Probing equilibrium glass flow up to exapoise viscosities

Glasses are out-of-equilibrium systems aging under the crystallization threat. During ordinary glass formation, the atomic diffusion slows down rendering its experimental investigation impractically long, to the extent that a timescale divergence is taken for granted by many. We circumvent here these limitations, taking advantage of a wide family of glasses rapidly obtained by physical vapor deposition directly into the solid state, endowed with different "ages" rivaling those reached by standard cooling and waiting for millennia. Isothermally probing the mechanical response of each of these glasses, we infer a correspondence with viscosity along the equilibrium line, up to exapoise values. We find a dependence of the elastic modulus on the glass age, which, traced back to temperature steepness index of the viscosity, tears down one of the cornerstones of several glass transition theories: the dynamical divergence. Critically, our results suggest that the conventional wisdom picture of a glass ceasing to flow at finite temperature could be wrong.Comment: 4 figures and 1 supplementary figur

Nuclear pore complex-mediated modulation of TCR signaling is required for naïve CD4+ T cell homeostasis.

Nuclear pore complexes (NPCs) are channels connecting the nucleus with the cytoplasm. We report that loss of the tissue-specific NPC component Nup210 causes a severe deficit of naïve CD4+ T cells. Nup210-deficient CD4+ T lymphocytes develop normally but fail to survive in the periphery. The decreased survival results from both an impaired ability to transmit tonic T cell receptor (TCR) signals and increased levels of Fas, which sensitize Nup210-/- naïve CD4+ T cells to Fas-mediated cell death. Mechanistically, Nup210 regulates these processes by modulating the expression of Cav2 (encoding Caveolin-2) and Jun at the nuclear periphery. Whereas the TCR-dependent and CD4+ T cell-specific upregulation of Cav2 is critical for proximal TCR signaling, cJun expression is required for STAT3-dependent repression of Fas. Our results uncover an unexpected role for Nup210 as a cell-intrinsic regulator of TCR signaling and T cell homeostasis and expose NPCs as key players in the adaptive immune system

EC2 model applied to the prediction of mechanical properties of soil cement based on test results at early ages

O modelo analítico proposto pelo Eurocódigo 2 (EC2) para a previsão das propriedades mecânicas do betão ao longo do tempo tem mostrado resultados bastante satisfatórios quando adaptado a formulações laboratoriais de Jet Grouting (JG) e de Cutter Soil Mixing (CSM). No entanto, apresenta com principal limitação o facto de estar dependente da realização de ensaios experimentais aos 28 dias de cura para a quantificação das respetivas propriedades, o que limita a sua aplicabilidade em fases mais avançadas do projeto, nomeadamente para fins de controlo de qualidade. No presente artigo o modelo analítico proposto pelo EC2 para a previsão da resistência e rigidez do betão é adaptado a formulações laboratoriais de JG e CSM. Em particular, a abordagem do EC2 é adaptada no sentido de considerar resultados laboratoriais a idades jovens, nomeadamente aos 3, 7 e 14 dias de cura, em substituição dos convencionais 28 dias. Os resultados obtidos mostram que o desempenho do modelo do EC2 aumenta proporcionalmente à idade dos resultados experimentais considerados. Contudo, observou-se também apenas uma ligeira diferença entre o desempenho do modelo do EC2 considerando resultados experimentais aos 14 e aos 28 dias, o que permite fazer um balanceamento entre a precisão do modelo e o tempo/custos totais do projeto.The Eurocode 2 (EC2) approach for strength and stiffness prediction of concrete has been successful adapted to soil-cement laboratory formulations for Jet Grouting (JG) and Cutter Soil Mixing (CSM) technologies. However, its dependence of 28 days test result represents an important limitation. Accordingly, in the present work EC2 approach is modified in order to use laboratory reference data at early ages (e.g. 3, 7 or 14 days) and the achieved results are compared with the conventional 28 days time of cure. As expected, the achieved results show a decrease in EC2 approach performance when reference data at early ages are used. However, it is also observed just a slightly difference in EC2 approach performance when test data at 14 days or 28 days are used. This observation allows us to balance the model prediction accuracy and time consuming in the final project and construction work costs

CHEMICAL COMPOSITION, ANTIOXIDANT AND ANTIMICROBIAL PROPERTIES OF THREE ESSENTIAL OILS FROM PORTUGUESE FLORA

The present work reports on the evaluation of chemical composition and antioxidant and antimicrobial activities of essential oils of three aromatic herbs, growing wild in the south of Portugal, used in traditional food preparations: Foeniculum vulgare, Mentha spicata and Rosmarinus officinalis. The principal components of essential oils were anethole (41.2%) for F. vulgare, carvone (41.1%) for M. spicata and myrcene (23.7%) for R. officinalis. Essential oils showed antioxidant activity either by DPPH radical scavenging method and system β- carotene/acid linoleic method. Antimicrobial activity of essential oils was observed against pathogenic bacteria and yeasts and food spoilage fungi. F.vulgare essential oil showed bacterial activity against Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa, Salmonella enteritidis, Staphylococcus aureus, Aspergillus niger and Fusarium oxysporum with MICs of 0.25-0.75mg/mL. M. spicata oil was active against E.coli, S.aureus, C.albicans, A. niger and F. oxysporum with MICs ranging between 0.25 and 0.75mg/mL. R. officinalis essential oil showed activity against E.coli and C.albicans with MICs of 0.5-1.0mg/mL. Having in account the important antioxidant and antimicrobial properties observed in present work, we consider that these essential oils might be useful on pharmaceutical and food industry as natural antibiotic and food preservativ

Electronic and phononic properties of the chalcopyrite CuGaS2

The availability of ab initio electronic calculations and the concomitant techniques for deriving the corresponding lattice dynamics have been profusely used for calculating thermodynamic and vibrational properties of semiconductors, as well as their dependence on isotopic masses. The latter have been compared with experimental data for elemental and binary semiconductors with different isotopic compositions. Here we present theoretical and experimental data for several vibronic and thermodynamic properties of CuGa2, a canonical ternary semiconductor of the chalcopyrite family. Among these properties are the lattice parameters, the phonon dispersion relations and densities of states (projected on the Cu, Ga, and S constituents), the specific heat and the volume thermal expansion coefficient. The calculations were performed with the ABINIT and VASP codes within the LDA approximation for exchange and correlation and the results are compared with data obtained on samples with the natural isotope composition for Cu, Ga and S, as well as for isotope enriched samples.Comment: 9 pages, 8 Figures, submitted to Phys. Rev

Data-driven models for uniaxial compressive strength prediction applied to unseen data

Data Mining (DM) techniques have been successfully applied to solve a wide range of real-world problems in different real-world domains, particularly in the field of geotechnical civil engineering. A remarkable example is their use in Jet Grouting (JG) technology. Due to the high number of parameters involved and to the heterogeneity of the soil, JG mechanical properties prediction, as well as columns diameter, are complex tasks. Accordingly, the high learning capabilities of DM, namely of the Support Vector Machine (SVM), were applied in the development of new approaches to accurately perform such tasks. This paper aims to assess the SVM model performance trained to predict Uniaxial Compressive Strength (UCS) of JG samples extracted directly from JG columns, when applied to a new set of records collected from a new JG work not contemplated in the database used during the model learning phase. The achieved results highlight the importance of the model domain applicability, as well as the restrictions and recommendations for its generalization when applied to new JG work data not contemplated in the training dataset.The authors wish to thank to Fundacao para a Cienciae a Tecnologia (FCT) for the financial support under the Pos-Doc grant of strategic project PEstOE/ECI/UI4047/2011. Also, the authors would like to thank the interest Tecnasol-FGE company for providing all data needed

Application of data mining techniques in the estimation of the uniaxial compressive strength of jet grouting columns over time

Jet grouting (JG) is a soil treatment technique which is the best solution for several soil improvement problems. However, JG lacks design rules and quality controls. As a result, the main JG works are planned from empirical rules that are too conservative. The development of rational models to simulate the effects of the different parameters involved in the JG process is of primary importance in order to satisfy the binomial safety-economy that is required in any engineering project. In this paper, we present a new approach to predict the uniaxial compressive strength (UCS) of JG materials based on data mining techniques. This model was developed and verified using data from a JG laboratory formulation that involves the measurement of UCS. The results of the proposed approach are compared with the EC2 analytical model adapted to the JG material, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the UCS of JG material and its contributing factors.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/ 45781/2008Tecnasol-FG

A data mining approach for jet grouting uniaxial compressive strength prediction

Jet Grouting (JG) is a Geotechnical Engineering technique that is characterized by a great versatility, being the best solution for several soil treatment improvement problems. However, JG lacks design rules and quality control. As the result, the main JG works are planned from empirical rules that are often too conservative. The development of rational models to simulate the effect of the different parameters involved in the JG process is of primary importance in order to satisfy the binomial safety-economy that is required in any engineering project. In this work, three data mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN), were adapted to predict the Uniaxial Compressive Strength (UCS) of JG laboratory formulations. A comparative study was held, by using a dataset used that was obtained from several studies previously accomplished in University of Minho. We show that the novel data-driven models are able to learn with high accuracy the complex relationships between the UCS of JG laboratory formulations and its contributing factors.Tecnasol-FG