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Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants' brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.

IntroductionEvaluating the body composition and dietary habits of non-professional athletes can help identify areas for improvement to enhance sports performance. The present study aimed to describe the anthropometric and body composition features, as well as the dietary habits, of non-professional rugby players in Argentina.MethodsFifty-seven rugby players from a Group III Club of the Unión de Rugby de Buenos Aires (URBA) were assessed using extensive anthropometric measurements according to the International Society for the Advancement of Kinanthropometry (ISAK) protocol. Reference data from professional rugby players in Group I clubs were used as a control for body composition comparisons. Dietary intake was evaluated using the 24-h recall method, and nutrient analysis was performed with SARA software.ResultsNon-professional rugby players were shorter (Forwards: 175.9 vs. 181.5 cm; Backs: 172.5 vs. 175.7 cm), had higher body fat percentages (Forwards: 16.4 vs. 12.3%; Backs: 11.0 vs. 9.3%), and were less muscular (Forwards: 46.0 vs. 48.8%; Backs: 48.4 vs. 50.2%) compared to professional rugby players. The average dietary intake was 3,363 Kcal, with protein and carbohydrate intakes of 1.4 g kg−1 day−1 and 4.1 g kg−1 day−1, respectively, and 35% of energy intake from fat. Backs reported a higher caloric intake than forwards (3,682 vs. 2,827 Kcal). There was a high prevalence of insufficient intake of calcium (58%), vitamin A (49%), and vitamin C (65%), the latter two corresponding with a low intake of fruits and vegetables (6% of total energy intake). Meal pattern analysis showed that 46% of total energy was ingested at dinner.ConclusionsThe body composition of non-professional rugby players from low-income clubs could be improved to enhance rugby performance, as compared to players in more competitive tiers. Economic constraints might contribute to a sub-optimal nutritional profile, potentially affecting body composition and on-field performance negatively. Recommendations to improve dietary intake should be made considering the budget constraints of these players.

We investigate the diffusion phenomenon of particles in the vicinity of a spherical colloidal particle where particles may be adsorbed/desorbed and react on the surface of the colloidal particle. The mathematical model comprises a generalized diffusion equation to govern bulk dynamics and kinetic equations which can describe non-Debye relaxations and is used for the colloid’s surface. For the reaction processes, we also consider the presence of convolution kernels, which offer the flexibility of describing a single process or process with intermediate reactions before forming the final species. Our analysis focuses on analytical and numerical calculations to obtain the particles’ behavior on the colloidal particle’s surface and to determine how it affects the diffusion of particles around it. The solutions obtained show various behaviors that can be connected to anomalous diffusion phenomena and may be used to describe the ever-richer science of colloidal particles better.

We investigate the H-theorem for a class of generalized kinetic equations with fractional time-derivative, hyperbolic term, and nonlinear diffusion. When the H-theorem is satisfied, we demonstrate that different entropic forms may emerge due to the equation’s nonlinearity. We obtain the entropy production related to these entropies and show that its form remains invariant. Furthermore, we investigate some behaviors for these equations from both numerical and analytical perspectives, showing a large class of behaviors connected with anomalous diffusion and their effects on entropy.

Abstract Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants’ brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.

This note has the purpose of calling the attention of the chemist to some aspects of the process of translating chemical terms onto the Portuguese language. A quick inspection on the chemistry literature in Portuguese shows a certain number of inadequate or dubious translations of terms originally coined in other languages. Firstly, the possible origins of these inadequacies are briefly discussed. Then one proceeds to a cursory explanation of some fundamental concepts such as signs, words, meaning, real referents, scientific concept, and scientific term. These concepts are traced back to the origin of Western Civilization, being first systematized in the work of Aristotle, forming the backbone of all Western languages. A short discussion on the features of a well-done translation is presented. Finally, a set of ill-translated or dubiously translated or dubiously named chemical terms are used to illustrate the presented concepts, divided among the general type of inadequacy. In some cases, with justification, grammatically correct or more adequate terms are suggested to replace the offending translated chemical terms in portuguese.

In this study, we investigate a nonlinear diffusion process in which particles stochastically reset to their initial positions at a constant rate. The nonlinear diffusion process is modeled using the porous media equation and its extensions, which are nonlinear diffusion equations. We use analytical and numerical calculations to obtain and interpret the probability distribution of the position of the particles and the mean square displacement. These results are further compared and shown to agree with the results of numerical simulations. Our findings show that a system of this kind exhibits non-Gaussian distributions, transient anomalous diffusion (subdiffusion and superdiffusion), and stationary states that simultaneously depend on the nonlinearity and resetting rate.

Abstract The emergence of digital pathology environments and the application of computer vision to the analysis of histological sections has given rise to a new area of Anatomical Pathology, termed Computational Pathology. Advances in Computational Pathology may substantially change the routine of Anatomical Pathology laboratories and the work profile of the pathologist.

Simulations of a fluidised bed reactor for gasification of municipal solid refuse-derived fuel were performed using OpenFOAM software. Firstly, evaluation was made of a simplified gas-solid two-phase model, considering sand and air as the components, according to a transient Eulerian-Eulerian approach. A scale-up study was also performed to obtain thermal-fluid dynamic parameters. Then, a real dimensions non-reacting model was developed, based on the experimental information from a semi-industrial gasification plant with capacity for processing 7.1 t day−1 of municipal refuse-derived fuel, producing 16.9 t day−1 of syngas. The fluidising regime was mapped for different inlet conditions, at 1,123 K, with air velocities ranging from 0.01 to 1.25 m s−1, and the continuous operation of the reactor was analysed, where in the solid particles packing remained at approximately 88% from maximum, with bed height of 2.05 m. The results were in good agreement with data available in the scientific literature, and the computational model was able to provide consistent results when compared to the experimental information for the semi-industrial reactor. The authors’ major remark was the hability of this computational model in obtaining consistent results from simulations of the semi-industrial scale reactor, with good prediction of the internal fluid dynamics characteristics.

We investigate the diffusion phenomenon of particles in the vicinity of a spherical colloidal particle where particles may be adsorbed/desorbed and react on the surface of the colloidal particle. The mathematical model comprises a generalized diffusion equation to govern bulk dynamics and kinetic equations which can describe non-Debye relaxations and is used for the colloid's surface. For the reaction processes, we also consider the presence of convolution kernels, which offer the flexibility of describing a single process or process with intermediate reactions before forming the final species. Our analysis focuses on analytical and numerical calculations to obtain the particles' behavior on the colloidal particle's surface and to determine how it affects the diffusion of particles around it. The solutions obtained show various behaviors that can be connected to anomalous diffusion phenomena and may be used to describe the ever-richer science of colloidal particles better. [ABSTRACT FROM AUTHOR]

We study the entropy production in a fractal system composed of two subsystems, each of which is subjected to an external force. This is achieved by using the H-theorem on the nonlinear Fokker–Planck equations (NFEs) characterizing the diffusing dynamics of each subsystem. In particular, we write a general NFE in terms of Hausdorff derivatives to take into account the metric of each system. We have also investigated some solutions from the analytical and numerical point of view. We demonstrate that each subsystem affects the total entropy and how the diffusive process is anomalous when the fractal nature of the system is considered.

We investigate the H-theorem for a class of generalized kinetic equations with fractional time-derivative, hyperbolic term, and nonlinear diffusion. When the H-theorem is satisfied, we demonstrate that different entropic forms may emerge due to the equation's nonlinearity. We obtain the entropy production related to these entropies and show that its form remains invariant. Furthermore, we investigate some behaviors for these equations from both numerical and analytical perspectives, showing a large class of behaviors connected with anomalous diffusion and their effects on entropy. [ABSTRACT FROM AUTHOR]

Introduction: Evaluating the body composition and dietary habits of nonprofessional athletes can help identify areas for improvement to enhance sports performance. The present study aimed to describe the anthropometric and body composition features, as well as the dietary habits, of non-professional rugby players in Argentina. Methods: Fifty-seven rugby players froma Group III Club of the Unión de Rugby de Buenos Aires (URBA)were assessed using extensive anthropometricmeasurements according to the International Society for the Advancement of Kinanthropometry (ISAK) protocol. Reference data from professional rugby players in Group I clubs were used as a control for body composition comparisons. Dietary intake was evaluated using the 24-h recall method, and nutrient analysis was performed with SARA software. Results: Non-professional rugby players were shorter (Forwards: 175.9 vs. 181.5 cm; Backs: 172.5 vs. 175.7 cm), had higher body fat percentages (Forwards: 16.4 vs. 12.3%; Backs: 11.0 vs. 9.3%), andwere lessmuscular (Forwards:46.0vs.48.8%;Backs: 48.4 vs. 50.2%) compared to professional rugby players. The average dietary intake was 3,363 Kcal, with protein and carbohydrate intakes of 1.4 g kg1 day-1 and 4.1 g kg-1 day-1, respectively, and 35% of energy intake from fat. Backs reported a higher caloric intake than forwards (3,682 vs. 2,827 Kcal). There was a high prevalence of insufficient intake of calcium (58%), vitamin A (49%), and vitamin C (65%), the latter two corresponding with a low intake of fruits and vegetables (6% of total energy intake). Meal pattern analysis showed that 46% of total energy was ingested at dinner. Conclusions: The body composition of non-professional rugby players from lowincome clubs could be improved to enhance rugby performance, as compared to players in more competitive tiers. Economic constraints might contribute to a sub-optimal nutritional profile, potentially affecting body composition and on- field performance negatively. Recommendations to improve dietary intake should be made considering the budget constraints of these players. [ABSTRACT FROM AUTHOR]

In the Central Andean region in South America, high-altitude ecosystems (3500–6000 masl) are distributed across Argentina, Chile, Bolivia, and Peru, in which poly-extremophilic microbes thrive under extreme environmental conditions. In particular, in the Puna region, total solar irradiation and UV incidence are the highest on Earth, thus, restraining the physiology of individual microorganisms and the composition of microbial communities. UV-resistance of microbial strains thriving in High-Altitude Andean Lakes was demonstrated and their mechanisms were partially characterized by genomic analysis, biochemical and physiological assays. Then, the existence of a network of physiological and molecular mechanisms triggered by ultraviolet light exposure was hypothesized and called “UV-resistome”. It includes some or all of the following subsystems: (i) UV sensing and effective response regulators, (ii) UV-avoidance and shielding strategies, (iii) damage tolerance and oxidative stress response, (iv) energy management and metabolic resetting, and (v) DNA damage repair. Genes involved in the described UV-resistome were recently described in the genome of Nesterenkonia sp. Act20, an actinobacterium which showed survival to high UV-B doses as well as efficient photorepairing capability. The aim of this work was to use a proteomic approach together with photoproduct measurements to help dissecting the molecular events involved in the adaptive response of a model High-Altitude Andean Lakes (HAAL) extremophilic actinobacterium, Nesterenkonia sp. Act20, under artificial UV-B radiation. Our results demonstrate that UV-B exposure induced over-abundance of a well-defined set of proteins while recovery treatments restored the proteomic profiles present before the UV-challenge. The proteins involved in this complex molecular network were categorized within the UV-resistome subsystems: damage tolerance and oxidative stress response, energy management and metabolic resetting, and DNA damage repair.

Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

BackgroundMental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. ObjectiveThis article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective. MethodsWe performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends. ResultsA total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal. ConclusionsThis systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

We investigate the solutions of a generalized diffusion-like equation by considering a spatial and time fractional derivative and the presence of non-local terms, which can be related to reaction or adsorption–desorption processes. We use the Green function approach to obtain solutions and evaluate the spreading of the system to show a rich class of behaviors. We also connect the results obtained with the anomalous diffusion processes.

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.