Coalescence of Liquid Drops

When two drops of radius $R$ touch, surface tension drives an initially singular motion which joins them into a bigger drop with smaller surface area. This motion is always viscously dominated at early times. We focus on the early-time behavior of the radius \rmn of the small bridge between the two drops. The flow is driven by a highly curved meniscus of length 2\pi \rmn and width \Delta\ll\rmn around the bridge, from which we conclude that the leading-order problem is asymptotically equivalent to its two-dimensional counterpart. An exact two-dimensional solution for the case of inviscid surroundings [Hopper, J. Fluid Mech. ${\bf 213}$, 349 (1990)] shows that \Delta \propto \rmn^3 and \rmn \sim (t\gamma/\pi\eta)\ln [t\gamma/(\eta R)]; and thus the same is true in three dimensions. The case of coalescence with an external viscous fluid is also studied in detail both analytically and numerically. A significantly different structure is found in which the outer fluid forms a toroidal bubble of radius \Delta \propto \rmn^{3/2} at the meniscus and \rmn \sim (t\gamma/4\pi\eta) \ln [t\gamma/(\eta R)]. This basic difference is due to the presence of the outer fluid viscosity, however small. With lengths scaled by $R$ a full description of the asymptotic flow for \rmn(t)\ll1 involves matching of lengthscales of order \rmn^2, \rmn^{3/2}, \rmn$, 1 and probably$\rmn^{7/4}$.Comment: 36 pages, including 9 figure

Recurrent Memory Networks for Language Modeling

Recurrent Neural Networks (RNN) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success remains a challenge. In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data. We demonstrate the power of RMN on language modeling and sentence completion tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM) network on three large German, Italian, and English dataset. Additionally we perform in-depth analysis of various linguistic dimensions that RMN captures. On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state-of-the-art by a large margin.Comment: 8 pages, 6 figures. Accepted at NAACL 201

Concussion in sport: Practical management guidelines for medical practitioners

No Abstract

MULTI-INSTRUMENTAL IDENTIFICATION OF ORPIMENT IN ARCHAEOLOGICAL MORTUARY CONTEXTS

Indexación: Web of Science; Scielo.This paper reports on an unknown yellowish mineral compound found in an archaeological context from Chorrillos cemetery (Calama, Chile) dating to the Early Formative period (800 - 200 B.C.). We used optic microscopy, SEM, EDX, 1H-RMN, 13C-RMN, and infrared (IR) and Raman spectroscopy to tease out the chemical and molecular composition of the sample. The microscopic images show amorphous yellowish granulates with heterogeneous chemical surfaces. 1H-RMN and 13C-RMN negative results show that the sample is free of organic matter. The SEM and EDX indicate the presence of arsenic and sulfur in the sample. The IR and Raman analyses suggest the presence of orpiment which is a toxic yellow arsenic sulfide mineral.http://ref.scielo.org/mfcms