Calculating the Process Driven Business Value of RFID Investments - A Causal Model for the Measurement of RFID Technologies in Supply Chain Logistics

Calculating the process driven value of RFID investments is very difficult. From a company’s perspective it is important to understand the concrete contribution of an RFID system with regard to individual processes. The problem of profitability analyses in IS is that such technologies cannot be calculated as an economic standard investment. Hence, we propose a reference model as a generic knowledge base for referential RFID impacts. Our model supports the structuring and evaluation of RFID benefits along business processes. With this, we propose indicators for the derivation of an RFID cause-and-effect chain. The allocation of RFID effects to processes within the reference framework helps in identifying the right logistic unit levels for RFID transponder investments

Non-Gaussian Component Analysis using Entropy Methods

Non-Gaussian component analysis (NGCA) is a problem in multidimensional data analysis which, since its formulation in 2006, has attracted considerable attention in statistics and machine learning. In this problem, we have a random variable $X$ in $n$-dimensional Euclidean space. There is an unknown subspace $\Gamma$ of the $n$-dimensional Euclidean space such that the orthogonal projection of $X$ onto $\Gamma$ is standard multidimensional Gaussian and the orthogonal projection of $X$ onto $\Gamma^{\perp}$, the orthogonal complement of $\Gamma$, is non-Gaussian, in the sense that all its one-dimensional marginals are different from the Gaussian in a certain metric defined in terms of moments. The NGCA problem is to approximate the non-Gaussian subspace $\Gamma^{\perp}$ given samples of $X$. Vectors in $\Gamma^{\perp}$ correspond to `interesting' directions, whereas vectors in $\Gamma$ correspond to the directions where data is very noisy. The most interesting applications of the NGCA model is for the case when the magnitude of the noise is comparable to that of the true signal, a setting in which traditional noise reduction techniques such as PCA don't apply directly. NGCA is also related to dimension reduction and to other data analysis problems such as ICA. NGCA-like problems have been studied in statistics for a long time using techniques such as projection pursuit. We give an algorithm that takes polynomial time in the dimension $n$ and has an inverse polynomial dependence on the error parameter measuring the angle distance between the non-Gaussian subspace and the subspace output by the algorithm. Our algorithm is based on relative entropy as the contrast function and fits under the projection pursuit framework. The techniques we develop for analyzing our algorithm maybe of use for other related problems

Broad redshifted line as a signature of outflow

We formulate and solve the diffusion problem of line photon propagation in a bulk outflow from a compact object (black hole or neutron star) using a generic assumption regarding the distribution of line photons within the outflow. Thomson scattering of the line photons within the expanding flow leads to a decrease of their energy which is of first order in v/c, where v is the outflow velocity and c is the speed of light. We demonstrate that the emergent line profile is closely related to the time distribution of photons diffusing through the flow (the light curve) and consists of a broad redshifted feature. We analyzed the line profiles for the general case of outflow density distribution. We emphasize that the redshifted lines are intrinsic properties of the powerful outflow that are supposed to be in many compact objects.Comment: 16 pages, 1 black-white figure and 2 color figures; accepted for publication in the Astrophysical Journa

Unique Proteomic Signatures Distinguish Macrophages and Dendritic Cells

Monocytes differentiate into heterogeneous populations of tissue macrophages and dendritic cells (DCs) that regulate inflammation and immunity. Identifying specific populations of myeloid cells in vivo is problematic, however, because only a limited number of proteins have been used to assign cellular phenotype. Using mass spectrometry and bone marrow-derived cells, we provided a global view of the proteomes of M-CSF-derived macrophages, classically and alternatively activated macrophages, and GM-CSF-derived DCs. Remarkably, the expression levels of half the plasma membrane proteins differed significantly in the various populations of cells derived in vitro. Moreover, the membrane proteomes of macrophages and DCs were more distinct than those of classically and alternatively activated macrophages. Hierarchical cluster and dual statistical analyses demonstrated that each cell type exhibited a robust proteomic signature that was unique. To interrogate the phenotype of myeloid cells in vivo, we subjected elicited peritoneal macrophages harvested from wild-type and GM-CSF-deficient mice to mass spectrometric and functional analysis. Unexpectedly, we found that peritoneal macrophages exhibited many features of the DCs generated in vitro. These findings demonstrate that global analysis of the membrane proteome can help define immune cell phenotypes in vivo

Preheating After Modular Inflation

We study (p)reheating in modular (closed string) inflationary scenarios, with a special emphasis on Kahler moduli/Roulette models. It is usually assumed that reheating in such models occurs through perturbative decays. However, we find that there are very strong non-perturbative preheating decay channels related to the particular shape of the inflaton potential (which is highly nonlinear and has a very steep minimum). Preheating after modular inflation, proceeding through a combination of tachyonic instability and broad-band parametric resonance, is perhaps the most violent example of preheating after inflation known in the literature. Further, we consider the subsequent transfer of energy to the standard model sector in scenarios where the standard model particles are confined to a D7-brane wrapping the inflationary blow-up cycle of the compactification manifold or, more interestingly, a non-inflationary blow up cycle. We explicitly identify the decay channels of the inflaton in these two scenarios. We also consider the case where the inflationary cycle shrinks to the string scale at the end of inflation; here a field theoretical treatment of reheating is insufficient and one must turn instead to a stringy description. We estimate the decay rate of the inflaton and the reheat temperature for various scenarios.Comment: 34 pages, 10 figures. Accepted for publication in JCA

Feasibility of detecting single atoms using photonic bandgap cavities

We propose an atom-cavity chip that combines laser cooling and trapping of neutral atoms with magnetic microtraps and waveguides to deliver a cold atom to the mode of a fiber taper coupled photonic bandgap (PBG) cavity. The feasibility of this device for detecting single atoms is analyzed using both a semi-classical treatment and an unconditional master equation approach. Single-atom detection seems achievable in an initial experiment involving the non-deterministic delivery of weakly trapped atoms into the mode of the PBG cavity.Comment: 11 pages, 5 figure

Prediction of anisotropy from flow models: A comparison of three methods

Observations of anisotropy in Earth are used regularly as constraints for models of deformation, using various assumptions about the relationship between deformation and the resulting anisotropic fabric. We compare three methods for calculating fabric from velocity fields: tracking of finite strain ellipses, a kinematic crystallographic code, and the evolution of directors. We find that the use of the finite strain ellipse provides only limited prediction capabilities, as it cannot reproduce experimental observations that involve recrystallization. The crystallographic code we tested (a variant of the popular code D-Rex) provides a more complete fabric prediction, but at a much higher computational cost. The directors method provides an intermediate solution: while it does not include some of the more complex crystallographic processes that D-Rex does, the results of this method closely resemble those of D-Rex, at a lower computational cost. The directors are also capable of tracking anisotropy at much larger strains than D-Rex. We conclude that when computation speed is important, for example, in self-consistent geodynamic models with anisotropic rheology, the directors method provides an appropriate approximation

Analytical Parametrization of Self-Consistent Polycrystal Mechanics: Fast Calculation of Upper Mantle Anisotropy

Progressive deformation of upper mantle rocks via dislocation creep causes their constituent crystals to take on a non-random orientation distribution (crystallographic preferred orientation or CPO) whose observable signatures include shear-wave splitting and azimuthal dependence of surface wave speeds. Comparison of these signatures with mantle flow models thus allows mantle dynamics to be unraveled on global and regional scales. However, existing self-consistent models of CPO evolution are computationally expensive when used in 3-D and/or time-dependent convection models. Here we propose a new method, called ANPAR, which is based on an analytical parameterisation of the crystallographic spin predicted by the second-order (SO) self-consistent theory. Our parameterisation runs approximately 2-6x10^4 times faster than the SO model and fits its predictions for CPO and crystallographic spin with a variance reduction > 99%. We illustrate the ANPAR model predictions for the deformation of olivine with three dominant slip systems, (010)[100], (001)[100] and (010)[001], for three uniform deformations (uniaxial compression, pure shear, simple shear) and for a corner-flow model of a spreading mid-ocean ridge