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The flow state is defined by intense involvement in an activity with high degrees of concentration and focused attention accompanied by a sense of pleasure. Video games are effective tools for inducing flow, and keeping players in this state is considered to be one of the central goals of game design. Many studies have focused on the underlying physiological and neural mechanisms of flow. Results are inconsistent when describing a unified mechanism underlying this mental state. This paper provides a comprehensive review of the physiological and neural correlates of flow and explains the relationship between the reported physiological and neural markers of the flow experience. Despite the heterogeneous results, it seems possible to establish associations between reported markers and the cognitive and experiential aspects of flow, particularly regarding arousal, attention control, reward processing, automaticity, and self-referential processing.

The flow state – an experience of complete absorption in an activity – is linked with less self-referential processing and increased arousal. We used the heart-evoked potential (HEP), an index representing brain-heart interaction, as well as indices of peripheral physiology to assess the state of flow in individuals playing a video game. 22 gamers and 21 non-gamers played the video game Thumper for 25 minutes while their brain and cardiorespiratory signals were simultaneously recorded. The more participants were absorbed in the game, the less they thought about time and the faster time passed subjectively. On the cortical level, the fronto-central HEP amplitude was significantly lower while playing the game compared to resting states before and after the game, reflecting less self-referential processing while playing. This HEP effect corresponded with lower activity during gameplay in brain regions contributing to interoceptive processing. The HEP amplitude predicted the level of absorption in the game. While the HEP amplitude was overall lower during the gaming session than during the resting states, within the gaming session the amplitude of HEP was positively associated with absorption. Since higher absorption was related to higher performance in the game, the higher HEP in more absorbed individuals reflects more efficient brain-heart interaction, which is necessary for efficient game play. On the physiological level, a higher level of flow was associated with increased overall sympathetic activity and less inhibited parasympathetic activity towards the end of the game. These results are building blocks for future neurophysiological assessments of flow.

Mangrove forests are among the world’s most productive and carbon-rich ecosystems. Despite growing understanding of factors controlling mangrove forest soil carbon stocks, there is a need to advance understanding of the speed of peat development beneath maturing mangrove forests, especially in created and restored mangrove forests that are intended to compensate for ecosystem functions lost during mangrove forest conversion to other land uses. To better quantify the rate of soil organic matter development beneath created, maturing mangrove forests, we measured ecosystem changes across a 25-yr chronosequence.We compared ecosystem properties in created, maturing mangrove forests to adjacent natural mangrove forests.We also quantified site-specific changes that occurred between 2010 and 2016. Soil organic matter accumulated rapidly beneath maturing mangrove forests as sandy soils transitioned to organic-rich soils (peat). Within 25 yr, a 20-cm deep peat layer developed. The time required for created mangrove forests to reach equivalency with natural mangrove forests was estimated as (1) <15 yr for herbaceous and juvenile vegetation, (2) ~55 yr for adult trees, (3) ~25 yr for the upper soil layer (0–10 cm), and (4) ~45–80 yr for the lower soil layer (10–30 cm). For soil elevation change, the created mangrove forests were equivalent to or surpassed natural mangrove forests within the first 5 yr. A comparison to chronosequence studies from other ecosystems indicates that the rate of soil organic matter accumulation beneath maturing mangrove forests may be among the fastest globally. In most peatland ecosystems, soil organic matter formation occurs slowly (over centuries, millennia); however, these results show that mangrove peat formation can occur within decades. Peat development, primarily due to subsurface root accumulation, enables mangrove forests to sequester carbon, adjust their elevation relative to sea level, and adapt to changing conditions at the dynamic land–ocean interface. In the face of climate change and rising sea levels, coastal managers are increasingly concerned with the longevity and functionality of coastal restoration efforts. Our results advance understanding of the pace of ecosystem development in created, maturing mangrove forests, which can improve predictions of mangrove forest responses to global change and ecosystem restoration.

In this paper, two novel and practical regularizing methods are proposed to improve existing neural network architectures for monocular optical flow estimation. The proposed methods aim to alleviate deficiencies of current methods, such as flow leakage across objects and motion consistency within rigid objects, by exploiting contextual information. More specifically, the first regularization method utilizes semantic information during the training process to explicitly regularize the produced optical flow field. The novelty of this method lies in the use of semantic segmentation masks to teach the network to implicitly identify the semantic edges of an object and better reason on the local motion flow. A novel loss function is introduced that takes into account the objects’ boundaries as derived from the semantic segmentation mask to selectively penalize motion inconsistency within an object. The method is architecture agnostic and can be integrated into any neural network without modifying or adding complexity at inference. The second regularization method adds spatial awareness to the input data of the network in order to improve training stability and efficiency. The coordinates of each pixel are used as an additional feature, breaking the invariance properties of the neural network architecture. The additional features are shown to implicitly regularize the optical flow estimation enforcing a consistent flow, while improving both the performance and the convergence time. Finally, the combination of both regularization methods further improves the performance of existing cutting edge architectures in a complementary way, both quantitatively and qualitatively, on popular flow estimation benchmark datasets.

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

Individual testing of samples is time- and cost-intensive, particularly during an ongoing pandemic. Better practical alternatives to individual testing can significantly decrease the burden of disease on the healthcare system. Herein, we presented the clinical validation of Segtnan™ on 3929 patients. Segtnan™ is available as a mobile application entailing an AI-integrated personalized risk assessment approach with a novel data-driven equation for pooling of biological samples. The AI was selected from a comparison between 15 machine learning classifiers (highest accuracy = 80.14%) and a feed-forward neural network with an accuracy of 81.38% in predicting the rRT-PCR test results based on a designed survey with minimal clinical questions. Furthermore, we derived a novel pool-size equation from the pooling data of 54 published original studies. The results demonstrated testing capacity increase of 750%, 60%, and 5% at prevalence rates of 0.05%, 22%, and 50%, respectively. Compared to Dorfman’s method, our novel equation saved more tests significantly at high prevalence, i.e., 28% ( p  = 0.006), 40% ( p  = 0.00001), and 66% ( p  = 0.02). Lastly, we illustrated the feasibility of the Segtnan™ usage in clinically complex settings like emergency and psychiatric departments.

Latest advances of deep learning paradigm and 3D imaging systems have raised the necessity for more complete datasets that allow exploitation of facial features such as pose, gender or age. In our work, we propose a new facial dataset collected with an innovative RGB–D multi-camera setup whose optimization is presented and validated. 3DWF includes 3D raw and registered data collection for 92 persons from low-cost RGB–D sensing devices to commercial scanners with great accuracy. 3DWF provides a complete dataset with relevant and accurate visual information for different tasks related to facial properties such as face tracking or 3D face reconstruction by means of annotated density normalized 2K clouds and RGB–D streams. In addition, we validate the reliability of our proposal by an original data augmentation method from a massive set of face meshes for facial landmark detection in 2D domain, and by head pose classification through common Machine Learning techniques directed towards proving alignment of collected data.

Media applications are amongst the most demanding services. They require high amounts of network capacity as well as computational resources for synchronous high-quality audio–visual streaming. Recent technological advances in the domain of new generation networks, specifically network virtualization and Multiaccess Edge Computing (MEC) have unlocked the potential of the media industry. They enable high-quality media services through dynamic and efficient resource allocation taking advantage of the flexibility of the layered architecture offered by 5G. The presented work demonstrates the potential application of Artificial Intelligence (AI) capabilities for multimedia services deployment. The goal was targeted to optimize the Quality of Experience (QoE) of real-time video using dynamic predictions by means of Deep Reinforcement Learning (DRL) algorithms. Specifically, it contains the initial design and test of a self-optimized cloud streaming proof-of-concept. The environment is implemented through a virtualized end-to-end architecture for multimedia transmission, capable of adapting streaming bitrate based on a set of actions. A prediction algorithm is trained through different state conditions (QoE, bitrate, encoding quality, and RAM usage) that serves the optimizer as the encoding values of the environment for action prediction. Optimization is applied by selecting the most suitable option from a set of actions. These consist of a collection of predefined network profiles with associated bitrates, which are validated by a list of reward functions. The optimizer is built employing the most prominent algorithms in the DRL family, with the use of two Neural Networks (NN), named Advantage Actor–Critic (A2C). As a result of its application, the ratio of good quality video segments increased from 65% to 90%. Furthermore, the number of image artifacts is reduced compared to standard sessions without applying intelligent optimization. From these achievements, the global QoE obtained is clearly better. These results, based on a simulated scenario, increase the interest in further research on the potential of applying intelligence to enhance the provisioning of media services under real conditions.

The COVID-19 pandemic and associated lockdowns triggered worldwide changes in the daily routines of human experience. The Blursday database provides repeated measures of subjective time and related processes from participants in nine countries tested on 14 questionnaires and 15 behavioural tasks during the COVID-19 pandemic. A total of 2,840 participants completed at least one task, and 439 participants completed all tasks in the first session. The database and all data collection tools are accessible to researchers for studying the effects of social isolation on temporal information processing, time perspective, decision-making, sleep, metacognition, attention, memory, self-perception and mindfulness. Blursday includes quantitative statistics such as sleep patterns, personality traits, psychological well-being and lockdown indices. The database provides quantitative insights on the effects of lockdown (stringency and mobility) and subjective confinement on time perception (duration, passage of time and temporal distances). Perceived isolation affects time perception, and we report an inter-individual central tendency effect in retrospective duration estimation.The Blursday database contains repeated measures of subjective time and related processes collected during the COVID-19 pandemic. The more isolated individuals felt, the slower time seemed to pass.

The TeNDER project aims to improve the quality of life (QoL) of chronic patients through an integrated care ecosystem. This study evaluates the health recommender system (HRS) developed for the project, which offers personalized recommendations based on data collected from a set of monitoring devices. The list of notifications covered different areas of daily life such as physical activity, nutrition, and sleep. We conducted this case study to evaluate the effectiveness and usability of the HRS in providing accurate and relevant recommendations to users. Evaluation process consisted on survey administration for QoL assessment and the satisfaction and usability of the HRS. The four-week pilot study involved several patients and caregivers and demonstrated that the HRS was perceived as user-friendly, consistent, and helpful, with a positive impact on patients’ QoL. However, the study highlights the need for improvement in terms of personalization of recommendations.