Thermodynamic dislocation theory of high-temperature deformation in aluminum and steel

The statistical-thermodynamic dislocation theory developed in previous papers is used here in an analysis of high-temperature deformation of aluminum and steel. Using physics-based parameters that we expect theoretically to be independent of strain rate and temperature, we are able to fit experimental stress-strain curves for three different strain rates and three different temperatures for each of these two materials. Our theoretical curves include yielding transitions at zero strain in agreement with experiment. We find that thermal softening effects are important even at the lowest temperatures and smallest strain rates.Comment: 7 pages, 8 figure

Bounding film drainage in common thin films

A review of thin film drainage models is presented in which the predictions of thinning velocities and drainage times are compared to reported values on foam and emulsion films found in the literature. Free standing films with tangentially immobile interfaces and suppressed electrostatic repulsion are considered, such as those studied in capillary cells. The experimental thinning velocities and drainage times of foams and emulsions are shown to be bounded by predictions from the Reynolds and the theoretical MTsR equations. The semi-empirical MTsR and the surface wave equations were the most consistently accurate with all of the films considered. These results are used in an accompanying paper to develop scaling laws that bound the critical film thickness of foam and emulsion films

Red blood cells and other non-spherical capsules in shear flow: oscillatory dynamics and the tank-treading-to-tumbling transition

We consider the motion of red blood cells and other non-spherical microcapsules dilutely suspended in a simple shear flow. Our analysis indicates that depending on the viscosity, membrane elasticity, geometry and shear rate, the particle exhibits either tumbling, tank-treading of the membrane about the viscous interior with periodic oscillations of the orientation angle, or intermittent behavior in which the two modes occur alternately. For red blood cells, we compute the complete phase diagram and identify a novel tank-treading-to-tumbling transition at low shear rates. Observations of such motions coupled with our theoretical framework may provide a sensitive means of assessing capsule properties.Comment: 11 pages, 4 figure

Novel steady state of a microtubule assembly in a confined geometry

We study the steady state of an assembly of microtubules in a confined volume, analogous to the situation inside a cell where the cell boundary forms a natural barrier to growth. We show that the dynamical equations for growing and shrinking microtubules predict the existence of two steady states, with either exponentially decaying or exponentially increasing distribution of microtubule lengths. We identify the regimes in parameter space corresponding to these steady states. In the latter case, the apparent catastrophe frequency near the boundary was found to be significantly larger than that in the interior. Both the exponential distribution of lengths and the increase in the catastrophe frequency near the cell margin is in excellent agreement with recent experimental observations.Comment: 8 pages, submitted to Phys. Rev.

Spearfishing-induced behavioral changes of an unharvested species inside and outside a marine protected area.

By prohibiting fishing, marine protected areas (MPAs) provide a refuge for harvested species. Humans are often perceived as predators by prey and therefore respond fearfully to humans. Thus, fish responses to humans inside and outside of an MPA can provide insights into their perception of humans as a predatory threat. Previous studies have found differences in the distance that harvested species of fish initiate flight (flight initiation distance-FID) from humans inside and outside an MPA, but less is known about unharvested species. We focused on whether the lined bristletooth Ctenochaetus striatus, an unharvested surgeonfish, can discriminate between a snorkeler and a snorkeler with a spear gun inside and outside of a no-take MPA in Mo'orea, French Polynesia. Additionally, we incorporated starting distance (the distance between the person and prey at the start of an experimental approach), a variable that has been found to be important in assessing prey escape decisions in terrestrial species, but that has not been extensively studied in aquatic systems. Lined bristletooth FID was significantly greater in the presence of a spear gun and varied depending on if the spear gun encounter was inside or outside of the MPA. These results imply a degree of sophistication of fish antipredator behavior, generate questions as to how a nontargeted species of fish could acquire fear of humans, and demonstrate that behavioral surveys can provide insights about antipredator behavior

Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)

A molecular perspective on the limits of life: Enzymes under pressure

From a purely operational standpoint, the existence of microbes that can grow under extreme conditions, or "extremophiles", leads to the question of how the molecules making up these microbes can maintain both their structure and function. While microbes that live under extremes of temperature have been heavily studied, those that live under extremes of pressure have been neglected, in part due to the difficulty of collecting samples and performing experiments under the ambient conditions of the microbe. However, thermodynamic arguments imply that the effects of pressure might lead to different organismal solutions than from the effects of temperature. Observationally, some of these solutions might be in the condensed matter properties of the intracellular milieu in addition to genetic modifications of the macromolecules or repair mechanisms for the macromolecules. Here, the effects of pressure on enzymes, which are proteins essential for the growth and reproduction of an organism, and some adaptations against these effects are reviewed and amplified by the results from molecular dynamics simulations. The aim is to provide biological background for soft matter studies of these systems under pressure.Comment: 16 pages, 8 figure

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Involutivity of integrals for sine-Gordon, modified KdV and potential KdV maps

Closed form expressions in terms of multi-sums of products have been given in \cite{Tranclosedform, KRQ} of integrals of sine-Gordon, modified Korteweg-de Vries and potential Korteweg-de Vries maps obtained as so-called $(p,-1)$-traveling wave reductions of the corresponding partial difference equations. We prove the involutivity of these integrals with respect to recently found symplectic structures for those maps. The proof is based on explicit formulae for the Poisson brackets between multi-sums of products.Comment: 24 page

Exploring the corporate image formation process

Purpose - This paper aims to demonstrate the need to explore the image formation process to develop a more holistic definition of corporate image. Diminishing trust in managers has created increasingly negative perceptions toward corporations. Stakeholders are constantly evaluating and scrutinizing corporations to determine their trustworthiness and authenticity. To develop their perceptions toward these corporations, stakeholders rely on the key role of corporate image. In the present study, the complex relationships between corporate image, corporate reputation, corporate communication and corporate personality are investigated. These concepts form a corporation’s image formation process. Design/methodology/approach - Radley Yelday (RY), the communications agency collaborating in this research, facilitated 15 interviews with their employees. Using a semi structured interviewing method, discussions were guided toward the topic of corporate image among the respondents. Findings - Findings reveal the importance of corporate image under seven different dimensions: visual expression, positive feelings, environments expression, online appearance, staff/employees appearance, attitude and behavior and external communications (offline, online and effectiveness). Theoretical and managerial implications are discussed with suggestions for future researches. Originality/value - The authors develop a conceptual model that illustrates the corporate image formation process. The model includes seven dimensions – both with tangible and intangible aspects – forming corporate communication and corporate personality. These, in turn, translate into the corporate image. With time and experiences, corporate image creates a more consistent reputation, which consists of five different levels: awareness, familiarity, favorability, trust and advocacy. As demonstrated in this research, the seven key dimensions influencing this process are: visual expression, positive feelings, environment, online appearance, staff/employees appearance, attitude and behavior and external communications