Modeling thermodynamic properties of aqueous single‐solute and multi‐solute sugar solutions with PC‐SAFT

The Perturbed‐Chain Statistical Association Fluid Theory is applied to simultaneously describe various thermodynamic properties (solution density, osmotic coefficient, solubility) of aqueous solutions containing a monosaccharide or a disaccharide. The 13 sugars considered within this work are: glucose, fructose, fucose, xylose, maltose, mannitol, mannose, sorbitol, xylitol, galactose, lactose, trehalose, and sucrose. Four adjustable parameters (three pure‐sugar parameters and a k_ij between sugar and water that was allowed to depend linearly on temperature) were obtained from solution densities and osmotic coefficients of binary sugar/water solutions at 298.15 K available in literature. Using these parameters, the sugar solubility in water and in ethanol could be predicted satisfactorily. Further, osmotic coefficients and solubility in aqueous solutions containing two solutes (sugar/sugar, sugar/salt) were predicted (no additional k_ij parameters between the two solutes) reasonably. The model was also applied to predict the solubility of a sugar in a solvent mixture (e.g., water/ethanol) without additional fitting parameters. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4794–4805, 2013     

Constructive non-regularized geometry

Solid modelling is concerned with the construction and manipulation of unambiguous computer representations of solid objects. These representations permit us to distinguish the interior, the boundary and the complement of a solid. They are conveniently specified in Constructive Solid Geometry (CSG) by a construction tree that has solid primitives as leaves and rigid body motions or regularized Boolean operations as internal nodes. Algoriths for classifying sets with respect to CSG trees and for evaluating the boundaries of the corresponding solids are known, at least for simple geometric domains. Emerging CAD applications require that we extend the domain of solid modellers to support more general and more structured geometric objects. The focus is on dimensionally non-homogeneous, non-closed pointsets with internal structures. These entities are well suited for dealing with mixed-dimensional (‘non-manifold’) objects in ⁿ that have dangling or missing boundary elements, and that may be composed of several regions. A boundary representation for such objects has been described elsewhere. We propose to specify and represent inhomogeneous objects in terms of Constructive Non-Regularized Geometry (CNRG) trees that extend the domain of CSG by supporting non-regularized primitive shapes as leaves, and by providing more general set-theoretic and topological operators at interior nodes. Filtering operations are also provided that construct CNRG objects from aggregates of selected regions of other CNRG objects. A syntax and semantics of the operators in CNRG are presented, and some basic algorithms for classifying pointsets with respect to the regions of objects represented by CNRG trees are outlined.     

Activity preserving graph simplification

We study the problem of simplifying a given directed graph by keeping a small subset of its arcs. Our goal is to maintain the connectivity required to explain a set of observed traces of information propagation across the graph. Unlike previous work, we do not make any assumption about an underlying model of information propagation. Instead, we approach the task as a combinatorial problem. We prove that the resulting optimization problem is $\mathbf{NP}$ NP -hard. We show that a standard greedy algorithm performs very well in practice, even though it does not have theoretical guarantees. Additionally, if the activity traces have a tree structure, we show that the objective function is supermodular, and experimentally verify that the approach for size-constrained submodular minimization recently proposed by Nagano et al. (28th International Conference on Machine Learning, 2011) produces very good results. Moreover, when applied to the task of reconstructing an unobserved graph, our methods perform comparably to a state-of-the-art algorithm devised specifically for this task.     

JAID: An algorithm for data fusion and jamming avoidance on distributed sensor networks

Mobile Agent (MA) technology has been recently proposed in Wireless Sensor Networks (WSNs) literature to answer the scalability problem of client/server model in data fusion applications. In this paper, we describe the critical role MAs can play in the field of security and robustness of a WSN in addition to data fusion. The design objective of our Jamming Avoidance Itinerary Design (JAID) algorithm is twofold: (a) to calculate near-optimal routes for MAs that incrementally fuse the data as they visit the nodes; (b) in the face of jamming attacks against the WSN, to modify the itineraries of the MAs to bypass the jammed area(s) while not disrupting the efficient data dissemination from working sensors. If the number of jammed nodes is small, JAID only modifies the pre-jamming scheduled itineraries to increase the algorithm’s promptness. Otherwise, JAID re-constructs the agent itineraries excluding the jammed area(s). Another important feature of JAID is the suppression of data taken from sensors when the associated successive readings do not vary significantly. Data suppression also occurs when sensors’ readings are identical to those of their neighboring sensors. Simulation results confirm that JAID enables retrieval of information from the working sensors of partially jammed WSNs and verifies its performance gain over alternative approaches in data fusion tasks.     

Large-scale synthesis, size control, and anisotropic growth of gamma-Fe2O3 nanoparticles: organosols and hydrosols

Monodispersed, spherical gamma-Fe2O3 nanoparticles with controllable size in large-scale were prepared by thermolytic decomposition of FeCl3.6H2O in aliphatic amines. The nanoparticles gave very stable colloidal solution in organic solvents and can be easily converted to water-soluble by a very simple route. Their characterisation was based on TEM microscopy, XRD, M?ssbauer, and magnetic measurements. Furthermore, a small amount of Pt can lead to the formation of anisotropic gamma-Fe2O3 nanostructures.     

An approach for near-optimal distributed data fusion in wireless sensor networks

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.