Wake-Driven Dynamics of Finite-Sized Buoyant Spheres in Turbulence

Particles suspended in turbulent flows are affected by the turbulence and at the same time act back on the flow. The resulting coupling can give rise to rich variability in their dynamics. Here we report experimental results from an investigation of finite-sized buoyant spheres in turbulence. We find that even a marginal reduction in the particle's density from that of the fluid can result in strong modification of its dynamics. In contrast to classical spatial filtering arguments and predictions of particle models, we find that the particle acceleration variance increases with size. We trace this reversed trend back to the growing contribution from wake-induced forces, unaccounted for in current particle models in turbulence. Our findings highlight the need for improved multi-physics based models that account for particle wake effects for a faithful representation of buoyant-sphere dynamics in turbulence.Comment: 5 pages, 4 figures, http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.115.12450

Links between dissipation, intermittency, and helicity in the GOY model revisited

High-resolution simulations within the GOY shell model are used to study various scaling relations for turbulence. A power-law relation between the second-order intermittency correction and the crossover from the inertial to the dissipation range is confirmed. Evidence is found for the intermediate viscous dissipation range proposed by Frisch and Vergassola. It is emphasized that insufficient dissipation-range resolution systematically drives the energy spectrum towards statistical-mechanical equipartition. In fully resolved simulations the inertial-range scaling exponents depend on both model parameters; in particular, there is no evidence that the conservation of a helicity-like quantity leads to universal exponents.Comment: 24 pages, 13 figures; submitted to Physica

Novel metrics and methodology for the characterisation of 3D imaging systems

© 2016 The AuthorsThe modelling, benchmarking and selection process for non-contact 3D imaging systems relies on the ability to characterise their performance. Characterisation methods that require optically compliant artefacts such as matt white spheres or planes, fail to reveal the performance limitations of a 3D sensor as would be encountered when measuring a real world object with problematic surface finish. This paper reports a method of evaluating the performance of 3D imaging systems on surfaces of arbitrary isotropic surface finish, position and orientation. The method involves capturing point clouds from a set of samples in a range of surface orientations and distances from the sensor. Point clouds are processed to create a single performance chart per surface finish, which shows both if a point is likely to be recovered, and the expected point noise as a function of surface orientation and distance from the sensor. In this paper, the method is demonstrated by utilising a low cost pan-tilt table and an active stereo 3D camera. Its performance is characterised by the fraction and quality of recovered data points on aluminium isotropic surfaces ranging in roughness average (Ra) from 0.09 to 0.46 µm at angles of up to 55° relative to the sensor over a distances from 400 to 800 mm to the scanner. Results from a matt white surface similar to those used in previous characterisation methods contrast drastically with results from even the dullest aluminium sample tested, demonstrating the need to characterise sensors by their limitations, not just best case performance

Self-propelling Microdroplets Generated and Sustained by Liquid-liquid Phase Separation in Confined Spaces

Flow transport in confined spaces is ubiquitous in technological processes, ranging from separation and purification of pharmaceutical ingredients by microporous membranes and drug delivery in biomedical treatment to chemical and biomass conversion in catalyst-packed reactors and carbon dioxide sequestration. In this work, we suggest a distinct pathway for enhanced liquid transport in a confined space via self-propelling microdroplets. These microdroplets can form spontaneously from localized liquid-liquid phase separation as a ternary mixture is diluted by a diffusing poor solvent. High speed images reveal how the microdroplets grow, break up and propel rapidly along the solid surface, with a maximal velocity up to ~160 um/s, in response to a sharp concentration gradient resulting from phase separation. The microdroplet self-propulsion induces a replenishing flow between the walls of the confined space towards the location of phase separation, which in turn drives the mixture out of equilibrium and leads to a repeating cascade of events. Our findings on the complex and rich phenomena of self-propelling droplets suggest an effective approach to enhanced flow motion of multicomponent liquid mixtures within confined spaces for time effective separation and smart transport processes.Comment: This is the authors' submitted version of the manuscrip

To err is robot: How humans assess and act toward an erroneous social robot

© 2017 Mirnig, Stollnberger, Miksch, Stadler, Giuliani and Tscheligi. We conducted a user study for which we purposefully programmed faulty behavior into a robot's routine. It was our aim to explore if participants rate the faulty robot different from an error-free robot and which reactions people show in interaction with a faulty robot. The study was based on our previous research on robot errors where we detected typical error situations and the resulting social signals of our participants during social human-robot interaction. In contrast to our previous work, where we studied video material in which robot errors occurred unintentionally, in the herein reported user study, we purposefully elicited robot errors to further explore the human interaction partners' social signals following a robot error. Our participants interacted with a human-like NAO, and the robot either performed faulty or free from error. First, the robot asked the participants a set of predefined questions and then it asked them to complete a couple of LEGO building tasks. After the interaction, we asked the participants to rate the robot's anthropomorphism, likability, and perceived intelligence. We also interviewed the participants on their opinion about the interaction. Additionally, we video-coded the social signals the participants showed during their interaction with the robot as well as the answers they provided the robot with. Our results show that participants liked the faulty robot significantly better than the robot that interacted flawlessly. We did not find significant differences in people's ratings of the robot's anthropomorphism and perceived intelligence. The qualitative data confirmed the questionnaire results in showing that although the participants recognized the robot's mistakes, they did not necessarily reject the erroneous robot. The annotations of the video data further showed that gaze shifts (e.g., from an object to the robot or vice versa) and laughter are typical reactions to unexpected robot behavior. In contrast to existing research, we assess dimensions of user experience that have not been considered so far and we analyze the reactions users express when a robot makes a mistake. Our results show that decoding a human's social signals can help the robot understand that there is an error and subsequently react accordingly

Fluorescence changes reveal kinetic steps of muscarinic receptor–mediated modulation of phosphoinositides and Kv7.2/7.3 K+ channels

G protein–coupled receptors initiate signaling cascades. M1 muscarinic receptor (M1R) activation couples through Gαq to stimulate phospholipase C (PLC), which cleaves phosphatidylinositol 4,5-bisphosphate (PIP2). Depletion of PIP2 closes PIP2-requiring Kv7.2/7.3 potassium channels (M current), thereby increasing neuronal excitability. This modulation of M current is relatively slow (6.4 s to reach within 1/e of the steady-state value). To identify the rate-limiting steps, we investigated the kinetics of each step using pairwise optical interactions likely to represent fluorescence resonance energy transfer for M1R activation, M1R/Gβ interaction, Gαq/Gβ separation, Gαq/PLC interaction, and PIP2 hydrolysis. Electrophysiology was used to monitor channel closure. Time constants for M1R activation (<100 ms) and M1R/Gβ interaction (200 ms) are both fast, suggesting that neither of them is rate limiting during muscarinic suppression of M current. Gαq/Gβ separation and Gαq/PLC interaction have intermediate 1/e times (2.9 and 1.7 s, respectively), and PIP2 hydrolysis (6.7 s) occurs on the timescale of M current suppression. Overexpression of PLC accelerates the rate of M current suppression threefold (to 2.0 s) to become nearly contemporaneous with Gαq/PLC interaction. Evidently, channel release of PIP2 and closure are rapid, and the availability of active PLC limits the rate of M current suppression

Lingue slave e balcaniche fra Sprachbund e contatti linguistici: aspetti metodologici

The paper is a state-of-the-art presentation of Balkan linguistics and offers a discussion of some potential directions for its future development, such as the need for a careful investigation of the micro-parametric variation with respect to those particular phenomena that characterize best the dialectal continuum represented by the Balkan languages and dialects

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

Glucocorticoids—All-Rounders Tackling the Versatile Players of the Immune System

Glucocorticoids regulate fundamental processes of the human body and control cellular functions such as cell metabolism, growth, differentiation, and apoptosis. Moreover, endogenous glucocorticoids link the endocrine and immune system and ensure the correct function of inflammatory events during tissue repair, regeneration, and pathogen elimination via genomic and rapid non-genomic pathways. Due to their strong immunosuppressive, anti-inflammatory and anti-allergic effects on immune cells, tissues and organs, glucocorticoids significantly improve the quality of life of many patients suffering from diseases caused by a dysregulated immune system. Despite the multitude and seriousness of glucocorticoid-related adverse events including diabetes mellitus, osteoporosis and infections, these agents remain indispensable, representing the most powerful, and cost-effective drugs in the treatment of a wide range of rheumatic diseases. These include rheumatoid arthritis, vasculitis, and connective tissue diseases, as well as many other pathological conditions of the immune system. Depending on the therapeutically affected cell type, glucocorticoid actions strongly vary among different diseases. While immune responses always represent complex reactions involving different cells and cellular processes, specific immune cell populations with key responsibilities driving the pathological mechanisms can be identified for certain autoimmune diseases. In this review, we will focus on the mechanisms of action of glucocorticoids on various leukocyte populations, exemplarily portraying different autoimmune diseases as heterogeneous targets of glucocorticoid actions: (i) Abnormalities in the innate immune response play a crucial role in the initiation and perpetuation of giant cell arteritis (GCA). (ii) Specific types of CD4+ T helper (Th) lymphocytes, namely Th1 and Th17 cells, represent important players in the establishment and course of rheumatoid arthritis (RA), whereas (iii) B cells have emerged as central players in systemic lupus erythematosus (SLE). (iv) Allergic reactions are mainly triggered by several different cytokines released by activated Th2 lymphocytes. Using these examples, we aim to illustrate the versatile modulating effects of glucocorticoids on the immune system. In contrast, in the treatment of lymphoproliferative disorders the pro-apoptotic action of glucocorticoids prevails, but their mechanisms differ depending on the type of cancer. Therefore, we will also give a brief insight into the current knowledge of the mode of glucocorticoid action in oncological treatment focusing on leukemia