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