HSPM 7336 - The Healthcare Supply Chain

The healthcare supply chain is a vital core business component of the health organization with the mission of delivering the technological elements of the patient care process to the providers of care. From strategic sourcing and purchasing, acquisition, logistics, inventory management, to point of use applications, this course provides understanding, knowledge and evaluation models to operate and manage an organization’s enterprise resource planning and management system, specifically with regard to the supply chain system and the management of that system as evaluated from a strategic, operations management and financial perspective

Gas Plume Species Identification in Airborne LWIR Imagery Using Constrained Stepwise Regression Analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Airborne thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work is performed only on pixels having been determined to contain the plume. Constrained stepwise linear regression is used in this study. Results indicate that the ability of the algorithm to correctly identify plume gases is directly related to the concentration of the gas. Previous concerns that the algorithm is hindered by spectral overlap were eliminated through the use of constraints on the regression

Gas Plume Species Identification by Regression Analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Overhead thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work will then be used only on pixels having been determined to contain the plume. Stepwise linear regression is considered in this study. Stepwise regression is attractive for this application as only those gases truly in the plume will be present in the final model. Preliminary results from the study show that stepwise regression is successful at correctly identifying the gases present in a plume. Analysis of the results indicates that the spectral overlap of absorption features in different gas species leads to false identifications

Hybridization of Hyperspectral Imaging Target Detection Algorithm Chains

Detection of a known target in an image can be accomplished using several different approaches. The complexity and number of steps involved in the target detection process makes a comparison of the different possible algorithm chains desirable. Of the different steps involved, some have a more significant impact than others on the final result - the ability to find a target in an image. These more important steps often include atmospheric compensation, noise and dimensionality reduction, background characterization, and detection (matched filtering for this research). A brief overview of the algorithms to be compared for each step will be presented. This research seeks to identify the most effective set of algorithms for a particular image or target type. Several different algorithms for each step will be presented, to include ELM, FLAASH, MNF, PPI, MAXD, the structured background matched filters OSP, and ASD. The chains generated by these algorithms will be compared using the Forest Radiance I HYDICE data set. Finally, receiver operating characteristic (ROC) curves will be calculated for each algorithm chain and, as an end result, a comparison of the various algorithm chains will be presented

The Invariant Algorithm for Identification and Detection of Multiple Gas Plumes and Weak Releases

The ability to detect and identify gaseous effluents is a problem that has been pursued with limited success. It has been shown to bepossible using the Invariant algorithm on synthetic hyperspectralscenes with a strong single gas release. That however, is a veryspecific case and leaves room for further investigation. This studylooks at more realistic detection and release scenarios. Ourimplementation of the Invariant algorithm uses Singular ValueDecomposition (SVD) to select basis vectors from a subspace of targetgases in conjunction with a Generalized Likelihood Ratio Test (GLRT) to determine on a pixel by pixel basis how ``like the target gas each pixel is. The target gases are modeled in the image radiance space including atmospheric effects. Target spectra are modeled in both emission and absorption. This study investigates how well weak plumes are detected. Also, there will be a test of a mixed gas in a strong plume release. Finally, a situation where a weak multiple gas release will be discussed. Synthetic hyperspectral imagery in the long wave infrared region (LWIR) of the electromagnetic spectrum will be the predominate data used in this study. This algorithm has been found to be applicable for these detection and identification scenarios

Identification and Detection of Gaseous Effluents from Hyperspectral Imagery Using Invariant Algorithms

The ability to detect and identify effluent gases is, and will continue to be, of great importance. This would not only aid in the regulation of pollutants but also in treaty enforcement and monitoring the production of weapons. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research utilizes hyperspectral imagery in the infrared region of the electromagnetic spectrum to evaluate an invariant method of detecting and identifying gases within a scene. The image is evaluated on a pixel-by-pixel basis and is studied at the subpixel level. A library of target gas spectra is generated using a simple slab radiance model. This results in a more robust description of gas spectra which are representative of real-world observations. This library is the subspace utilized by the detection and identification algorithms. The subspace will be evaluated for the set of basis vectors that best span the subspace. The Lee algorithm will be used to determine the set of basis vectors, which implements the Maximum Distance Method (MaxD). A Generalized Likelihood Ratio Test (GLRT) determines whether or not the pixel contains the target. The target can be either a single species or a combination of gases. Synthetically generated scenes will be used for this research. This work evaluates whether the Lee invariant algorithm will be effective in the gas detection and identification problem

Conflicts of interest in medicine and their management: current challenges and initiatives in Germany

Conflicts of interest (COI) in healthcare have increasingly gained attention in the lay press as well as among healthcare professionals. COIs increase the risk of undue influence on professional decision making and may have far-reaching consequences in healthcare. Therefore, it is essential to develop strategies to deal with such risk situations in order to prevent negative outcomes for patients and the health care system. This article describes recent research on COIs in Germany as well as initiatives aiming at more transparency and better management of COIs in Germany

Observation of Long-Lived Muonic Hydrogen in the 2S State

The kinetic energy distribution of ground state muonic hydrogen atoms mu-p(1S) is determined from time-of-flight spectra measured at 4, 16, and 64 hPa H2 room-temperature gas. A 0.9 keV-component is discovered and attributed to radiationless deexcitation of long-lived mu-p(2S) atoms in collisions with H2 molecules. The analysis reveals a relative population of about 1%, and a pressure-dependent lifetime (e.g. (30.4 +21.4 -9.7) ns at 64 hPa) of the long-lived mu-p(2S) population, equivalent to a 2S-quench rate in mu-p(2S) + H2 collisions of (4.4 +2.1 -1.8) 10^11 s^-1 at liquid hydrogen density.Comment: 4 pages, 2 figures, accepted for publication in Physical Review Letter