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The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit's stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 105 mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit. [ABSTRACT FROM AUTHOR]

In a long and narrow corridor, the installation of roof smoke blocking structures is a measure to slow down the spread of fire smoke. When employing multiple smoke blocking structures, the spacing between these structures is a critical parameter that needs to be considered for optimal effectiveness. This paper analyzes the smoke blocking performance of double structures at different spacing and measures the smoke flow velocity both upstream and downstream of the double structures. According to the analysis of the smoke velocity vector obtained from numerical simulation, the smoke can be divided into three zones based on the flow state of the smoke after passing through the front smoke screen structure, namely the vortex zone, surge wave zone, and steady flow zone. When the rear smoke screen is located in the surge zone, the smoke blocking effect is optimal. Analysis of the morphology of the smoke layer indicates that the length of the vortex region is directly proportional to the upstream smoke flow velocity. The numerical and experimental results both indicate that an excessively large or small spacing between the structures fails to achieve optimal smoke control effectiveness. When the spacing is within an optimal range, the smoke velocity is the lowest. Finally, using a real architectural corridor as a case background, this paper presents a design example of roof smoke blocking structures. In order to arrange as many smoke blocking structures as possible, an appropriate spacing between the structures should be slightly larger than the vortex region. The smoke control effectiveness of multiple roof structures was validated through numerical simulation. As a result, the time required for smoke to pass through the corridor increases by 110 s. [ABSTRACT FROM AUTHOR]

Engagement is enhanced by the ability to access the state of flow during a task, which is described as a full immersion experience. We report two studies on the efficacy of using physiological data collected from a wearable sensor for the automated prediction of flow. Study 1 took a two-level block design where activities were nested within its participants. A total of five participants were asked to complete 12 tasks that aligned with their interests while wearing the Empatica E4 sensor. This yielded 60 total tasks across the five participants. In a second study representing daily use of the device, a participant wore the device over the course of 10 unstructured activities over 2 weeks. The efficacy of the features derived from the first study were tested on these data. For the first study, a two-level fixed effects stepwise logistic regression procedure indicated that five features were significant predictors of flow. In total, two were related to skin temperature (median change with respect to the baseline and skewness of the temperature distribution) and three were related to acceleration (the acceleration skewness in the x and y directions and the kurtosis of acceleration in the y direction). Logistic regression and naïve Bayes models provided a strong classification performance (AUC > 0.7, between-participant cross-validation). For the second study, these same features yielded a satisfactory prediction of flow for the new participant wearing the device in an unstructured daily use setting (AUC > 0.7, leave-one-out cross-validation). The features related to acceleration and skin temperature appear to translate well for the tracking of flow in a daily use environment. [ABSTRACT FROM AUTHOR]

The Boltzmann equation for d-dimensional inelastic Maxwell models is considered to determine the collisional moments of the second, third and fourth degree in a granular binary mixture. These collisional moments are exactly evaluated in terms of the velocity moments of the distribution function of each species when diffusion is absent (mass flux of each species vanishes). The corresponding associated eigenvalues as well as cross coefficients are obtained as functions of the coefficients of normal restitution and the parameters of the mixture (masses, diameters and composition). The results are applied to the analysis of the time evolution of the moments (scaled with a thermal speed) in two different nonequilibrium situations: the homogeneous cooling state (HCS) and the uniform (or simple) shear flow (USF) state. In the case of the HCS, in contrast to what happens for simple granular gases, it is demonstrated that the third and fourth degree moments could diverge in time for given values of the parameters of the system. An exhaustive study on the influence of the parameter space of the mixture on the time behavior of these moments is carried out. Then, the time evolution of the second- and third-degree velocity moments in the USF is studied in the tracer limit (namely, when the concentration of one of the species is negligible). As expected, while the second-degree moments are always convergent, the third-degree moments of the tracer species can be also divergent in the long time limit. [ABSTRACT FROM AUTHOR]

Chanting is practiced in many religious and secular traditions and involves rhythmic vocalization or mental repetition of a sound or phrase. This study examined how chanting relates to cognitive function, altered states, and quality of life across a wide range of traditions. A global survey was used to assess experiences during chanting including flow states, mystical experiences, mindfulness, and mind wandering. Further, attributes of chanting were assessed to determine their association with altered states and cognitive benefits, and whether psychological correlates of chanting are associated with quality of life. Responses were analyzed from 456 English speaking participants who regularly chant across 32 countries and various chanting traditions. Results revealed that different aspects of chanting were associated with distinctive experiential outcomes. Stronger intentionality (devotion, intention, sound) and higher chanting engagement (experience, practice duration, regularity) were associated with altered states and cognitive benefits. Participants whose main practice was call and response chanting reported higher scores of mystical experiences. Participants whose main practice was repetitive prayer reported lower mind wandering. Lastly, intentionality and engagement were associated with quality of life indirectly through altered states and cognitive benefits. This research sheds new light on the phenomenology and psychological consequences of chanting across a range of practices and traditions. [ABSTRACT FROM AUTHOR]

Difficulty in video games is an essential factor for a game to be considered engaging and is directly linked to losing in a game. However, for the user to not feel bored or frustrated, it is necessary for the difficulty of the game to be balanced and ideally tailored to the user. This paper presents the design and development of a serious game that adjusts its difficulty based on the user's bio signals, so that it is demanding enough to match his/her skills, in order to enter the flow state. The serious game is accompanied by a server that uses machine learning algorithms to analyze the user's bio signals and classify them into different affective states. These states are later used to adjust the difficulty of the serious game in real-time, without interfering with the user's game experience. Finally, a heuristic evaluation was conducted in order to measure its usability and highlight the good practices and to draw attention to some elements of the game that should be changed in a future version. [ABSTRACT FROM AUTHOR]

This pilot cross-sectional study aimed to evaluate the usability of two new interactive game sensor-based hand devices (GripAble and Smart Sensor Egg) in both healthy adults as well as in persons with Parkinson's Disease (PD). Eight healthy adults and eight persons with PD participated in this study. Besides a standardised usability measure, the state of flow after one training session and the effect of cognitive abilities on flow were evaluated. High system usability scores (SUS) were obtained both in healthy participants (72.5, IQR = 64.375–90, GripAble) as well as persons with PD (77.5, IQR = 70–80.625, GripAble; 77.5, IQR = 75–82.5, Smart Sensor Egg). Similarly, high FSSOT scores were achieved after one training session (42.5, IQR = 39.75–50, GripAble; 50, IQR = 47–50, Smart Sensor Egg; maximum score 55). Across both groups, FSSOT scores correlated significantly with SUS scores (r = 0.52, p = 0.039). Finally, MoCA did not correlate significantly with FSSOT scores (r = 0.02, p = 0.9). The present study shows high usability for both interactive game sensor-based hand training devices, for persons with PD and healthy participants. [ABSTRACT FROM AUTHOR]

Video streaming has become a major usage scenario for the Internet. The growing popularity of new applications, such as 4K and 360-degree videos, mandates that network resources must be carefully apportioned among different users in order to achieve the optimal Quality of Experience (QoE) and fairness objectives. This results in a challenging online optimization problem, as networks grow increasingly complex and the relevant QoE objectives are often nonlinear functions. Recently, data-driven approaches, deep Reinforcement Learning (RL) in particular, have been successfully applied to network optimization problems by modeling them as Markov decision processes. However, existing RL algorithms involving multiple agents fail to address nonlinear objective functions on different agents' rewards. To this end, we leverage MAPG-finite, a policy gradient algorithm designed for multi-agent learning problems with nonlinear objectives. It allows us to optimize bandwidth distributions among multiple agents and to maximize QoE and fairness objectives on video streaming rewards. Implementing the proposed algorithm, we compare the MAPG-finite strategy with a number of baselines, including static, adaptive, and single-agent learning policies. The numerical results show that MAPG-finite significantly outperforms the baseline strategies with respect to different objective functions and in various settings, including both constant and adaptive bitrate videos. Specifically, our MAPG-finite algorithm maximizes QoE by 15.27 % and maximizes fairness by 22.47 % compared to the standard SARSA algorithm for a 2000 KB/s link. [ABSTRACT FROM AUTHOR]

Using a questionnaire applied in real time to students in stages 14–16 during a distance class, the authors appraise whether they experience feelings that lead to a central experience of flow, according to the flow theory of positive psychology. Students are exposed to a planned session that considers the moments of the training sequence and consciously integrates technological tools to support learning. A formal evaluation system, which includes formative and summative evaluations, determines if students build meaningful learning. This research contributes to understanding that an optimal learning experience characterized by the pedagogical principles of curiosity, concentration, challenge, and enjoyment, favor the construction of meaningful learning. Furthermore, the simplicity of the proposed experimental design suggests a direct way to replicate the study in later learning stages and assess the efficiency of new technology-based pedagogies within the distance education paradigm imposed by the 2020 pandemic crisis. [ABSTRACT FROM AUTHOR]

Social media and other web 2.0 tools have provided users with the platform to interact with and also disclose personal information to not only their friends and acquaintances but also relative strangers with unprecedented ease. This has enhanced the ability of people to share more about themselves, their families, and their friends through a variety of media including text, photo, and video, thus developing and sustaining social and business relationships. The purpose of the paper is to identify the factors that predict self-disclosure on social networking sites from the perspective of privacy and flow. Data was collected from 452 students in three leading universities in Ghana and analyzed with Partial Least Square-Structural Equation Modeling. Results from the study revealed that privacy risk was the most significant predictor. We also found privacy awareness, privacy concerns, and privacy invasion experience to be significant predictors of self-disclosure. Interaction and perceived control were found to have significant effect on self-disclosure. In all, the model accounted for 54.6 percent of the variance in self-disclosure. The implications and limitations of the current study are discussed, and directions for future research proposed. [ABSTRACT FROM AUTHOR]