The psycho-emotional status of women after radical surgery for breast cancer was assessed. The international questionnaire FACT-G and module FACT-B module were applied to investigate life quality of 186 women under dispensary monitoring concerning breast cancer of stage I-II. The respondents were aged 30-40, 41-50, 51-60, 61-70 and 71 years and older. In women who underwent radical surgery for breast cancer, psycho-emotional status is manifested most vividly as compared with other perceptions in the postoperative period and decreases quality of life. The overwhelming majority of women with breast removal experiences feeling of inferiority and depression, despair, fear of health deterioration and death that increase with age. The percentage of women satisfied with their coping with illness is 64.8% among 30-41 years old, 45.8% among 51-60 years old and 4.2% among 70 years and older. The impact of stress on disease is most felt in women of 51-60 years old (63,6%) and 61-70 years old (58,8%). No reliable differences in level of emotional condition of urban and rural respondents was established. The attitude of women to methods of postoperative correction through formation of breast lost depends on their age, place of residence, level of education. More than 60% of respondents under age of 50 years consider solution of aesthetic problem in endoprosthesis replacement with implants. Among respondents aged 51-60 years, 25.0% preferred simultaneous mastectomy and breast reconstruction and 10.1% - breast plastic surgery. The endoprosthesis replacement is more often resorted by women older 60 years and rural residents. The choice of mode of breast correction often depends on financial possibilities of patients. The study of quality of life of women with post-mastectomy syndrome is valuable component of integrated approach to dispensary monitoring that individualizes rehabilitation measures. The effectiveness of psychological rehabilitation of women requires involvement of clinical psychologists and medical social services.

Facial expression recognition (FER) is crucial for understanding the emotional state of others during human social interactions. It has been assumed that humans share universal visual sampling strategies to achieve this task. However, recent studies in face identification have revealed striking idiosyncratic fixation patterns, questioning the universality of face processing. More importantly, very little is known about whether such idiosyncrasies extend to the biological relevant recognition of static and dynamic facial expressions of emotion (FEEs). To clarify this issue, we tracked observers\' eye movements categorizing static and ecologically valid dynamic faces displaying the six basic FEEs, all normalized for time presentation (1 s), contrast and global luminance across exposure time. We then used robust data-driven analyses combining statistical fixation maps with hidden Markov Models to explore eye-movements across FEEs and stimulus modalities. Our data revealed three spatially and temporally distinct equally occurring face scanning strategies during FER. Crucially, such visual sampling strategies were mostly comparably effective in FER and highly consistent across FEEs and modalities. Our findings show that spatiotemporal idiosyncratic gaze strategies also occur for the biologically relevant recognition of FEEs, further questioning the universality of FER and, more generally, face processing.

OBJECTIVE: This study aimed to examine the developmental relationships between achievement goals and affects in school physical education.
METHODS: The study sample comprised 1063 (girls 546) Finnish Grade 6 students (M = 12.27 years ± .33 at the beginning of the study). The participants in the study were asked to provide annual assessments about their perceptions of achievement goals, enjoyment, and anxiety. The assessments were conducted four times over three consecutive years.
RESULTS: The results indicated that mastery-approach goals were positively associated with enjoyment (between-level), whereas both mastery-avoidance and performance-avoidance goals were positively associated with anxiety (between-level). Additionally, the associations between repeated measures (within-level) of mastery-approach and enjoyment were similar each year, whereas mastery- and performance-approach/avoidance were consistently associated with anxiety within the same year. Girls reported lower performance-approach and enjoyment but higher mastery-avoidance and anxiety than boys.
CONCLUSIONS: The findings suggest that mastery approach goals exhibit a positive correlation with favorable affective reactions, while performance-avoidance goals demonstrate a connection with maladaptive responses. Physical education teachers should endeavor to establish mastery-oriented goals for their students from the outset of the educational process.

OBJECTIVE: The aim of this study was to explore factors that helped when a child with cancer transitioned to end of life care in a hospital setting.
METHODS: Qualitative exploratory design using reflexive thematic analysis.
METHODS: In-depth, semi-structured interviews were carried out with 7 sets of bereaved parents and 10 health professionals from one specialist paediatric oncology centre. Results were shared with professionals to help shape services in a new children\'s hospital.
RESULTS: Three themes were identified: \'change and facing the unknown\', \'the comfort of feeling normal\' and \'knowing and being known\'. Bereaved parents described a gradual awareness of the deterioration of their child\'s condition and the need for trust in health professionals. Professionals described the process as challenging but were guided by the needs of children and parents. Supportive and trusting relationships with professionals helped parents to cope with the transition.
CONCLUSIONS: We identified practices that helped create a culture that supported parents and professionals involved in caring for children facing death from cancer. These were rooted in feeling supported and working to provide the best end of life care for children.
CONCLUSIONS: Given that the death of a child is a uniquely challenging event, this study indicates that the clinical setting can assist via the promotion of familiarity (supporting families over time) and normality (allowing family-focused activities). These were helpful to parents and to professionals. However, professionals need emotional support when working with these families.
UNASSIGNED: The study adhered to the Consolidated Criteria for Reporting Qualitative Research.
UNASSIGNED: The project steering group included one bereaved parent (who was not involved in the study), one consultant paediatric oncologist and one hospital chaplain.

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.

Stress is an important factor affecting human behavior, with recent works in the literature distinguishing it as either productive or destructive. The present study investigated how the primary emotion of stress is correlated with engagement, focus, interest, excitement, and relaxation during university students\' examination processes. Given that examinations are highly stressful processes, twenty-six postgraduate students participated in a four-phase experiment (rest, written examination, oral examination, and rest) conducted at the International Hellenic University (IHU) using a modified Trier protocol. Network analysis with a focus on centralities was employed for data processing. The results highlight the important role of stress in the examination process; correlate stress with other emotions, such as interest, engagement, enthusiasm, relaxation, and concentration; and, finally, suggest ways to control and creatively utilize stress.

Virtual Reality is an effective technique for eliciting emotions. It provides immersive and ecologically valid emotional experiences while maintaining experimental control. Recently, novel VR forms like 360° videos have been used successfully for emotion elicitation. Some preliminary databases of 360° videos for emotion elicitation have been proposed, but they tapped mainly into an emotional dimensional approach and did not include a concurrent physiological assessment of an emotional profile. This study expands on these databases by combining dimensional and discrete approaches to validate a new set of 360° emotion-inducing images. Twenty-six participants viewed 46 immersive images, and their emotional reactions were measured using self-reporting, psychophysiological signals, and eye tracking. The IAVRS database can successfully elicit a wide range of emotional responses, including both positive and negative valence, as well as different levels of arousal. Results reveal an important correspondence between the discrete and dimensional models of emotions. Furthermore, the images that exhibit convergence between the dimensional and discrete emotional models are particularly impactful regarding arousal and valence values. The IAVRS database provides insights into potential relationships between physiological parameters and emotional responses. This preliminary investigation highlights the complexity of emotional elicitation processes and their physiological correlates, suggesting the need for further research to deepen our understanding.

Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals\' stability. Stress is a major emotional state that affects individuals\' capability to perform day-to-day tasks. The main objective of this work is to study the effect of mental and emotional stress on such systems. Two experiments have been performed. In the first, we used hand-crafted features (time domain, frequency domain, and non-linear features), followed by a machine learning classifier. In the second, raw EEG signals were used as an input for the deep learning approaches. Different types of mental and emotional stress have been examined using two datasets, SAM 40 and DEAP. The proposed experiments proved that performing enrollment in a relaxed or calm state and identification in a stressed state have a negative effect on the identification system\'s performance. The best achieved accuracy for the DEAP dataset was 99.67% in the calm state and 96.67% in the stressed state. For the SAM 40 dataset, the best accuracy was 99.67%, 93.33%, 92.5%, and 91.67% for the relaxed state and stress caused by identifying mirror images, the Stroop color-word test, and solving arithmetic operations, respectively.

Exploring the objective signals associated with subjective emotional states has practical significance [...].

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.