1,341 research outputs found

    Figurative Language: How is it Used in Basal Readings?

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    How much attention do editors of basal readers give to figurative language? What is the most common figure of speech found in basal readers? What is the least common figure of speech bound in basal readers? These were the questions the authors sought to answer through their research

    Malaria Clusters among Illegal Chinese Immigrants to Europe through Africa

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    Between November 2002 and March 2003, 17 cases of malaria (1 fatal) were observed in illegal Chinese immigrants who traveled to Italy through Africa. A further cluster of 12 was reported in August, 2002. Several immigrants traveled by air, making the risk of introducing sudden acute respiratory syndrome a possibility should such illegal immigrations continue

    Methodological Issues of Spatial Agent-Based Models

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    Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling

    Sharper and Simpler Nonlinear Interpolants for Program Verification

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    Interpolation of jointly infeasible predicates plays important roles in various program verification techniques such as invariant synthesis and CEGAR. Intrigued by the recent result by Dai et al.\ that combines real algebraic geometry and SDP optimization in synthesis of polynomial interpolants, the current paper contributes its enhancement that yields sharper and simpler interpolants. The enhancement is made possible by: theoretical observations in real algebraic geometry; and our continued fraction-based algorithm that rounds off (potentially erroneous) numerical solutions of SDP solvers. Experiment results support our tool's effectiveness; we also demonstrate the benefit of sharp and simple interpolants in program verification examples

    Non-smooth optimization methods for computation of the conditional value-at-risk and portfolio optimization

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    We examine numerical performance of various methods of calculation of the Conditional Value-at-risk (CVaR), and portfolio optimization with respect to this risk measure. We concentrate on the method proposed by Rockafellar and Uryasev in (Rockafellar, R.T. and Uryasev, S., 2000, Optimization of conditional value-at-risk. Journal of Risk, 2, 21-41), which converts this problem to that of convex optimization. We compare the use of linear programming techniques against a non-smooth optimization method of the discrete gradient, and establish the supremacy of the latter. We show that non-smooth optimization can be used efficiently for large portfolio optimization, and also examine parallel execution of this method on computer clusters.<br /

    Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana'a metropolitan city, Yemen.

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    An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana’a (Yemen) for the period 2004–2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer’s accuracy of 83.3 % and the user’s accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique “urban growth signatures”. The models generated future urban growth pattern and land use changes from the period 2004–2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands

    Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

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    This paper presents the integration of desktop grid infrastructure with GIS technologies, by proposing a parallel resolution method in a generic distributed environment. A case study focused on a discrete facility location problem, in the biomass area, exemplifies the high amount of computing resources (CPU, memory, HDD) required to solve the spatial problem. A comprehensive analysis is undertaken in order to analyse the behaviour of the grid-enabled GIS system. This analysis, consisting of a set of the experiments on the case study, concludes that the desktop grid infrastructure is able to use a commercial GIS system to solve the spatial problem achieving high speedup and computational resource utilization. Particularly, the results of the experiments showed an increase in speedup of fourteen times using sixteen computers and a computational efficiency greater than 87 % compared with the sequential procedure.This work has been developed under the support of the program Formacion de Personal Investigador, grants number BFPI/2009/103 and BES-2007-17019, from the Conselleria d'Educacio of the Generalitat Valenciana and the Spanish Ministry of Science and Technology.García García, A.; Perpiñá Castillo, C.; Alfonso Laguna, CD.; Hernández García, V. (2013). Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure. Earth Science Informatics. 6(4):199-207. https://doi.org/10.1007/s12145-013-0119-1S19920764Anderson D (2004) Boinc: a system for public-resource computing and storage. Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing. IEEE Computer Society, Washington DC, pp 4–10Available scripts webpage: http://personales.upv.es/angarg12/Campos I et al (2012) Modelling of a watershed: a distributed parallel application in a grid framework. Comput Informat 27(2):285–296Church RL (2002) Geographical information systems and location science. Comput Oper Res 29:541–562Clarke KC (1986) Advances in geographic information systems, computers. Environ Urban Syst 10:175–184Dowers S, Gittings BM, Mineter MJ (2000) Towards a framework for high-performance geocomputation: handling vector-topology within a distributed service environment. Comput Environ Urban Syst 24:471–486Geograma SL (2009). Teleatlas. http://www.geograma.com . Accessed September 2009GRASS Development Team (2012) GRASS GIS. http://grass.osgeo.org/Hoekstra AG, Sloot PMA (2005) Introducing grid speedup: a scalability metric for parallel applications on the grid, EGC 2005, LNCS 3470, pp. 245–254Hu Y et al. (2004) Feasibility study of geo-spatial analysis using grid computing. Computational Science-ICCS. Springer Berlin Heidelberg, 956–963Huang Z et al (2009) Geobarn: a practical grid geospatial database system. Adv Electr Comput Eng 9:7–11Huang F et al (2011) Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS. Comput Geosci 37:426–434Laure E et al (2006) Programming the grid with gLite. CMST 12(1):33–45Li WJ et al (2005) The Design and Implementation of GIS Grid Services. In: Zhuge H, Fox G (eds) Grid and Cooperative Computing. Vol. 3795 of Lecture Notes in Computer Science 10. Springer, Berlin, pp 220–225National Geographic Institute (2010) BCN25: numerical cartographic database. http://www.ign.es/ign/main/index.do . Accessed April 2010Open Geospatial Consortium, Inc (2012) Open GIS Specification Model, http://www.opengeospatial.org/Openshaw S, Turton I (1996) A parallel Kohonen algorithm for the classification of large spatial datasets. Comput Geosci 22:1019–1026Perpiñá C, Alfonso D, Pérez-Navarro A (2007) BIODER project: biomass distributed energy resources assessment and logistic strategies for sitting biomass plants in the Valencia province (Spain), 17th European Biomass Conference and Exhibition Proceedings, Hamburg, Germany, pp. 387–393Perpiñá C et al (2008) Methodology based on Geographic Information Systems for biomass logistics and transport optimization. Renew Energ 34:555–565Shen Z et al (2007) Distributed computing model for processing remotely sensed images based on grid computing. Inf Sci 177:504–518Spanish Ministry of Agriculture, fisheries and food (2009). http://www.magrama.gob.es/es/ . Accessed March 2009Spanish Ministry of Environment (2008). http://www.magrama.gob.es/es/ . Accessed May 2008University of California. List of BOINC projects. http://boinc.berkeley.edu/projects.phpXiao N, Fu W (2003) SDPG: Spatial data processing grid. J Comput Sci Technol 18:523–53

    A novel method for measuring patients' adherence to insulin dosing guidelines: introducing indicators of adherence

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    <p>Abstract</p> <p>Background</p> <p>Diabetic type 1 patients are often advised to use dose adjustment guidelines to calculate their doses of insulin. Conventional methods of measuring patients' adherence are not applicable to these cases, because insulin doses are not determined in advance. We propose a method and a number of indicators to measure patients' conformance to these insulin dosing guidelines.</p> <p>Methods</p> <p>We used a database of logbooks of type 1 diabetic patients who participated in a summer camp. Patients used a guideline to calculate the doses of insulin lispro and glargine four times a day, and registered their injected doses in the database. We implemented the guideline in a computer system to calculate recommended doses. We then compared injected and recommended doses by using five indicators that we designed for this purpose: absolute agreement (AA): the two doses are the same; relative agreement (RA): there is a slight difference between them; extreme disagreement (ED): the administered and recommended doses are merely opposite; Under-treatment (UT) and over-treatment (OT): the injected dose is not enough or too high, respectively. We used weighted linear regression model to study the evolution of these indicators over time.</p> <p>Results</p> <p>We analyzed 1656 insulin doses injected by 28 patients during a three weeks camp. Overall indicator rates were AA = 45%, RA = 30%, ED = 2%, UT = 26% and OT = 30%. The highest rate of absolute agreement is obtained for insulin glargine (AA = 70%). One patient with alarming behavior (AA = 29%, RA = 24% and ED = 8%) was detected. The monitoring of these indicators over time revealed a crescendo curve of adherence rate which fitted well in a weighted linear model (slope = 0.85, significance = 0.002). This shows an improvement in the quality of therapeutic decision-making of patients during the camp.</p> <p>Conclusion</p> <p>Our method allowed the measurement of patients' adherence to their insulin adjustment guidelines. The indicators that we introduced were capable of providing quantitative data on the quality of patients' decision-making for the studied population as a whole, for each individual patient, for all injections, and for each time of injection separately. They can be implemented in monitoring systems to detect non-adherent patients.</p
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