31 research outputs found
Gesture-Based Interface Using Baby Signs for the Elderly and People with Mobility Impairment in a Smart House Environment
Radicalization as a reaction to failure: An economic model of Islamic extremism
This paper views Islamist radicals as self-interested political revolutionaries and builds on a general model of political extremism developed in a previous paper (Ferrero, 2002). Extremism is modelled as a production factor whose effect on expected revenue is initially positive and then turns negative, and whose level is optimally chosen by a revolutionary organization. The organization is bound by a free-access constraint and hence uses the degree of extremism as a means of indirectly controlling its level of membership with the aim of maximizing expected per capita income of its members, like a producer co-operative. The gist of the argument is that radicalization may be an optimal reaction to perceived failure (a widespread perception in the Muslim world) when political activists are, at the margin, relatively strongly averse to effort but not so averse to extremism. This configuration is at odds with secular, Western-style revolutionary politics but seems to capture well the essence of Islamic revolutionary politics, embedded as it is in a doctrinal framework. Copyright Springer Science + Business Media, Inc. 2005
Atomoxetine for depression and other neuropsychiatric symptoms in Parkinson disease(LOE Classification)
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The CatWISE2020 Catalog
The CatWISE2020 Catalog consists of 1,890,715,640 sources over the entire sky selected from Wide-field Infrared Survey Explorer (WISE) and NEOWISE survey data at 3.4 and 4.6 μm (W1 and W2) collected from 2010 January 7 to 2018 December 13. This data set adds two years to that used for the CatWISE Preliminary Catalog, bringing the total to six times as many exposures spanning over 16 times as large a time baseline as the AllWISE catalog. The other major change from the CatWISE Preliminary Catalog is that the detection list for the CatWISE2020 Catalog was generated using crowdsource from Schlafly et al., while the CatWISE Preliminary Catalog used the detection software used for AllWISE. These two factors result in roughly twice as many sources in the CatWISE2020 Catalog. The scatter with respect to Spitzer photometry at faint magnitudes in the COSMOS field, which is out of the Galactic Plane and at low ecliptic latitude (corresponding to lower WISE coverage depth) is similar to that for the CatWISE Preliminary Catalog. The 90% completeness depth for the CatWISE2020 Catalog is at W1 = 17.7 mag and W2 = 17.5 mag, 1.7 mag deeper than in the CatWISE Preliminary Catalog. In comparison to Gaia, CatWISE2020 motions are accurate at the 20 mas yr-1 level for W1∼15 mag sources and at the ∼100 mas yr-1 level for W1∼17 mag sources. This level of accuracy represents a 12 improvement over AllWISE. The CatWISE catalogs are available in the WISE/NEOWISE Enhanced and Contributed Products area of the NASA/IPAC Infrared Science Archive. © 2021. The American Astronomical Society. All rights reserved..Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Coordinating multi-location production and customer delivery
We study two parallel machine scheduling problems with equal processing time jobs and delivery times and costs. The jobs are processed on machines which are located at different sites, and delivered to a customer by a single vehicle. The first objective considered is minimizing the sum of total weighted completion time and total vehicle delivery costs. The second objective considered is minimizing the sum of total tardiness and total vehicle delivery costs. We develop several interesting properties of an optimal scheduling and delivery policy, and show that both problems can be solved by reduction to the Shortest-Path problem in a corresponding network. The overall computational effort of both algorithms is O(n m2+m+1) (where n and m are the number of jobs and the number of machines, respectively) by the application of the Directed Acyclic Graph (DAG) method. We also discuss several special cases for which the overall computational effort can be significantly reduced