Emotional Signals Research Articles

Zooming into the Complex Dynamics of Electrodermal Activity Recorded during Emotional Stimuli: A Multiscale Approach.

Physiological phenomena exhibit complex behaviours arising at multiple time scales. To investigate them, techniques derived from chaos theory were applied to physiological signals, providing promising results in distinguishing between healthy and pathological states. Fractal-like properties of electrodermal activity (EDA), a well-validated tool for monitoring the autonomic nervous system state, have been reported in previous literature. This study proposes the multiscale complexity index of electrodermal activity (MComEDA) to discern different autonomic responses based on EDA signals. This method builds upon our previously proposed algorithm, ComEDA, and it is empowered with a coarse-graining procedure to provide a view at multiple time scales of the EDA response. We tested MComEDA's performance on the EDA signals of two publicly available datasets, i.e., the Continuously Annotated Signals of Emotion (CASE) dataset and the Affect, Personality and Mood Research on Individuals and Groups (AMIGOS) dataset, both containing physiological data recorded from healthy participants during the view of ultra-short emotional video clips. Our results highlighted that the values of MComEDA were significantly different (p-value < 0.05 after Wilcoxon signed rank test with Bonferroni's correction) when comparing high- and low-arousal stimuli. Furthermore, MComEDA outperformed the single-scale approach in discriminating among different valence levels of high-arousal stimuli, e.g., showing significantly different values for scary and amusing stimuli (p-value = 0.024). These findings suggest that a multiscale approach to the nonlinear analysis of EDA signals can improve the information gathered on task-specific autonomic response, even when ultra-short time series are considered.

The role of pre-supplementary motor cortex in action control with emotional stimuli: Arepetitive transcranial magnetic stimulationstudy.

Swiftly halting ongoing motor actions is essential to react to unforeseen and potentially perilous circumstances. However, the neural bases subtending the complex interplay between emotions and motor control have been scarcely investigated. Here, we used an emotional stop signal task (SST) to investigate whether specific neural circuits engaged by action suppression are differently modulated by emotional signals with respect to neutral ones. Participants performed an SST before and after the administration of one session of repetitive transcranial magnetic stimulation (rTMS) over the pre-supplementary motor cortex (pre-SMA), the right inferior frontal gyrus (rIFG), and the left primary motor cortex (lM1). Results show that rTMS over the pre-SMA improved the ability to inhibit prepotent action (i.e., better action control) when emotional stimuli were presented. In contrast, action control in a neutral context was fostered by rTMS over the rIFG. No changes were observed after lM1 stimulation. Intriguingly, individuals with higher impulsivity traits exhibited enhanced motor control when facing neutral stimuli following rIFG stimulation. These results further our understanding of the interplay between emotions and motor functions, shedding light on the selective modulation of neural pathways underpinning these processes.

Addressing subjectivity in paralinguistic data labeling for improved classification performance: A case study with Spanish-speaking Mexican children using data balancing and semi-supervised learning

Paralinguistics is an essential component of verbal communication, comprising elements that provide additional information to the language, such as emotional signals. However, the subjective nature of perceiving affective aspects, such as emotions, poses a significant challenge to the development of quality resources for training recognition models of paralinguistic features. Labelers may have different opinions and perceive different emotions from others, making it difficult to achieve a diverse and sufficient representation of considered categories. In this study, we focused on the automatic classification of paralinguistic aspects in Spanish-speaking Mexican children of elementary school age. However, the dataset presents a strong imbalance in all labeled aspects and a low agreement between the labelers. Furthermore, the audio samples were too short, making it challenging to accurately classify affective speech. To address these challenges, we propose a novel method that combines data balancing algorithms and semisupervised learning to improve the classification performance of the trained models. Our method aims to mitigate the subjectivity involved in labeling paralinguistic data, thus advancing the development of robust and accurate recognition models of affective aspects in speech.

Painful Experiences in Social Contexts Facilitate Sensitivity to Emotional Signals of Pain from Conspecifics in Laboratory Rats.

Previous studies demonstrated that laboratory rats could visually receive emotional pain signals from conspecifics through pictorial stimuli. The present study examined whether a prior painful emotional experience of the receiver influenced the sensitivity of emotional expression recognition in laboratory rats. The experiment comprised four phases: the baseline preference test, pain manipulation test, post-manipulation preference test, and state anxiety test. In the baseline phase, the rats explored an apparatus comprising two boxes to which pictures of pain or neutral expressions of other conspecifics were attached. In the pain manipulation phase, each rat was allocated to one of three conditions: foot shock alone (pained-alone; PA), foot shock with other unfamiliar conspecifics (pained-with-other; PWO), or no foot shock (control). In the post-manipulation phase, the animals explored the apparatus in the same manner as they did in the baseline phase. Finally, an open-field test was used to measure state anxiety. These findings indicate that rats in the PWO group stayed longer per entry in a box with photographs depicting a neutral disposition than in a box with photographs depicting pain after manipulation. The results of the open-field test showed no significant differences between the groups, suggesting that the increased sensitivity to pain expression in other individuals due to pain experiences in social settings was not due to increased primary state anxiety. Furthermore, the results indicate that rats may use a combination of self-painful experiences and the states of other conspecifics to process the emotional signal of pain from other conspecifics. In addition, changes in the responses of rats to facial expressions in accordance with social experience suggest that the expression function of rats is not only used for emotional expressions but also for communication.

Non-speech emotion recognition based on back propagation feed forward networks

Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable accuracy of 98.6%. Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper’s focus on leveraging AI algorithms for high-precision non-speech emotion recognition.

Single point motion kinematics convey emotional signals in children and adults.

This study investigates whether humans recognize different emotions conveyed only by the kinematics of a single moving geometrical shape and how this competence unfolds during development, from childhood to adulthood. To this aim, animations in which a shape moved according to happy, fearful, or neutral cartoons were shown, in a forced-choice paradigm, to 7- and 10-year-old children and adults. Accuracy and response times were recorded, and the movement of the mouse while the participants selected a response was tracked. Results showed that 10-year-old children and adults recognize happiness and fear when conveyed solely by different kinematics, with an advantage for fearful stimuli. Fearful stimuli were also accurately identified at 7-year-olds, together with neutral stimuli, while, at this age, the accuracy for happiness was not significantly different than chance. Overall, results demonstrates that emotions can be identified by a single point motion alone during both childhood and adulthood. Moreover, motion contributes in various measures to the comprehension of emotions, with fear recognized earlier in development and more readily even later on, when all emotions are accurately labeled.

Emotional Responses to Music: The Essential Inclusion of Emotion Adaptability and Situational Context

The link between music and emotion, as articulated from a cognitive perspective, assumes that music carries expressive cues that convey or induce emotional responses in listeners. Studies following this paradigm often investigate how responses converge or diverge among individuals, social groups, and cultures. However, results vary from one study to another, with few satisfactory explanations as to why. We contend that emotional responses to music are adaptable, arising from a conscious and subconscious continuous processing of the overarching situational context and its interaction with psychophysical, cultural, and personal variables. By integrating theory and data from multiple domains, we present the Framework for Adaptable Musical Emotions (FAME), which explains emotional responses to music through the mechanism of emotion adaptability on a continuum of evolutionary to fleeting time frames. FAME represents an advance on models of music and emotion that primarily focus on decoding emotional signals from the sounded music. FAME provides the first basis for predictions of emotional adaptability and situational context and may explain previously observed variability in emotional responses to music, guiding future research, and novel understandings.

From emotional signals to symbols.

The quest for the origins of language is a diverse enterprise, where research from a variety of disciplines brings area-specific ideas and area-specific terminology to bear. This variety often results in misunderstandings and misconceptions about communication in various species. In the present paper, we argue for focus on emotional systems as the primary motivators for social signals in animals in general. This focus can help resolve discrepancies of interpretation among different areas of inquiry and can illuminate distinctions among different social signals as well as their phylogenetic origins in animals and especially in humans. We advocate, following Jaak Panksepp, a view wherein the Seeking System, the endogenous tendency to search and explore, is the most fundamental emotional motivation. The Seeking System forms the basis for flexible, voluntary, and exploratory control of motor systems and makes much of learning possible. The relative lack of vocal learning and expression in nonhuman primates contrasted with extensive vocal learning and expression in humans began, we propose, with the evolution in ancient hominins of a necessary foundation for the many subsequent capabilities required for language. That foundation was, according to the reasoning, naturally selected in the form of neurological connections between the Seeking System and mechanisms of glottal/phonatory control. The new connections allowed ancient hominins to develop flexible, endogenous vocal fitness signals produced at very high rates and including large numbers of discrete syllables, recombinable to form syllable combinations with many prosodic variations. The increasing sociality of hominins supported evolution of massive expansion in the utilization of these flexible vocal forms to allow development of words and sentences.

Sexual differences in neuronal and synaptic properties across subregions of the mouse insular cortex

BackgroundThe insular cortex (IC) plays a pivotal role in processing interoceptive and emotional information, offering insights into sex differences in behavior and cognition. The IC comprises two distinct subregions: the anterior insular cortex (aIC), that processes emotional and social signals, and the posterior insular cortex (pIC), specialized in interoception and perception of pain. Pyramidal projection neurons within the IC integrate multimodal sensory inputs, influencing behavior and cognition. Despite previous research focusing on neuronal connectivity and transcriptomics, there has been a gap in understanding pyramidal neurons characteristics across subregions and between sexes.MethodsAdult male and female C57Bl/6J mice were sacrificed and tissue containing the IC was collected for ex vivo slice electrophysiology recordings that examined baseline sex differences in synaptic plasticity and transmission within aIC and pIC subregions.ResultsClear differences emerged between aIC and pIC neurons in both males and females: aIC neurons exhibited distinctive features such as larger size, increased hyperpolarization, and a higher rheobase compared to their pIC counterparts. Furthermore, we observed variations in neuronal excitability linked to sex, with male pIC neurons displaying a greater level of excitability than their female counterparts. We also identified region-specific differences in excitatory and inhibitory synaptic activity and the balance between excitation and inhibition in both male and female mice. Adult females demonstrated greater synaptic strength and maximum response in the aIC compared to the pIC. Lastly, synaptic long-term potentiation occurred in both subregions in males but was specific to the aIC in females.ConclusionsWe conclude that there are sex differences in synaptic plasticity and excitatory transmission in IC subregions, and that distinct properties of IC pyramidal neurons between sexes could contribute to differences in behavior and cognition between males and females.

The impact of clinical context on the recognition of facial expressions

IntroductionSeveral authors have demonstrated the relevance of the therapist sensitivity to the affective expression of his client (Merten &amp; Schwab, 2005; 150-158), as well as to his own emotional experience (Haynal-Raymond et al., 2005;142-148) in order to build a more effective therapeutic relationship, and results. An important source of information to decode the emotional expression hints is the face, and its expression (Ekman &amp; Friesen, 1975; Russel &amp; Fernández-Dolls, 1997;275-294). Despite common sense saying that context is relevant to understand the meaning of the emotional facial expression, the literature review shows inconsistent results.ObjectivesThe main goal of this study was to evaluate the impact of clinical context over the perception of the emotional facial expression.MethodsThis study followed a within-subjects design, and its sample consisted of 60 clinical psychologists. 21 combinations of prototypical expression images with mixed emotional signals, and clinical information texts were presented to the participants. Then their judgement on the type of emotion displayed was requested. The presentation of the text-image pairs was randomized between three conditions: consistent, and non-consistent, and neutral.ResultsThe results suggest that emotions are more easily recognized in the presence of a concordant context than a non-concordant or neutral one, and that the greater the similarity between the facial expression of the image presented and the face prototypically associated with the context, the greater the influence of the context.However, In the recognition of mixed emotional signs, there was greater recognition of signs of anger in the facial expression, as a non-dominant emotion, when in the presence of the neutral story than of the story that agreed with the dominant emotion (sadness). There was also greater recognition of sadness, as a non-dominant emotion, in the presence of a story in agreement with fear than in the presence of a neutral story. There was also a statistically significant increase in the attribution of anger to images in which it is not present and whose dominant emotion is fear, when associated with a context of aggression vs. a neutral context.It was also found that there was a significant decrease in the attribution of fear to the sadness-anger image (25%-75%) in the presence of the aggression context compared to the neutral and panic contexts.There was also a statistically significant decrease in the attribution of sadness to an image of fear in the neutral context compared to the other contexts (panic and aggression).ConclusionsIn conclusion, our study have shown an impact of context over overvaluation or the undervaluation of the emotional facial expression as well as either with prototypical expressions or the mixed emotional signals when referring to sadness, fear, and anger. Thus, mental health clinicians should consider the influence of these contexts.Disclosure of InterestNone Declared

The Caring Machine: Feeling AI for Customer Care

Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI’s emotion understanding, employing “response engineering” to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value.

Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning.

In clinical and scientific research on emotion recognition using physiological signals, selecting the appropriate segment is of utmost importance for enhanced results. In our study, we optimized the electrodermal activity (EDA) segment for an emotion recognition system. Initially, we obtained EDA signals from two publicly available datasets: the Continuously annotated signals of emotion (CASE) and Wearable stress and affect detection (WESAD) for 4-class dimensional and three-class categorical emotional classification, respectively. These signals were pre-processed, and decomposed into phasic signals using the 'convex optimization to EDA' method. Further, the phasic signals were segmented into two equal parts, each subsequently segmented into five nonoverlapping windows. Spectrograms were then generated using short-time Fourier transform and Mel-frequency cepstrum for each window, from which we extracted 85 features. We built four machine learning models for the first part, second part, and whole phasic signals to investigate their performance in emotion recognition. In the CASE dataset, we achieved the highest multi-class accuracy of 62.54% using the whole phasic and 61.75% with the second part phasic signals. Conversely, the WESAD dataset demonstrated superior performance in three-class emotions classification, attaining an accuracy of 96.44% for both whole phasic and second part phasic segments. As a result, the second part of EDA is strongly recommended for optimal outcomes.

Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram.

Recently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition. We proposed a KLGEV-criterion-based microstate analysis method, which can automatically and adaptively identify the optimal number of microstates in emotional EEG. The extracted temporal and spatial microstate features then served as novel feature sets to improve the performance of EEG emotion recognition. We evaluated the proposed method on two publicly available emotional EEG datasets: the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP). For the SEED dataset, 10 microstates were identified using the proposed method. These temporal and spatial features were fed into AutoGluon, an open-source automatic machine learning model, yielding an average three-class accuracy of 70.38% (±8.03%) in subject-dependent emotion recognition. For the DEAP dataset, the method identified 9 microstates. The average accuracy in the arousal dimension was 74.33% (±5.17%) and 75.49% (±5.70%) in the valence dimension, which were competitive performance compared to some previous machine-learning-based studies. Based on these results, we further discussed the neurophysiological relationship between specific microstates and emotions, which broaden our knowledge of the interpretability of EEG microstates. In particular, we found that arousal ratings were positively correlated with the activity of microstate C (anterior regions of default mode network) and negatively correlated with the activity of microstate D (dorsal attention network), while valence ratings were positively correlated with the activity of microstate B (visual network) and negatively correlated with the activity of microstate D (dorsal attention network). In summary, the findings in this paper indicate that the proposed KLGEV-criterion-based method can be employed to research emotional EEG signals effectively, and the microstate features are promising feature sets for EEG-based emotion recognition.

The Role of Emotional Intelligence in Leadership Effectiveness

The aim of this study is to emphasize the role that emotional intelligence plays in leadership. The ability to focus on one's own emotional state as well as the emotional signals that others are displaying in the dynamic context of an interaction is known as emotional intelligence. Keeping an eye on other people's and your own emotions during a heated discussion helps you handle that specific circumstance well. Mutual understanding is the best kind of relationship that people can have, and it happens most quickly when one is still conscious of the dynamics of emotional intelligence. The benefits that emotionally intelligent leaders provide to their followers and organisations will be examined in this study.Emotional skills can be used to manipulate other human beings in different situations. Emotional intelligence and leadership are related to each, the extent to which leadership is impacted by emotional intelligence, with the help of extensive theoretical and empirical review of literature. This paper will help to understand the different perspectives of emotional intelligence along with related components and to study the relationship between emotional intelligence and leadership. Keywords: emotional intelligence, leadership, self-awareness, self-management, social, awareness, relationship management

RLCA: Reinforcement Learning Model Integrating Cognition and Affection for Empathetic Response Generation

Empathy is a crucial field of social science research. The current research on the dialog systems with empathy has two main limitations: 1) less adequate integration of the cognition and affection aspects of empathy for enhancing the perceptual and emotional expression abilities and 2) lack of the evaluation of the generated response at the sentence level in the training process for reducing the problem of exposure bias. Therefore, we proposed the reinforcement learning model integrating cognition and affection (RLCA) model that utilizes the RL framework integrating cognition and affection to evoke greater empathetic expression in the model. In particular, the cognitive response generator can reason commonsense information based on the user’s situation to improve the perceptual capabilities of the proposed model. Moreover, the emotional regulator can mitigate the exposure bias problem by distilling multiple emotion signals from predicted responses and imparting higher emotional intelligence to the proposed model. Furthermore, the interaction between the cognition and affection aspects helps the model to learn the features of empathic expressions in human conversation. Extensive experimental findings on a benchmark dataset indicate that the RLCA outperforms the popular baseline models of automatic metrics and human evaluations while generating more interpretable empathetic responses.

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

The existing EEG emotion classification methods have some problems, such as insufficient emotion representation and lack of targeted channel enhancement module due to feature redundancy. To this end, a novel EEG emotion recognition method (SCLE-2D-CNN) combining scaled convolutional layer (SCLs), enhanced channel module and two-dimensional convolutional neural network (2D-CNN) is proposed. Firstly, the time-frequency features of multi-channel EEG emotional signals were extracted by stacking scl layer by layer. Secondly, channel enhancement module is used to reassign different importance to all EEG physical channels. Finally, 2D-CNN was used to obtain deep local spatiotemporal features and complete emotion classification. The experimental results show that the accuracy of SEED data set and F1 are 98.09% and 97.00%, respectively, and the binary classification accuracy of DEAP data set is 98.06% and 96.83%, respectively, which are superior to other comparison methods. The proposed method has a certain application prospect in the recognition of human mental state.

Amygdalar Excitation of Hippocampal Interneurons Can Lead to Emotion-driven Overgeneralization of Context.

Context is central to cognition: Detailed contextual representations enable flexible adjustment of behavior via comparison of the current situation with prior experience. Emotional experiences can greatly enhance contextual memory. However, sufficiently intense emotional signals can have the opposite effect, leading to weaker or less specific memories. How can emotional signals have such intensity-dependent effects? A plausible mechanistic account has emerged from recent anatomical data on the impact of the amygdala on the hippocampus in primates. In hippocampal CA3, the amygdala formed potent synapses on pyramidal neurons, calretinin (CR) interneurons, as well as parvalbumin (PV) interneurons. CR interneurons are known to disinhibit pyramidal neuron dendrites, whereas PV neurons provide strong perisomatic inhibition. This potentially counterintuitive connectivity, enabling amygdala to both enhance and inhibit CA3 activity, may provide a mechanism that can boost or suppress memory in an intensity-dependent way. To investigate this possibility, we simulated this connectivity pattern in a spiking network model. Our simulations revealed that moderate amygdala input can enrich CA3 representations of context through disinhibition via CR interneurons, but strong amygdalar input can impoverish CA3 activity through simultaneous excitation and feedforward inhibition via PV interneurons. Our model revealed an elegant circuit mechanism that mediates an affective "inverted U" phenomenonv: There is an optimal level of amygdalar input that enriches hippocampal context representations, but on either side of this zone, representations are impoverished. This circuit mechanism helps explain why excessive emotional arousal can disrupt contextual memory and lead to overgeneralization, as seen in severe anxiety and posttraumatic stress disorder.

An Affective EEG Analysis Method Without Feature Engineering

Emotional electroencephalography (EEG) signals are a primary means of recording emotional brain activity.Currently, the most effective methods for analyzing emotional EEG signals involve feature engineering and neuralnetworks. However, neural networks possess a strong ability for automatic feature extraction. Is it possible to discardfeature engineering and directly employ neural networks for end-to-end recognition? Based on the characteristics of EEGsignals, this paper proposes an end-to-end feature extraction and classification method for a dynamic self-attention network(DySAT). The study reveals significant differences in brain activity patterns associated with different emotions acrossvarious experimenters and time periods. The results of this experiment can provide insights into the reasons behind thesedifferences.

Visual cues and consumer's booking intention in P2P accommodation: Exploring the role of social and emotional signals from hosts' profile photos

While previous studies have examined social and emotional value through textual cues, the precise impact of these values conveyed by hosts' facial images, as depicted in their profile photos, on consumer booking intentions is unclear. This study aims to fill this gap by exploring how these values from hosts' facial images affect consumer's booking intentions. To this end, we proposed a deep learning framework to evaluate the social and emotional signals conveyed through hosts' facial images. Then, using data from 44,583 listings in six major US cities, we find a positive correlation between the social signal and consumer's booking intention, while the emotional signal shows a U-shaped relationship. Additionally, the impact of social and emotional signals differs across room type, with non-entire rental consumers more likely to be influenced by social signals and entire room consumers by emotional signals. Our findings emphasize the significance of non-functional values in P2P accommodation.

The Effect of Tone Changes on The Interjection of ‘Aiya’ (哎呀) in Attitude and Emotional Signals

The purpose of this research is to prove that modification of the interjection ‘Aiya’ [哎呀] at certain intonation signals a different attitude. As a result, this study focuses on the different emotional expressions that the Chinese word aiya might convey. Re-searchers employed recording methods with the PRAAT application. Furthermore, the researcher presented 40 respondents who were Mandarin students to give a direct as-sessment of the original and the manipulated interjection. The perception test is carried out by playing five interjection utterances without telling the respondent which sound is the initial utterance. The first sentence has the results of happy emotional attitudes. When it is raised and lowered to 4 semitones, there is a balanced variation of emotions. However, when it was raised to 8 semitones, the test results show an emotional attitude of disappointment, and when lowered to 8 semitones, showed an emotional attitude of impressed. An increase of one tone point can create a positive impression. Then, if there is a decrease, it will make a negative impression. However, because of the vast variation in everyone's perception, generating these conclusions is still impossible. This is where the suggestion appears for future researchers to continue this research and make this research a reference.