Computing the Viscosity of Supercooled Liquids: Markov Network Model

The microscopic origin of glass transition, when liquid viscosity changes continuously by more than ten orders of magnitude, is challenging to explain from first principles. Here we describe the detailed derivation and implementation of a Markovian Network model to calculate the shear viscosity of deeply supercooled liquids based on numerical sampling of an atomistic energy landscape, which sheds some light on this transition. Shear stress relaxation is calculated from a master-equation description in which the system follows a transition-state pathway trajectory of hopping among local energy minima separated by activation barriers, which is in turn sampled by a metadynamics-based algorithm. Quantitative connection is established between the temperature variation of the calculated viscosity and the underlying potential energy and inherent stress landscape, showing a different landscape topography or “terrain” is needed for low-temperature viscosity (of order 10[superscript 7] Pa·s) from that associated with high-temperature viscosity (10[superscript −5] Pa·s). Within this range our results clearly indicate the crossover from an essentially Arrhenius scaling behavior at high temperatures to a low-temperature behavior that is clearly super-Arrhenius (fragile) for a Kob-Andersen model of binary liquid. Experimentally the manifestation of this crossover in atomic dynamics continues to raise questions concerning its fundamental origin. In this context this work explicitly demonstrates that a temperature-dependent “terrain” characterizing different parts of the same potential energy surface is sufficient to explain the signature behavior of vitrification, at the same time the notion of a temperature-dependent effective activation barrier is quantified.Corning IncorporatedBoston University. Center for Scientific Computing and VisualizationNational Science Foundation (U.S.) (grant DMR-1008104)National Science Foundation (U.S.) (grant DMR-0520020)United States. Air Force Office of Scientific Research (FA9550-08-1-0325

New Keywords: Migration and Borders

“New Keywords: Migration and Borders” is a collaborative writing project aimed at developing a nexus of terms and concepts that fill-out the contemporary problematic of migration. It moves beyond traditional and critical migration studies by building on cultural studies and post-colonial analyses, and by drawing on a diverse set of longstanding author engagements with migrant movements. The paper is organized in four parts (i) Introduction, (ii) Migration, Knowledge, Politics, (iii) Bordering, and (iv) Migrant Space/Times. The keywords on which we focus are: Migration/Migration Studies; Militant Investigation; Counter-mapping; Border Spectacle; Border Regime; Politics of Protection; Externalization; Migrant Labour; Differential inclusion/exclusion; Migrant struggles; and Subjectivity

Riociguat treatment in patients with chronic thromboembolic pulmonary hypertension: Final safety data from the EXPERT registry

Objective: The soluble guanylate cyclase stimulator riociguat is approved for the treatment of adult patients with pulmonary arterial hypertension (PAH) and inoperable or persistent/recurrent chronic thromboembolic pulmonary hypertension (CTEPH) following Phase

Evolution Of Sensor Suites For Complex Environments

We present a genetic algorithm (GA) based decision tool for the design and configuration of teams of unmanned ground sensors. The goal of the algorithm is to generate candidate solutions that meet cost and performance constraints. The GA evolves the membership, placement, and characteristics of a team of cooperating sensors. Previous work shows that this algorithm can generate successful teams in simple, obstacle free environments. This work examines the performance of our algorithm in environments that include obstacles. © 2006 IEEE

Distributed Task Allocation In Dynamic Environments

This work investigates the behavior of a distributed team of agents on a dynamic distributed task allocation problem. Previous work finds that a distributed decision making process can effectively assign tasks appropriately to team members even when agents have only local information. We study this problem in a distributed environment in which agents can move, thus causing local neighborhoods to change over time. Results indicate that a higher level of adaptation is clearly required in the dynamic environment. Despite the increased difficulty, the distributed team is able achieve comparable behavior in both static and dynamic environments. © 2009 SPIE

Designing Teams Of Unattended Ground Sensors Using Genetic Algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA\u27s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Team-Based Resource Allocation Using A Decentralized Social Decision-Making Paradigm

We examine the use of local decentralized decision-making methods for solving the problem of resource allocation. Specifically, we study the problem of frequency coverage given a team of cooperating receivers. The decision making process is decentralized in that receivers can only communicate locally. We use an extension of the minority game approach to allocate receivers to current frequency coverage tasks

Designing teams of unattended ground sensors using genetic algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA\u27s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Designing Teams of Unattended Ground Sensors Using Genetic Algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA’s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements