Morphology and thermal conductivity of model organic aerogels

The intersection volume of two independent 2-level cut Gaussian random fields is proposed to model the open-cell microstructure of organic aerogels. The experimentally measured X-ray scattering intensity, surface area and solid thermal conductivity of both polymeric and colloidal organic aerogels can be accounted for by the model.Comment: 5 pages. RevTex with 4 encapsulated figures. Higher resolution figures have been submitted for publication. To be published in Phys. Rev. E (Rapid Comm.). email, [email protected]

What affects the freezing behaviors of cement-based porous materials: The role of the unfrozen liquid-like layer

A key factor that affects freeze-thaw damages of cement-based porous materials (CBPMs) is the amount of the freezable water confined in the pores that generate large internal pressures during freezing. Taking account of an unfrozen liquid-like layer (ULLL) between ice crystals and pore wall, this paper investigates deformations of a saturated CBPM specimen under freezing with different thickness values of the ULLL. To bridge the macro strains and the local pressure exerted on the pore wall of the material, the thermodynamic equilibrium between the water and ice, and a poroelastic approach were adopted. The hydraulic pressure by volume change as phase transition takes place in the pores, the fusion pressure by energy change as ice forms and penetrates through the thin pores and the hydrothermal pressure by TEC discrepancies between the pore fluids and solid substrate dominate the internal freezing stress. The obtained results reveal that the ULLL plays an important role on the estimation of the amount of ice crystals confined in the pores, and thus influences the pore pressures and deformations of the CBPM specimen used. Appropriate model of the ULLL helps to decrease the deviations between the predicted strains and the experimental data

Space-Based Analysis of the Cloud Thermodynamic Phase Transition for Varying Microphysical and Meteorological Regimes

International audiencePhase transitions leading to cloud glaciation occur at temperatures that vary between -38°C and 0°C depending on aerosol types and concentrations, the meteorology, and cloud microphysical and macrophysical parameters, although the relationships remain poorly understood. Here, we statistically retrieve a cloud glaciation temperature from two passive space-based instruments that are part of the NASA/CNES A-Train, the POLarization and Directionality of the Earth's Reflectances (POLDER) and the MODerate resolution Imaging Spectroradiometer (MODIS). We compare the glaciation temperature for varying bins of cloud droplet effective radius, latitude, and large-scale vertical pressure velocity and specific humidity at 700 hPa. Cloud droplet size has the strongest influence on glaciation temperature: For cloud droplets larger than 21 μm, the glaciation temperature is 6°C higher than for cloud droplets smaller than 9 μm. Stronger updrafts are also associated with lower glaciation temperatures

Landauer transport model for Hawking radiation from a Reissner-Nordstrom black hole

The recent work of Nation et al in which Hawking radiation energy and entropy flow from a black hole can be regarded as a one-dimensional (1D) Landauer transport process is extended to the case of a Reissner-Nordstrom (RN) black hole. It is found that the flow of charge current can also be transported via a 1D quantum channel except the current of Hawking radiation. The maximum entropy current, which is shown to be particle statistics independence, is also obtained

AUGMENTATION OF RAT LIVER REGENERATION BY FK 506 COMPARED WITH CYCLOSPORIN

The immunosuppressive drug, FK 506, increased the regeneration response that follows 40% and 70% hepatectomy in rats. The effect was similar to that obtained with cyclosporin. © 1989

Automatic Synonym Discovery with Knowledge Bases

Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., "apple" could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised-learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as "distant" supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework