differences in amino acid loss between high efficiency hemodialysis and postdilution and predilution hemodiafiltration using high convection volume exchange a new metabolic scenario a pilot study

Objective The objective of the study was to quantify the loss of total amino acids (TAAs), nonessential amino acids, essential amino acids, and branched chain amino acids (BCAAs) produced by high-efficiency hemodialysis (HEHD), postdilution hemodiafiltration (HDFpost), and predilution hemodiafiltration (HDFpre) using high ultrafiltration volumes; and to define the specific AA losses registered in HEHD, HDFpost, and HDFpre; to identify a potential metabolic and nutritional decline into protein energy wasting; to compare AA analysis of arterial blood samples taken from healthy controls and patients with end-stage renal disease undergoing hemodialysis. Design and Methods Identical dialysis monitors, membranes, and dialysate/infusate were used to homogenize extracorporeal body influence. Ten patients were recruited and randomized to receive treatment with HEHD, HDFpost, and HDFpre it was used on-line dialytic water methodologies (OL); patients' AA arterial concentrations were measured at the start and on completion of dialysis; TAA from the dialyzer filter was calculated, and baseline levels were subsequently compared with findings obtained 1 year later. Finally, the results obtained were compared with the data from a study of 8 healthy volunteers conducted using bioimpedance analysis and laboratory blood tests to assess nutritional status. Results A higher convective dose results in a higher weekly loss of TAA, nonessential AAs, essential AAs, and BCAAs (HEHD: 15.7 g; HDFpost-OL: 16.1 g; HDFpre-OL: 16.3 g, P P Conclusion The study shows that the AA losses in dialytic liquid are greater after high exchange volume HDF techniques, especially HDFpre. The AA losses are not metabolically compensated, so these increase the derangements of predialytic arterial plasma AA levels. Both AA losses and arterial AA perturbations further worsened body composition already after 12 months of additional dialysis

Learning position controls for hybrid step motors: from current-fed to full-order models

The experimental comparison of two different global learning position controls (namely, ‘adaptive learning’ and ‘repetitive learning’ controls) for hybrid step motors performing repetitive tasks has been recently presented in the literature. Related benefits and drawbacks have been successfully analyzed on the same robotic application. However, the design of the two aforementioned learning controls - though relying on a rigorous stability analysis - are based on a simplified current-fed model of the motor. They cannot achieve precise current tracking due to the mere presence of PI control actions in the outer current control loops. The aim of this paper is to enrich and update the results of the above comparison in the light of the latest contributions that generalize the theoretical design to the fullorder voltage-fed motor models of hybrid step motors. Learning actions are now included in the outer current control loops: they generalize the corresponding PI actions to the periodic scenario and allow to solve a control problem whose solution was seeming very difficult to be obtained

Effect of Obstacle Type and Cognitive Task on Situation Awareness and Takeover Performance in Conditionally Automated Driving

In conditionally automated driving, several factors can affect the driver’s situation awareness and ability to take over control. To better understand the influence of some of these factors, 88 participants spent 20 minutes in a conditionally automated driving simulator. They had to react to four obstacles that varied in danger and movement. Half of the participants were required to engage in a verbal cognitive non-driving-related task. Situation awareness, takeover performance and physiological responses were measured for each situation. The results suggest that obstacle movement influences obstacle danger perception, situation awareness, and response time, while the latter is also influenced by obstacle danger. The cognitive verbal task also had an effect on the takeover response time. These results imply that the driver’s cognitive state and the driving situation (e.g. the movement/danger of entities present around the vehicle) must be considered when conveying information to drivers through in-vehicle interfaces

A dataset on the physiological state and behavior of drivers in conditionally automated driving

This dataset contains data of 346 drivers collected during six experiments conducted in a fixed-base driving simulator. Five studies simulated conditionally automated driving (L3 SAE), and the other one simulated manual driving (L0-SAE). The dataset includes physiological data (electrocardiogram (ECG), electrodermal activity (EDA), and respiration (RESP)), driving and behavioral data (reaction time, steering wheel angle, …), performance data of non-driving-related tasks, and questionnaire responses. Among them, measures from standardized questionnaires were collected, either to control the experimental manipulation of the driver's state, or to measure constructs related to human factors and driving safety (drowsiness, mental workload, affective state, situation awareness, situational trust, user experience). In the provided dataset, some raw data have been processed, notably physiological data from which physiological indicators (or features) have been calculated. The latter can be used as input for machine learning models to predict various states (sleep deprivation, high mental workload, ...) that may be critical for driver safety. Subjective self-reported measures can also be used as ground truth to apply regression techniques. Besides that, statistical analyses can be performed using the dataset, in particular to analyze the situational awareness or the takeover quality of drivers, in different states and different driving scenarios. Overall, this dataset contributes to better understanding and consideration of the driver's state and behavior in conditionally automated driving. In addition, this dataset stimulates and inspires research in the fields of physiological/affective computing and human factors in transportation, and allows companies from the automotive industry to better design adapted human-vehicle interfaces for safe use of automated vehicles on the roads

Tâche secondaire et conscience de l'environnement, une application mobile pour véhicule semi-autonome

National audienceAutonomous vehicles are developing rapidly and will lead to a significant change in the driver's role: he/she will have to move from the role of actor to the role of supervisor. Indeed, the driver will soon be able to perform a secondary task but he/she must be able to take over control in the event of a critical situation that is not managed by the autonomous system. This implies that the role of new interfaces and interactions within the vehicle is important to take into account. This article describes the design of an application that provides the driver with information about the environment perceived by his/her vehicle in the form of modules. This application is displayed as split screen on a tablet by which a secondary task can be performed. Initial tests were carried out with this application in a driving simulator. They made it possible to test the acceptance of the application and the clarity of the information transmitted. The results generally showed that the participants correctly identified some of the factors limiting the proper functioning of the autonomous pilot while performing a secondary task on a tablet.Les véhicules autonomes se développent rapidement et entraîneront un changement de rôle important chez le conducteur: ce dernier sera amené à passer du rôle d'acteur à celui de superviseur. En effet, le conducteur sera bientôt en mesure d'effectuer une tâche secondaire mais devra toutefois être capable de reprendre le contrôle dans le cas d'une situation critique non gérée par le système autonome. Ceci implique que le rôle des nouvelles interfaces et interactions au sein du véhicule est important à prendre en compte. Cet article décrit la conception d'une application transmettant au conducteur des informations relatives à l'environnement perçu par son véhicule sous forme de modules. Cette application s'affiche en partage d'écran sur une tablette grâce à laquelle une tâche secondaire peut être effectuée. De premiers tests ont été effectués avec cette application dans un simulateur de conduite. Ils ont permis de tester l'acceptation de l'application et la clarté des informations transmises. Les résultats ont globalement montré que les participants ont correctement identifié certains facteurs limitant le bon fonctionnement du pilote autonome tout en réalisant une tâche secondaire sur tablette

L'evento

Il concetto di evento \ue8 di grande rilievo per gli studiosi delle scienze storiche, anche se non \ue8 tra i pi\uf9 indagati, essendo stato, per lo pi\uf9 oggetto di interesse per i filosofi e per i metodologi delle scienze sociali. Da vari angoli disciplinari e culturali si propone una nuova tematizzazione del concetto a pi\uf9 voci, accompagnata dalla traduzione italiana di un brevissimo trattato di Plutarco di Cherone

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987