Influence of the Curing Temperature in the Mechanical and Thermal Properties of Nanosilica Filled Epoxy Resin Coating

0.5–3 wt% nanosilica was added to an epoxy resin based on diglycidyl ether of bisphenol A (DGEBA) and cured at 25, 40 or 60 °C using isophoronediamine (IPDA) as hardener. Aggregates of nanosilica were properly dispersed into the DGEBA-IPDA resin and agglomerates formation was avoided. Addition of nanosilica increased the storage modulus E′ and the area and height of the tan δ curve of DGEBA-IPDA resin cured at 25 °C, but no significant differences were found by curing at higher temperature. Gel time measurements and the results obtained by applying the Kamal model to isotherm DSC curing of DGEBA-IPDA-nanosilica revealed that nanosilica catalysed the curing reaction between DGEBA and IPDA, in less extent by increasing the curing temperature.     

Level-set techniques for microwave medical imaging

Microwave tomographic imaging is showing significant promise as a new technique for the early detection of breast cancer. Its physical basis is the contrast between the dielectric properties of the healthy breast tissue and the malignant tumors at microwave frequencies. We propose and analyze a novel shape-reconstruction technique for the early detection of breast cancer from microwave data which is based on a level-set technique. The advantages of this method compared to more traditional pixel-based approaches are well-defined boundaries and the incorporation of an intrinsic regularization in form of a-priori assumptions about the general anatomical structure of breast that reduces the dimensionality of the inverse problem and thereby stabilizes the reconstruction. Our goal is not only to detect the tumors but to simultaneously determine their approximate locations, sizes and permittivity values. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Entropies and IPR as Markers for a Phase Transition in a Two-Level Model for Atom–Diatomic Molecule Coexistence

A quantum phase transition (QPT) in a simple model that describes the coexistence of atoms and diatomic molecules is studied. The model, which is briefly discussed, presents a second-order ground state phase transition in the thermodynamic (or large particle number) limit, changing from a molecular condensate in one phase to an equilibrium of diatomic molecules–atoms in coexistence in the other one. The usual markers for this phase transition are the ground state energy and the expected value of the number of atoms (alternatively, the number of molecules) in the ground state. In this work, other markers for the QPT, such as the inverse participation ratio (IPR), and particularly, the Rényi entropy, are analyzed and proposed as QPT markers. Both magnitudes present abrupt changes at the critical point of the QPT.     

Geometry of Lagrangian First-order Classical Field Theories

We construct a lagrangian geometric formulation for first-order field theories using the canonical structures of first-order jet bundles, which are taken as the phase spaces of the systems in consideration. First of all, we construct all the geometric structures associated with a first-order jet bundle and, using them, we develop the lagrangian formalism, defining the canonical forms associated with a lagrangian density and the density of lagrangian energy, obtaining the Euler-Lagrange equations in two equivalent ways: as the result of a variational problem and developing the jet field formalism (which is a formulation more similar to the case of mechanical systems). A statement and proof of Noether's theorem is also given, using the latter formalism.     

Correction to: Accelerating Reactive Transport Modeling: On-Demand Machine Learning Algorithm for Chemical Equilibrium Calculations

Transport in Porous Media - In the original publication of the article, Table 2 was published incorrectly.     

Optimization of STEM-HAADF Electron Tomography Reconstructions by Parameter Selection in Compressed Sensing Total Variation Minimization-Based Algorithms

A novel procedure to optimize the 3D morphological characterization of nanomaterials by means of high angle annular dark field scanning-transmission electron tomography is reported and is successfully applied to the analysis of a metal- and halogen-free ordered mesoporous carbon material. The new method is based on a selection of the two parameters (μ and β) which are key in the reconstruction of tomographic series by means of total variation minimization (TVM). The parameter-selected TVM reconstructions obtained using this approach clearly reveal the porous structure of the carbon-based material as consisting of a network of parallel, straight channels of ≈6 nm diameter ordered in a honeycomb-type arrangement. Such an unusual structure cannot be retrieved from a TVM 3D reconstruction using default reconstruction values. Moreover, segmentation and further quantification of the optimized 3D tomographic reconstruction provide values for different textural parameters, such as pore size distribution and specific pore volume that match very closely with those determined by macroscopic physisorption techniques. The approach developed can be extended to other reconstruction models in which the final result is influenced by parameter choice.     

A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.     

Simulation of the ion beam-plasma interaction processes for point-like ions in doped DT plasmas

The purpose of this work is to study the interaction between an ion beam and a doped deuterium-tritium (DT) plasma in a fast ignition nuclear fusion context. In order to analyze the influence of the dopants in the interaction process, we present a physical model to carry out spatial-temporal simulations of the stopping of an ion beam interacting with a doped plasma target, the plasma heating processes, and the formation of the ignition regions. We perform a set of numerical experiments where different concentrations of dopants are added to a fully ionized DT plasma. These simulations allow us to characterize the increase in the stopping power and the maximum temperatures achieved with the presence of impurities, as well as the reduction of the heated and ignition regions. This reduction in the ignition region indicates difficulties for the formation of an efficient hot spot when there are dopants in the DT plasma.     

Immunomodulatory Properties of Masticadienonic Acid and 3α-Hydroxy Masticadienoic Acid in Dendritic Cells

Dendritic cells are antigen-presenting cells, which identify and process pathogens to subsequently activate specific T lymphocytes. To regulate the immune responses, DCs have to mature by the recognition of TLR ligands, TNFα or IFNγ. These ligands have been used as adjuvants to activate DCs in situ or in vitro, with toxic effects. It has been shown that some molecules affect the immune system, e.g., Masticadienonic acid (MDA) and 3α-hydroxy masticadienoic acid (3α-OH MDA) triterpenes naturally occurring in several medicinal plants, since they activate the nitric oxide synthase in macrophages and induce T lymphocyte proliferation. The DCs maturation induced by MDA or 3a-OH MDA was determined by incubating these cells with MDA or 3α-OH MDA, and their phenotype was afterwards analyzed. The results showed that only 3α-OH MDA was able to induce DCs maturation. When mice with melanoma were inoculated with DCs/3α-OH MDA, a decreased tumor growth rate was observed along with an extended cell death area within tumors compared to mice treated with DCs incubated with MDA. In conclusion, it is proposed that 3α-OH MDA may be an immunostimulant molecule. Conversely, it is proposed that MDA may be a molecule with anti-inflammatory properties.     

Theil Entropy as a Non-Lineal Analysis for Spectral Inequality of Physiological Oscillations

Theil entropy is a statistical measure used in economics to quantify income inequalities. However, it can be applied to any data distribution including biological signals. In this work, we applied different spectral methods on heart rate variability signals and cellular calcium oscillations previously to Theil entropy analysis. The behavior of Theil entropy and its decomposable property was investigated using exponents in the range of [−1, 2], on the spectrum of synthetic and physiological signals. Our results suggest that the best spectral decomposition method to analyze the spectral inequality of physiological oscillations is the Lomb–Scargle method, followed by Theil entropy analysis. Moreover, our results showed that the exponents that provide more information to describe the spectral inequality in the tested signals were zero, one, and two. It was also observed that the intra-band component is the one that contributes the most to total inequality for the studied oscillations. More in detail, we found that in the state of mental stress, the inequality determined by the Theil entropy analysis of heart rate increases with respect to the resting state. Likewise, the same analytical approach shows that cellular calcium oscillations present on developing interneurons display greater inequality distribution when inhibition of a neurotransmitter system is in place. In conclusion, we propose that Theil entropy is useful for analyzing spectral inequality and to explore its origin in physiological signals.