals were collected from 30 healthy undergraduate and graduate students while watching videos in both VR modes. Initially, power spectral density (PSD) computations revealed distinct brain activation patterns in different emotional states across the two modes, with VR-3D videos inducing significantly higher brainwave energy, primarily in the frontal, temporal, and occipital regions. Subsequently, Differential entropy (DE) feature sets, selected via a dual ten-fold cross-validation Support Vector Machine (SVM) classifier, demonstrate satisfactory classification accuracy, particularly superior in the VR-3D mode. The paper subsequently presents a deep learning-based EEG emotion recognition framework, adeptly utilizing the frequency, spatial, and temporal information of EEG data to improve recognition accuracy. The contribution of each individual feature to the prediction probabilities is discussed through machine-learning interpretability based on Shapley values. The study reveals notable differences in brain activation states for identical emotions between the two modes, with VR-3D mode showing more pronounced activation. [ABSTRACT FROM AUTHOR]

Human emotion recognition is crucial in various technological domains, reflecting our growing reliance on technology. Facial expressions play a vital role in conveying and preserving human emotions. While deep learning has been successful in recognizing emotions in video sequences, it struggles to effectively model spatio-temporal interactions and identify salient features, limiting its accuracy. This research paper proposed an innovative algorithm for facial expression recognition which combined a deep learning algorithm and dynamic texture methods. In the initial phase of this study, facial features were extracted using the Visual-Geometry-Group (VGG19) model and input into Long-Short-Term-Memory (LSTM) cells to capture spatio-temporal information. Additionally, the HOG-HOF descriptor was utilized to extract dynamic features from video sequences, capturing changes in facial appearance over time. Combining these models using the Multimodal-Compact-Bilinear (MCB) model resulted in an effective descriptor vector. This vector was then classified using a Support Vector Machine (SVM) classifier, chosen for its simpler interpretability compared to deep learning models. This choice facilitates better understanding of the decision-making process behind emotion classification. In the experimental phase, the fusion method outperformed existing state-of-the-art methods on the eNTERFACE05 database, with an improvement margin of approximately 1%. In summary, the proposed approach exhibited superior accuracy and robust detection capabilities. [ABSTRACT FROM AUTHOR]

The implementation of Video Assistant Referees (VARs) in 2018 has had a significant impact on the multi-billion-dollar soccer industry. As the most popular and watched sport globally, soccer's financial stakes are high, with clubs, leagues, broadcasters, sponsors, and fans heavily invested in the game. The ongoing debate surrounding the VAR system brings to light the intricate balance between preserving the authenticity of football (soccer) and harnessing technology to improve accuracy. It is crucial to strike the right equilibrium in order to uphold football's metaphorical power and sustain the timeless joy it has brought to fans throughout generations. In this context, Immanuel Kant's philosophy can offer valuable insights into the utilization of VARs in soccer. According to Kantian ethics, using VARs can be justified if it serves to enhance fairness and accuracy, aligning with the moral duties of referees. Nevertheless, it is important to consider the potential dehumanizing effects and the necessity of preserving the value of human judgment in the game. Therefore, this paper aims to explore in-depth the intricate dynamics that arise when technology is integrated into traditional practices, emphasizing the significance of critical reflection on the implications of such advancements. [ABSTRACT FROM AUTHOR]

Why is contempt seen as potentially lacking in the respect for persons and therefore prima facie subject to negative moral evaluation? This paper starts by looking at a distinctive feature of contempt in the context of thick relationships, such as those of friendship, close professional collaboration, or romantic love: there is an irreversibility effect attached to the experience of contempt. Once contempt occurs in a thick relationship, it seems very difficult to return to non-contemptuous reactive attitudes. The second part argues that the irreversibility effect is due to the fact that contempt is an affective attitude which tends to invisibilize the person who is the object of contempt. The tendency to invisibilize is inscribed in the intentional structure of contempt as well as in its motivational dimension. The final part explores some consequences of this hypothesis, and in particular argues that it also explains why contempt motivated by abject wrongdoing, as opposed to resentment, anger, or hatred, tends to block any process of forgiveness. [ABSTRACT FROM AUTHOR]

The emotional, physical, and spiritual health of athletes continues to be a concern at all levels of sport. With respect to emotions and health, previous studies have sought to understand the role of normalization of emotion on elite female rowers' decisions to train regardless of their health. This research demonstrated how athletes may be persuaded to accept that emotions are negative, irrational, and weak, and this may play a significant role in subsequent unhealthy behaviours. In turn, these findings have generated further explorations into a more comprehensive emotion education for all athletes, which have focused on athletes' emotional awareness and spiritual growth. The present paper provides theoretical, educational, and practical insight into the areas of emotion and spiritual development. In doing so, it presents a conceptual model for sport chaplains, coaches, and/or sport advocates for educating and mentoring the emotional and spiritual formation of athletes. [ABSTRACT FROM AUTHOR]

Background: Personal emotions and affects have been identified and studied in the context of pandemics, as well as coping strategies centered on emotional regulation or the balance between positive and negative emotions. Objectives: The objectives of this paper are to identify an emotion and affect structure in our sample and analyze the relationship of these dimensions with resilient coping in the context of the COVID-19 pandemic. Method: This study employed a cross-sectional design that involved a non-probabilistic sample with 598 participants over the age of 18, with 51.1% being female, and an average age of 40.73 years. First, the emotional structure was identified through principal component analysis (PCA). Secondly, a linear regression analysis was performed to investigate emotional dimensions as predictors of coping. Results: A valid and reliable emotional structure with four dimensions was identified. The regression model revealed that coping is positively associated with the active and positive dimension and negatively correlated with the negative and moral dimensions. Conclusions: Emotional dimensions are predictors of coping, with moral and negative dimensions having a negative effect, while active and positive dimensions have a positive effect. When designing interventions for coping strategies, multiple dimensions of emotions and affective states in people who are in vulnerable situations must be considered. [ABSTRACT FROM AUTHOR]

Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a major barrier to achieve consistently high-performance models is the paucity of available training labels. Self-supervised learning (SSL) is a family of methods which can learn despite a scarcity of supervised labels by predicting properties of the data itself. To understand the utility of self-supervised learning for audio-based emotion recognition, we have applied self-supervised learning pre-training to the classification of emotions from the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU- MOSEI)'s acoustic data. Unlike prior papers that have experimented with raw acoustic data, our technique has been applied to encoded acoustic data with 74 parameters of distinctive audio features at discrete timesteps. Our model is first pre-trained to uncover the randomly masked timestamps of the acoustic data. The pre-trained model is then fine-tuned using a small sample of annotated data. The performance of the final model is then evaluated via overall mean absolute error (MAE), mean absolute error (MAE) per emotion, overall four-class accuracy, and four-class accuracy per emotion. These metrics are compared against a baseline deep learning model with an identical backbone architecture. We find that self-supervised learning consistently improves the performance of the model across all metrics, especially when the number of annotated data points in the fine-tuning step is small. Furthermore, we quantify the behaviors of the self-supervised model and its convergence as the amount of annotated data increases. This work characterizes the utility of self-supervised learning for affective computing, demonstrating that self-supervised learning is most useful when the number of training examples is small and that the effect is most pronounced for emotions which are easier to classify such as happy, sad, and angry. This work further demonstrates that self-supervised learning still improves performance when applied to the embedded feature representations rather than the traditional approach of pre-training on the raw input space. [ABSTRACT FROM AUTHOR]

Unwanted pregnancies are considered a public health problem that affects women's mental health and quality of life. The aim of this paper was to access university students' understanding and behaviours regarding unwanted pregnancies and identify their needs to prevent them. Qualitative descriptive design was used, and 13 semi-structured interviews were carried out. Women between 18 and 20 years old participated. They discussed a lack of training for themselves, their partners, and their families, their desire to have access to non-in-person health care resources, and their belief that contraception was expensive. Emotional aspects were relevant, affecting the way communication is established with those close to them and with health professionals. Despite the existence of access to sexual health resources, the findings show the existence of needs related to the prevention of unwanted pregnancies. The findings are presented grouping the main identified needs as "related to capability", "related to opportunity", and "related to motivation". Among the aspects to consider when designing interventions to prevent unwanted pregnancies are the feelings shown by these women, the inclusion of couples and family members in educational programs, and access to non-face-to-face health resources and less expensive contraceptive methods. Interventions for social support and understanding of women are necessary both to prevent unwanted pregnancies and to support adolescents with unwanted pregnancies by avoiding criminalization or blame. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused a global health crisis directly impacting the healthcare system. Healthcare leaders influence and shape the ability of an organization to cope with and recover from a crisis such as the COVID-19 pandemic. Their actions serve to guide and support nurses' actions through unpredictable health service demands. The purpose of this paper was to examine frontline managers' experiences and organizational leadership responses that activated organizational resilience during the COVID-19 pandemic, and to learn for ongoing and future responses to healthcare crises. Fourteen managers participated in semi-structured interviews. We found that: (1) leadership challenges (physical resources and emotional burden), (2) the influence of senior leader decision-making on managers (constant change, shortage of human resources, adapting care delivery, and cooperation and collaboration), and (3) lessons learned (managerial caring behaviours and role modelling, adaptive leadership, education and training, culture of care for self, and others) were evidence of managers' responses to the crisis. Overall, the study provides evidence of managers experiences during the early waves of the pandemic in supporting nurses and fostering organizational resilience. Knowing manager's experiences can facilitate planning, preparing, and strengthening their leadership strategies to improve work conditions is a high priority to manage and sustain nurses' mental health and wellbeing. [ABSTRACT FROM AUTHOR]

We used quantitative text analysis to examine conversations in a series of online support groups attended by care partners of people living with rare dementias (PLWRD). We used transcripts of 14 sessions (>100,000 words) to explore patterns of communication in trained facilitators' (n = 2) and participants' (n = 11) speech and to investigate the impact of session agenda on language use. We investigated the features of their communication via Poisson regression and a clustering algorithm. We also compared their speech with a natural speech corpus. We found that differences to natural speech emerged, notably in emotional tone (d = −3.2, p < 0.001) and cognitive processes (d = 2.8, p < 0.001). We observed further differences between facilitators and participants and between sessions based on agenda. The clustering algorithm categorised participants' contributions into three groups: sharing experience, self-reflection, and group processes. We discuss the findings in the context of Social Comparison Theory. We argue that dedicated online spaces have a positive impact on care partners in combatting isolation and stress via affiliation with peers. We then discuss the linguistic mechanisms by which social support was experienced in the group. The present paper has implications for any services seeking insight into how peer support is designed, delivered, and experienced by participants. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide a narrative review of the effects of physical exercise in the treatment of chronic musculoskeletal pain. Physical inactivity and sedentary behavior are associated with chronic musculoskeletal pain and can aggravate it. For the management of musculoskeletal pain, physical exercise is an effective, cheap, and safe therapeutic option, given that it does not produce the adverse effects of pharmacological treatments or invasive techniques. In addition to its analgesic capacity, physical exercise has an effect on other pain-related areas, such as sleep quality, activities of daily living, quality of life, physical function, and emotion. In general, even during periods of acute pain, maintaining a minimum level of physical activity can be beneficial. Programs that combine several of the various exercise modalities (aerobic, strengthening, flexibility, and balance), known as multicomponent exercise, can be more effective and better adapted to clinical conditions. For chronic pain, the greatest benefits typically occur with programs performed at light-to-moderate intensity and at a frequency of two to three times per week for at least 4 weeks. Exercise programs should be tailored to the specific needs of each patient based on clinical guidelines and World Health Organization recommendations. Given that adherence to physical exercise is a major problem, it is important to empower patients and facilitate lifestyle change. There is strong evidence of the analgesic effect of physical exercise in multiple pathologies, such as in osteoarthritis, chronic low back pain, rheumatoid arthritis, and fibromyalgia. [ABSTRACT FROM AUTHOR]

EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion recognition can contribute to a large spectrum of application fields including healthcare and human–computer interaction. Recent studies in neuroscience reveal that the brain regions and their interactions play an essential role in the processing of different stimuli and the generation of corresponding emotional states. Nevertheless, such regional interactions, which have been proven to be critical in recognizing emotions in neuroscience, are largely overlooked in existing machine learning or deep learning models, which focus on individual channels in brain signals. Motivated by this, in this paper, we present RGNet, a model that is designed to learn the regional level representation of EEG signal for accurate emotion recognition. Specifically, after applying preprocessing and feature extraction techniques on raw signals, RGNet adopts a novel region-wise encoder to extract the features of channels located within each region as input to compute the regional level features, enabling the model to effectively explore the regional functionality. A graph is then constructed by considering each region as a node and connections between regions as edges, upon which a graph convolutional network is designed with spectral filtering and learned adjacency matrix. Instead of focusing on only the spatial proximity, it allows the model to capture more complex functional relationships. We conducted experiments from the perspective of region division strategies, region encoders and input feature types. Our model has achieved 98.64 % and 99.33 % for Deap and Dreamer datasets, respectively. The comparison studies show that RGNet outperforms the majority of the existing models for emotion recognition from EEG signals. [ABSTRACT FROM AUTHOR]

This essay surveys the shifting emotional regimes in Western art from the eighteenth century into the twenty-first, concentrating on the place accorded social affections. In particular, it calls attention to a significant change underway in recent decades as the suppression of the full range of emotions instigated by modernism has been challenged and the tender emotions re-embraced. Important contemporary artists, such as Hank Willis Thomas and Emily Hass, are invoking and exploring themes of love, care, empathy, and concern and, in many cases, making creative use of them to advance social, political, and environmental justice. [ABSTRACT FROM AUTHOR]

This correction notice addresses errors in a published paper titled "Epistemic Challenges in Neurophenomenology: Exploring the Reliability of Knowledge and Its Ontological Implications." The corrections include a change in the author's affiliation to the Department of Philosophy at Ural Federal University in Russia, a correction of the name of philosopher Michelle Maiese, and the addition of an acknowledgment section stating that AI-assisted tools were used in the preparation of the article. The updated text clarifies Maiese's argument about the relationship between cognitive and bodily aspects of emotions and introduces the concept of affective framing as a framework for understanding emotional experiences. [Extracted from the article]

Individuals have different rumination patterns after experiencing traumatic events in different cultural backgrounds and situations. This study aimed to explore the experience of Chinese young and middle-aged patients with acute coronary syndrome (ACS) to understand the content and nature of their rumination. Sixteen participants were selected using the purposive sampling method in the First Affiliated Hospital of Soochow University from May 2023 to December 2023. Data were collected using semi-structured interviews and analyzed using Colaizzi's seven-step phenomenological method. The participants went through two successive stages: non-adaptive rumination and adaptive rumination. During the initial admission phase, all the participants experienced varying degrees of non-adaptive rumination. Non-adaptive rumination included four sub-themes: worry or anxiety of daily activities and medical therapy (37.50%), fear of unpredictable outcomes and death (37.50%), sadness of maladjustment (25.00%), and remorse of carelessness (12.50%). During the period of stable condition and pre-discharge, the participants received health education and gradually all transitioned to adaptive rumination. Adaptive rumination included four sub-themes: tracing of disease processes (100%), enhancement of disease cognition (81.25%), improvement of health awareness (62.50%), and adjustment of lifestyle cognition (100%). In conclusion, although the Chinese young and middle-aged patients with ACS experienced negative emotions after a traumatic cardiac event, they gradually made positive changes, and optimism and information support played important roles in this transition. The results of this study provide a fundamental understanding of rumination experiences in Chinese young and middle-aged patients with ACS and provide new data for healthcare providers when designing intervention programs to enhance post-traumatic growth in these patients. [ABSTRACT FROM AUTHOR]