280 research outputs found
Textile Taxonomy and Classification Using Pulling and Twisting
Identification of textile properties is an important milestone toward
advanced robotic manipulation tasks that consider interaction with clothing
items such as assisted dressing, laundry folding, automated sewing, textile
recycling and reusing. Despite the abundance of work considering this class of
deformable objects, many open problems remain. These relate to the choice and
modelling of the sensory feedback as well as the control and planning of the
interaction and manipulation strategies. Most importantly, there is no
structured approach for studying and assessing different approaches that may
bridge the gap between the robotics community and textile production industry.
To this end, we outline a textile taxonomy considering fiber types and
production methods, commonly used in textile industry. We devise datasets
according to the taxonomy, and study how robotic actions, such as pulling and
twisting of the textile samples, can be used for the classification. We also
provide important insights from the perspective of visualization and
interpretability of the gathered data
A localização espacial e geográfica de alunos de ensino médio : uma investigação envolvendo o ensino de Astronomia
Trata-se de um estudo com enfoque na orientação espacial e geográfica de alunos de Ensino Médio de uma escola pública brasileira, tomando por base uma atividade didática que envolveu elementos astronômicos. Basicamente, tal atividade tomou como eixo central a marcação da trajetória do Sol, a partir da escola, e estabeleceu relações entre tal trajetória, os lados cardeais e sua localização num mapa do município. A partir de duas entrevistas, que ocorreram no início e no final do trabalho, identificamos as principais formas de se orientar que os alunos empregam, e analisamos a viabilidade de um trabalho desta natureza na construção, pelos alunos, de um sistema de referência mais próximo ao real
Novel Biological Therapies for Severe Asthma Endotypes
Severe asthma comprises several heterogeneous phenotypes, underpinned by complex pathomechanisms known as endotypes. The latter are driven by intercellular networks mediated by molecular components which can be targeted by specific monoclonal antibodies. With regard to the biological treatments of either allergic or non-allergic eosinophilic type 2 asthma, currently available antibodies are directed against immunoglobulins E (IgE), interleukin-5 (IL-5) and its receptor, the receptors of interleukins-4 (IL-4) and 13 (IL-13), as well as thymic stromal lymphopoietin (TSLP) and other alarmins. Among these therapeutic strategies, the best choice should be made according to the phenotypic/endotypic features of each patient with severe asthma, who can thus respond with significant clinical and functional improvements. Conversely, very poor options so far characterize the experimental pipelines referring to the perspective biological management of non-type 2 severe asthma, which thereby needs to be the focus of future thorough research
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
We study the problem of learning graph dynamics of deformable objects which
generalize to unknown physical properties. In particular, we leverage a latent
representation of elastic physical properties of cloth-like deformable objects
which we explore through a pulling interaction. We propose EDO-Net (Elastic
Deformable Object - Net), a model trained in a self-supervised fashion on a
large variety of samples with different elastic properties. EDO-Net jointly
learns an adaptation module, responsible for extracting a latent representation
of the physical properties of the object, and a forward-dynamics module, which
leverages the latent representation to predict future states of cloth-like
objects, represented as graphs. We evaluate EDO-Net both in simulation and real
world, assessing its capabilities of: 1) generalizing to unknown physical
properties of cloth-like deformable objects, 2) transferring the learned
representation to new downstream tasks
Elastic Context: Encoding Elasticity for Data-driven Models of Textiles
Physical interaction with textiles, such as assistive dressing, relies on
advanced dextreous capabilities. The underlying complexity in textile behavior
when being pulled and stretched, is due to both the yarn material properties
and the textile construction technique. Today, there are no commonly adopted
and annotated datasets on which the various interaction or property
identification methods are assessed. One important property that affects the
interaction is material elasticity that results from both the yarn material and
construction technique: these two are intertwined and, if not known a-priori,
almost impossible to identify through sensing commonly available on robotic
platforms. We introduce Elastic Context (EC), a concept that integrates various
properties that affect elastic behavior, to enable a more effective physical
interaction with textiles. The definition of EC relies on stress/strain curves
commonly used in textile engineering, which we reformulated for robotic
applications. We employ EC using Graph Neural Network (GNN) to learn
generalized elastic behaviors of textiles. Furthermore, we explore the effect
the dimension of the EC has on accurate force modeling of non-linear real-world
elastic behaviors, highlighting the challenges of current robotic setups to
sense textile properties
Preliminary Assessment of the Chemical Stability of Dried Extracts from Guazuma ulmifolia Lam. (Sterculiaceae)
We report the results of a preliminary estimation of the stability of the dried extract from bark of Guazuma ulmifolia Lam. (“Mutamba”), with and without added colloidal silicon dioxide (CSD). The physical and chemical properties and the compatibility of CSD in the extract were evaluated for 21 days of storage under stress conditions of temperature (45 ± 2°C) and humidity (75 ± 5%). Thermogravimetry (TG) was supplemented using selective high-performance liquid chromatography (HPLC) for determination of stability of the characteristic constituents (chemical markers), namely, procyanidin B2 (PB2) and epicatechin (EP). The results showed that PB2 is an appropriate compound to be used as a chemical marker in the quality control of dried extracts of G. ulmifolia. The stress study showed that there was no significant difference between the two formulations. However, considering the TG data and the high temperatures involved, the results suggest that CSD increases the stability of the dried extract of G. ulmifolia
Barotrauma during Noninvasive Respiratory Support in COVID-19 Pneumonia Outside ICU: The Ancillary COVIMIX-2 Study
Background: Noninvasive respiratory support (NIRS) has been extensively used during the COVID-19 surge for patients with acute respiratory failure. However, little data are available about barotrauma during NIRS in patients treated outside the intensive care unit (ICU) setting. Methods: COVIMIX-2 was an ancillary analysis of the previous COVIMIX study, a large multicenter observational work investigating the frequencies of barotrauma (i.e., pneumothorax and pneumomediastinum) in adult patients with COVID-19 interstitial pneumonia. Only patients treated with NIRS outside the ICU were considered. Baseline characteristics, clinical and radiological disease severity, type of ventilatory support used, blood tests and mortality were recorded. Results: In all, 179 patients were included, 60 of them with barotrauma. They were older and had lower BMI than controls (p < 0.001 and p = 0.045, respectively). Cases had higher respiratory rates and lower PaO2/FiO2 (p = 0.009 and p < 0.001). The frequency of barotrauma was 0.3% [0.1–1.3%], with older age being a risk factor for barotrauma (OR 1.06, p = 0.015). Alveolar-arterial gradient (A-a) DO2 was protective against barotrauma (OR 0.92 [0.87–0.99], p = 0.026). Barotrauma required active treatment, with drainage, in only a minority of cases. The type of NIRS was not explicitly related to the development of barotrauma. Still, an escalation of respiratory support from conventional oxygen therapy, high flow nasal cannula to noninvasive respiratory mask was predictive for in-hospital death (OR 15.51, p = 0.001). Conclusions: COVIMIX-2 showed a low frequency for barotrauma, around 0.3%. The type of NIRS used seems not to increase this risk. Patients with barotrauma were older, with more severe systemic disease, and showed increased mortality
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