Hormigón conductor con fibras de carbono recicladas para aplicaciones calefactables en mobiliario urbano

This paper presents a broad experimental study performed at laboratory and industrial facilities to develop conductive concrete for self-heating and de-icing applications in urban furniture. Self-heating capacity is achieved by the application of electric current through a highly dense matrix containing recycled carbon fibers and graphite flakes. Prisms and slabs were fabricated with two different conductive concretes and electrode con­figurations to characterize the electrical properties and heating performance. Finally, 3 benches with different electrode disposals were fabricated to assess the heating capacity in real-scale applications. The results presented indicate promising results about the use of recycled carbon fibers for electrothermal concrete applications and identify the electrode configuration that allows the most efficient heat transfer and reduction of temperature gradients within the heated element. Real-scale tests show that the current technology developed is potentially applicable at de-icing applications in climates where temperatures remain within the range of -3 or -5 ºC.Este artículo presenta un extenso trabajo experimental a escala laboratorio e industrial para desarrollar mobiliario urbano con hormigones conductores calefactables. La capacidad calefactable se alcanza mediante la aplicación de corriente eléctrica por una matriz de hormigón con fibras de carbono recicladas y escamas de grafito. Se fabricaron prismas y losas con dos hormigones conductores y distintas configuraciones de electrodos para caracterizar sus propiedades eléctricas y capacidad calefactora. Finalmente, se fabricaron 3 bancos para evaluar la capacidad de calentamiento en aplicaciones a escala real. Los resultados muestran el potencial de las fibras de carbono recicladas para su uso en aplicaciones electrotérmicas e identifican las configuraciones de elec­trodos más adecuadas para reducir los gradientes de temperatura dentro del elemento calefactado. Por último, los ensayos a escala real muestran que la tecnología desarrollada es potencialmente válida para aplicaciones de des-hielo en climas donde la temperatura varía entorno los -3 y -5 ºC

Metastable Coordination Dynamics of Collaborative Creativity in Educational Settings

Educational systems consider fostering creativity and cooperation as two essential aims to nurture future sustainable citizens. The cooperative learning approach proposes different pedagogical strategies for developing creativity in students. In this paper, we conceptualize collaborative creativity under the framework of coordination dynamics and, specifically, we base it on the formation of spontaneous multiscale synergies emerging in complex living systems when interacting with cooperative/competitive environments. This conception of educational agents (students, teachers, institutions) changes the understanding of the teaching/learning process and the traditional roles assigned to each agent. Under such an understanding, the design and co-design of challenging and meaningful learning environments is a key aspect to promote the spontaneous emergence of multiscale functional synergies and teams (of students, students and teachers, teachers, institutions, etc.). According to coordination dynamics, cooperative and competitive processes (within and between systems and their environments) are seen not as opposites but as complementary pairs, needed to develop collaborative creativity and increase the functional diversity potential of teams. Adequate manipulation of environmental and personal constraints, nested in different level and time scales, and the knowledge of their critical (tipping) points are key aspects for an adequate design of learning environments to develop synergistic creativity.This work was supported by the National Institute of Physical Education of Catalonia (INEFC), Generalitat de Catalunya. M.A. is supported by the project “Towards an embodied and transdisciplinary education” granted by the Ministerio de Educación y Formación Profesional of the Spanish government (FPU19/05693). J.A.S.K. is supported by NIMH Grant MH MH080838, the Davimos Family Endowment for Excellence in Science, and the Florida Atlantic University Foundation

dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter

Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft

Hipertensió pulmonar en un malalt jove amb hipertensió portal

La hipertensió pulmonar és una complicació poc freqüent de la hipertensió portal. Presentem un cas d'hipertensió arterial pulmonar severa en un malalt de 28 anys amb el diagnòstic anatomo-patològic d'absència de conductes biliars intrahepàtics i cirrosi hepàtica

Micro-mechanical response of ultrafine grain and nanocrystalline tantalum

In order to investigate the effect of grain boundaries on the mechanical response in the micrometer and submicrometer levels, complementary experiments and molecular dynamics simulations were conducted on a model bcc metal, tantalum. Microscale pillar experiments (diameters of 1 and 2 μm) with a grain size of ~100–200 nm revealed a mechanical response characterized by a yield stress of ~1500 MPa. The hardening of the structure is reflected in the increase in the flow stress to 1700 MPa at a strain of ~0.35. Molecular dynamics simulations were conducted for nanocrystalline tantalum with grain sizes in the range of 20–50 nm and pillar diameters in the same range. The yield stress was approximately 6000 MPa for all specimens and the maximum of the stress–strain curves occurred at a strain of 0.07. Beyond that strain, the material softened because of its inability to store dislocations. The experimental results did not show a significant size dependence of yield stress on pillar diameter (equal to 1 and 2 um), which is attributed to the high ratio between pillar diameter and grain size (~10–20). This behavior is quite different from that in monocrystalline specimens where dislocation ‘starvation’ leads to a significant size dependence of strength. The ultrafine grains exhibit clear ‘pancaking’ upon being plastically deformed, with an increase in dislocation density. The plastic deformation is much more localized for the single crystals than for the nanocrystalline specimens, an observation made in both modeling and experiments. In the molecular dynamics simulations, the ratio of pillar diameter (20–50 nm) to grain size was in the range 0.2–2, and a much greater dependence of yield stress to pillar diameter was observed. A critical result from this work is the demonstration that the important parameter in establishing the overall deformation is the ratio between the grain size and pillar diameter; it governs the deformation mode, as well as surface sources and sinks, which are only important when the grain size is of the same order as the pillar diameter

Micro-mechanical response of ultrafine grain and nanocrystalline tantalum

In order to investigate the effect of grain boundaries on the mechanical response in the micrometer and submicrometer levels, complementary experiments and molecular dynamics simulations were conducted on a model bcc metal, tantalum. Microscale pillar experiments (diameters of 1 and 2 μm) with a grain size of ~100-200 nm revealed a mechanical response characterized by a yield stress of ~1500 MPa. The hardening of the structure is reflected in the increase in the flow stress to 1700 MPa at a strain of ~0.35. Molecular dynamics simulations were conducted for nanocrystalline tantalum with grain sizes in the range of 20-50 nm and pillar diameters in the same range. The yield stress was approximately 6000 MPa for all specimens and the maximum of the stress-strain curves occurred at a strain of 0.07. Beyond that strain, the material softened because of its inability to store dislocations. The experimental results did not show a significant size dependence of yield stress on pillar diameter (equal to 1 and 2 um), which is attributed to the high ratio between pillar diameter and grain size (~10-20). This behavior is quite different from that in monocrystalline specimens where dislocation 'starvation' leads to a significant size dependence of strength. The ultrafine grains exhibit clear 'pancaking' upon being plastically deformed, with an increase in dislocation density. The plastic deformation is much more localized for the single crystals than for the nanocrystalline specimens, an observation made in both modeling and experiments. In the molecular dynamics simulations, the ratio of pillar diameter (20-50 nm) to grain size was in the range 0.2-2, and a much greater dependence of yield stress to pillar diameter was observed. A critical result from this work is the demonstration that the important parameter in establishing the overall deformation is the ratio between the grain size and pillar diameter; it governs the deformation mode, as well as surface sources and sinks, which are only important when the grain size is of the same order as the pillar diameter.Fil: Yang, Wen. University of California at San Diego; Estados UnidosFil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Interdisciplinario de Ciencias Básicas. - Universidad Nacional de Cuyo. Instituto Interdisciplinario de Ciencias Básicas; ArgentinaFil: Li, Zezhou. University of California at San Diego; Estados UnidosFil: Abad, Oscar Torrents. Leibniz Institute for New Materials; AlemaniaFil: Langdon, Terence G.. University of Southern California; Estados UnidosFil: Heiland, Birgit. Leibniz Institute for New Materials; AlemaniaFil: Koch, Marcus. Leibniz Institute for New Materials; AlemaniaFil: Arzt, Eduard. Leibniz Institute for New Materials; Alemania. Universitat Saarland; AlemaniaFil: Meyers, Marc A.. University of California at San Diego; Estados Unido

Towards Whole Placenta Segmentation At Late Gestation Using Multi-View Ultrasound Images

We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.Wellcome Trust IEH Award; EPSRC Centre for Medical Engineering; National Institute for Health Research (NIHR); King’s College London; NHS Foundation Trus

The catalytic subunit of the system L1 amino acid transporter (S<i>lc7a5</i>) facilitates nutrient signalling in mouse skeletal muscle

The System L1-type amino acid transporter mediates transport of large neutral amino acids (LNAA) in many mammalian cell-types. LNAA such as leucine are required for full activation of the mTOR-S6K signalling pathway promoting protein synthesis and cell growth. The SLC7A5 (LAT1) catalytic subunit of high-affinity System L1 functions as a glycoprotein-associated heterodimer with the multifunctional protein SLC3A2 (CD98). We generated a floxed Slc7a5 mouse strain which, when crossed with mice expressing Cre driven by a global promoter, produced Slc7a5 heterozygous knockout (Slc7a5+/-) animals with no overt phenotype, although homozygous global knockout of Slc7a5 was embryonically lethal. Muscle-specific (MCK Cre-mediated) Slc7a5 knockout (MS-Slc7a5-KO) mice were used to study the role of intracellular LNAA delivery by the SLC7A5 transporter for mTOR-S6K pathway activation in skeletal muscle. Activation of muscle mTOR-S6K (Thr389 phosphorylation) in vivo by intraperitoneal leucine injection was blunted in homozygous MS-Slc7a5-KO mice relative to wild-type animals. Dietary intake and growth rate were similar for MS-Slc7a5-KO mice and wild-type littermates fed for 10 weeks (to age 120 days) with diets containing 10%, 20% or 30% of protein. In MS-Slc7a5-KO mice, Leu and Ile concentrations in gastrocnemius muscle were reduced by ∼40% as dietary protein content was reduced from 30 to 10%. These changes were associated with >50% decrease in S6K Thr389 phosphorylation in muscles from MS-Slc7a5-KO mice, indicating reduced mTOR-S6K pathway activation, despite no significant differences in lean tissue mass between groups on the same diet. MS-Slc7a5-KO mice on 30% protein diet exhibited mild insulin resistance (e.g. reduced glucose clearance, larger gonadal adipose depots) relative to control animals. Thus, SLC7A5 modulates LNAA-dependent muscle mTOR-S6K signalling in mice, although it appears non-essential (or is sufficiently compensated by e.g. SLC7A8 (LAT2)) for maintenance of normal muscle mass

Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network.

Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast tumors, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast tumor within a region of interest (ROI) in a mammogram. The generative network learns to recognize the tumor area and to create the binary mask that outlines it. In turn, the adversarial network learns to distinguish between real (ground truth) and synthetic segmentations, thus enforcing the generative network to create binary masks as realistic as possible. The cGAN works well even when the number of training samples are limited. As a consequence, the proposed method outperforms several state-of-the-art approaches. Our working hypothesis is corroborated by diverse segmentation experiments performed on INbreast and a private in-house dataset. The proposed segmentation model, working on an image crop containing the tumor as well as a significant surrounding area of healthy tissue (loose frame ROI), provides a high Dice coefficient and Intersection over Union (IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a Convolutional Neural Network (CNN) is proposed to classify the generated masks into four tumor shapes: irregular, lobular, oval and round. The proposed shape descriptor was trained on DDSM, since it provides shape ground truth (while the other two datasets does not), yielding an overall accuracy of 80%, which outperforms the current state-of-the-art