The use of carbon nanomaterials in membrane distillation membranes: a review

Membrane distillation (MD) is a thermal-based separation technique with the potential to treat a wide range of water types for various applications and industries. Certain challenges remain however, which prevent it from becoming commercially widespread including moderate permeate flux, decline in separation performance over time due to pore wetting and high thermal energy requirements. Nevertheless, its attractive characteristics such as high rejection (ca. 100%) of non-volatile species, its ability to treat highly saline solutions under low operating pressures (typically atmospheric) as well as its ability to operate at low temperatures, enabling waste-heat integration, continue to drive research interests globally. Of particular interest is the class of carbon-based nanomaterials which includes graphene and carbon nanotubes, whose wide range of properties have been exploited in an attempt to overcome the technical challenges that MD faces. These low dimensional materials exhibit properties such as high specific surface area, high strength, tuneable hydrophobicity, enhanced vapour transport, high thermal and electrical conductivity and others. Their use in MD has resulted in improved membrane performance characteristics like increased permeability and reduced fouling propensity. They have also enabled novel membrane capabilities such as in-situ fouling detection and localised heat generation. In this review we provide a brief introduction to MD and describe key membrane characteristics and fabrication methods. We then give an account of the various uses of carbon nanomaterials for MD applications, focussing on polymeric membrane systems. Future research directions based on the findings are also suggested.     

Ant algorithms and simulated annealing for multicriteria dynamic programming

The paper presents a comparison of ant algorithms and simulated annealing as well as their applications in multicriteria discrete dynamic programming. The considered dynamic process consists of finite states and decision variables. In order to describe the effectiveness of multicriteria algorithms, four measures of the quality of the nondominated set approximations are used.     

Selectively grouping neurons in recurrent networks of lateral inhibition

Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, not much is known about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groups--the number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.     

A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models

Simplified protein models are used for investigating general properties of proteins and principles of protein folding. Furthermore, they are suited for hierarchical approaches to protein structure prediction. A well known protein model is the HP-model of Lau and Dill [Lau, K. F., & Dill, K. A. (1989)]. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22, 3986–3997) which models the important aspect of hydrophobicity. One can define the HP-model for various lattices, among them two-dimensional and three-dimensional ones. Here, we investigate the three-dimensional case. The main motivation for studying simplified protein models is to be able to predict model structures much more quickly and more accurately than is possible for real proteins. However, up to now there was a dilemma: the algorithmically tractable, simple protein models can not model real protein structures with good quality and introduce strong artifacts. We present a constraint-based method that largely improves this situation. It outperforms all existing approaches for lattice protein folding in HP-models. This approach is the first one that can be applied to two three-dimensional lattices, namely the cubic lattice and the face-centered-cubic (FCC) lattice. Moreover, it is the only exact method for the FCC lattice. The ability to use the FCC lattice is a significant improvement over the cubic lattice. The key to our approach is the ability to compute maximally compact sets of points (used as hydrophobic cores), which we accomplish for the first time for the FCC lattice.     

Comparison of analysis methods for package-induced stresses on moulded Hall sensors

Due to the piezoresistive and the piezo-Hall effect in semiconductor materials, Hall sensors show a strong temperature dependency and also a drift when subjected to temperature cycles Manic et al. (2000). Four factors mainly influence the mechanical stress in the sensitive layer. These are the geometry of the device, the differences of the coefficients of thermal expansion of the package materials, the temperature-dependent material properties and the time-dependent, viscous material properties. The objective of this investigation was to determine the mechanical stress in a moulded Hall sensor during the packaging process by finite-element simulation in comparison to experimental methods. It is shown that after each process-step the mechanical stress in the sensitive layer changes over time depending on the absolute value and the rate of the temperature change. Measurements of the inverse bending radius of glued and moulded chips show good agreement to the simulations.     

On Karnaugh maps and magic squares

In 1953, Karnaugh maps were developed at the Harvard Computation Laboratory for the minimization of Boolean algebraic expressions (as an approach to logic circuit synthesis) [5].     

Behavioral Refinement of Graph Transformation-Based Models

Model-driven software engineering requires the refinement of abstract models into more concrete, platform-specific ones. To create and verify such refinements, behavioral models capturing recon- figuration or communication scenarios are presented as instances of a dynamic meta-model, i.e., a typed graph transformation system specifying the concepts and basic operations scenarios may be composed of. Possible refinement relations between models can now be described based on the corresponding meta-models.In contrast to previous approaches, refinement relations on graph transformation systems are not defined as fixed syntactic mappings between abstract transformation rules and, e.g., concrete rule expressions, but allow for a more loose, semantically defined relation between the transformation systems, resulting in a more flexible notion of refinement.