Polite Combination of Algebraic Datatypes

Journal of Automated Reasoning - Algebraic datatypes, and among them lists and trees, have attracted a lot of interest in automated reasoning and Satisfiability Modulo Theories (SMT). Since its...     

A fluid approach to large volume job shop scheduling

We consider large volume job shop scheduling problems, in which there is a fixed number of machines, a bounded number of activities per job, and a large number of jobs. In large volume job shops it makes sense to solve a fluid problem and to schedule the jobs in such a way as to track the fluid solution. There have been several papers which used this idea to propose approximate solutions which are asymptotically optimal as the volume increases. We survey some of these results here. In most of these papers it is assumed that the problem consists of many identical copies of a fixed set of jobs. Our contribution in this paper is to extend the results to the far more general situation in which the many jobs are all different. We propose a very simple heuristic which can schedule such problems. We discuss asymptotic optimality of this heuristic, under a wide range of previously unexplored situations. We provide a software package to explore the performance of our policy, and present extensive computational evidence for its effectiveness.     

Sparse Approximation of 3D Meshes Using the Spectral Geometry of the Hamiltonian Operator

The discrete Laplace operator is ubiquitous in spectral shape analysis, since its eigenfunctions are provably optimal in representing smooth functions defined on the surface of the shape. Indeed, subspaces defined by its eigenfunctions have been utilized for shape compression, treating the coordinates as smooth functions defined on the given surface. However, surfaces of shapes in nature often contain geometric structures for which the general smoothness assumption may fail to hold. At the other end, some explicit mesh compression algorithms utilize the order by which vertices that represent the surface are traversed, a property which has been ignored in spectral approaches. Here, we incorporate the order of vertices into an operator that defines a novel spectral domain. We propose a method for representing 3D meshes using the spectral geometry of the Hamiltonian operator, integrated within a sparse approximation framework. We adapt the concept of a potential function from quantum physics and incorporate vertex ordering information into the potential, yielding a novel data-dependent operator. The potential function modifies the spectral geometry of the Laplacian to focus on regions with finer details of the given surface. By sparsely encoding the geometry of the shape using the proposed data-dependent basis, we improve compression performance compared to previous results that use the standard Laplacian basis and spectral graph wavelets.     

Confinement-guided shaping of semiconductor nanowires and nanoribbons: "writing with nanowires"

To fully exploit their full potential, new semiconductor nanowire building blocks with ab initio controlled shapes are desired. However, and despite the great synthetic advances achieved, the ability to control nanowire's geometry has been significantly limited. Here, we demonstrate a simple confinement-guided nanowire growth method that enables to predesign not only the chemical and physical attributes of the synthesized nanowires but also allows a perfect and unlimited control over their geometry. Our method allows the synthesis of semiconductor nanowires in a wide variety of two-dimensional shapes such as any kinked (different turning angles), sinusoidal, linear, and spiral shapes, so that practically any desired geometry can be defined. The shape-controlled nanowires can be grown on almost any substrate such as silicon wafer, quartz and glass slides, and even on plastic substrates (e.g., Kapton HN).     

Biological validation of a sample preparation method for ER-CALUX bioanalysis of estrogenic activity in sediment using mixtures of xeno-estrogens

The combined estrogenic effects of mixtures of environmental pollutants in the in vitro ER-CALUX (chemical activated luciferase gene expression) bioassaywere examined to biologically validate a sample preparation method for the analysis of estrogenic compounds in sediment. The method used accelerated solvent extraction (ASE) and gel permeation chromatography (GPC) and was validated with respect to recovery of biological response taking mixture effects into account. Four mixtures of three to six xenoestrogenic compounds (bisphenol A, 4-nonylphenol, (4,4'-dichlorodiphenyl)trichloroethane, (2,4'-dichlorodiphenyl)trichloroethane, dieldrin, 4-n-octylphenol, alpha-chlordane, dibutylphthalate, (4,4'-dichlorodiphenyl)dichloroethylene, and 2,4,5-trichlorobiphenyl) were prepared. Experimentally determined mixture effects were well described by the concept of concentration addition (CA), as expected for similarly acting compounds. Observed estradiol equivalence factors of the mixtures (on average 1.2 +/- 0.3) agreed very well with the value predicted according to CA. The sample preparation method was then applied to pure mixtures of standards and to sediment spiked with one of the mixtures. Recoveries of estrogenic compounds were estimated by determination of their mixture potencies in ER-CALUX and compared to the mixture effects predicted by CA. Recoveries of estrogenic activity were between 80 and 129%, indicating that the additive behavior of mixtures of xeno-estrogens is well conserved during sample preparation. Together with an average repeatability of 18.3%, low average limit of detection (2.6 +/- 1.8 pg of EEQ/ g), and coefficient of variance (3.5 +/- 3.3%),this demonstrated the suitability of the sample preparation method for the analysis of mixtures of (xeno-)estrogenic compounds in sediment with the ER-CALUX assay.     

Lifetime measurements in an electrostatic ion beam trap using image charge monitoring

A technique for mass-selective lifetime measurements of keV ions in a linear electrostatic ion beam trap is presented. The technique is based on bunching the ions using a weak RF potential and non-destructive ion detection by a pick-up electrode. This method has no mass-limitation, possesses the advantage of inherent mass-selectivity, and offers a possibility of measuring simultaneously the lifetimes of different ion species with no need for prior mass-selection.     

Conductive Scaffolds for Cardiac and Neuronal Tissue Engineering: Governing Factors and Mechanisms

Tissue engineering is a promising therapeutic approach in medicine, targeting the replacement of a diseased tissue with a healthy one grown within an artificial scaffold. Due to the high prevalence of cardiac and brain‐related ailments that involve some necrosis of tissue, cardiac and neuronal tissue engineering are intensely studied fields in regenerative medicine. A growing trend in the use of conductive scaffolds for the growth of these tissues has been witnessed recently. While the results are irrefutable, the mechanism of how an electrically conducting scaffold interacts with an electroactive tissue remains has remained elusive. An up‐to‐date summary of all work done in the field is reported, with a special focus on the specific contribution of the conductive scaffold on the performance of the formed tissue. The cell–scaffold electronic interface is then explored from an electrical engineering perspective. The electronic configuration of the system and the mechanisms and governing factors controlling the ability of a conductive scaffold to support cardiac and neuronal tissues are discussed. Using several simulations, the required conductivity of the scaffold in order for it to be suitable for tissue engineering—which also depends on the nature of the charge carriers—is also discussed.