324,773 research outputs found

    Partial Correctness of a Power Algorithm

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    This work continues a formal verification of algorithms written in terms of simple-named complex-valued nominative data [6],[8],[15],[11],[12],[13]. In this paper we present a formalization in the Mizar system [3],[1] of the partial correctness of the algorithm: i := val.1 j := val.2 b := val.3 n := val.4 s := val.5 while (i n) i := i + j s := s * b return s computing the natural n power of given complex number b, where variables i, b, n, s are located as values of a V-valued Function, loc, as: loc/.1 = i, loc/.3 = b, loc/.4 = n and loc/.5 = s, and the constant 1 is located in the location loc/.2 = j (set V represents simple names of considered nominative data [17]).The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2],[4] with partial pre- and post-conditions [14],[16],[7],[5].Institute of Informatics, University of Białystok, PolandGrzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, and Karol Pąk. The role of the Mizar Mathematical Library for interactive proof development in Mizar. Journal of Automated Reasoning, 61(1):9–32, 2018. doi:10.1007/s10817-017-9440-6.R.W. Floyd. Assigning meanings to programs. Mathematical aspects of computer science, 19(19–32), 1967.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.C.A.R. Hoare. An axiomatic basis for computer programming. Commun. ACM, 12(10): 576–580, 1969.Ievgen Ivanov and Mykola Nikitchenko. On the sequence rule for the Floyd-Hoare logic with partial pre- and post-conditions. In Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kyiv, Ukraine, May 14–17, 2018, volume 2104 of CEUR Workshop Proceedings, pages 716–724, 2018.Ievgen Ivanov, Mykola Nikitchenko, Andrii Kryvolap, and Artur Korniłowicz. Simple-named complex-valued nominative data – definition and basic operations. Formalized Mathematics, 25(3):205–216, 2017. doi:10.1515/forma-2017-0020.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. Implementation of the composition-nominative approach to program formalization in Mizar. The Computer Science Journal of Moldova, 26(1):59–76, 2018.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. On an algorithmic algebra over simple-named complex-valued nominative data. Formalized Mathematics, 26(2):149–158, 2018. doi:10.2478/forma-2018-0012.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. An inference system of an extension of Floyd-Hoare logic for partial predicates. Formalized Mathematics, 26(2): 159–164, 2018. doi:10.2478/forma-2018-0013.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. Partial correctness of GCD algorithm. Formalized Mathematics, 26(2):165–173, 2018. doi:10.2478/forma-2018-0014.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. On algebras of algorithms and specifications over uninterpreted data. Formalized Mathematics, 26(2):141–147, 2018. doi:10.2478/forma-2018-0011.Artur Kornilowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. Formalization of the algebra of nominative data in Mizar. In Maria Ganzha, Leszek A. Maciaszek, and Marcin Paprzycki, editors, Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, September 3–6, 2017., pages 237–244, 2017. ISBN 978-83-946253-7-5. doi:10.15439/2017F301.Artur Kornilowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. Formalization of the nominative algorithmic algebra in Mizar. In Leszek Borzemski, Jerzy Świątek, and Zofia Wilimowska, editors, Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017 – Part II, Szklarska Poręba, Poland, September 17–19, 2017, volume 656 of Advances in Intelligent Systems and Computing, pages 176–186. Springer, 2017. ISBN 978-3-319-67228-1. doi:10.1007/978-3-319-67229-8_16.Artur Korniłowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. An approach to formalization of an extension of Floyd-Hoare logic. In Vadim Ermolayev, Nick Bassiliades, Hans-Georg Fill, Vitaliy Yakovyna, Heinrich C. Mayr, Vyacheslav Kharchenko, Vladimir Peschanenko, Mariya Shyshkina, Mykola Nikitchenko, and Aleksander Spivakovsky, editors, Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, May 15–18, 2017, volume 1844 of CEUR Workshop Proceedings, pages 504–523. CEUR-WS.org, 2017.Artur Korniłowicz, Ievgen Ivanov, and Mykola Nikitchenko. Kleene algebra of partial predicates. Formalized Mathematics, 26(1):11–20, 2018. doi:10.2478/forma-2018-0002.Andrii Kryvolap, Mykola Nikitchenko, and Wolfgang Schreiner. Extending Floyd-Hoare logic for partial pre- and postconditions. In Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, and Grygoriy Zholtkevych, editors, Information and Communication Technologies in Education, Research, and Industrial Applications: 9th International Conference, ICTERI 2013, Kherson, Ukraine, June 19–22, 2013, Revised Selected Papers, pages 355–378. Springer International Publishing, 2013. ISBN 978-3-319-03998-5. doi:10.1007/978-3-319-03998-5_18.Volodymyr G. Skobelev, Mykola Nikitchenko, and Ievgen Ivanov. On algebraic properties of nominative data and functions. In Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, and Grygoriy Zholtkevych, editors, Information and Communication Technologies in Education, Research, and Industrial Applications – 10th International Conference, ICTERI 2014, Kherson, Ukraine, June 9–12, 2014, Revised Selected Papers, volume 469 of Communications in Computer and Information Science, pages 117–138. Springer, 2014. ISBN 978-3-319-13205-1. doi:10.1007/978-3-319-13206-8_6.27218919

    Prostate functional magnetic resonance image analysis using multivariate curve resolution methods

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    This paper discusses the potential of Multivariate Curve Resolution (MCR) models to extract physiological dynamics behaviors from Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) Imaging prostate perfusion studies for cancer diagnosis. A relationship with biomarkers ( hidden parameters for assessing the possible existence of a tumor) from pharmacokinetic models is also studied.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Prats-Montalbán, JM.; Sanz Requena, R.; Marti Bonmati, L.; Ferrer, A. (2014). Prostate functional magnetic resonance image analysis using multivariate curve resolution methods. Journal of Chemometrics. 28(8):672-680. https://doi.org/10.1002/cem.2585S672680288Collins, D. J., & Padhani, A. R. (2004). Dynamic magnetic resonance imaging of tumor perfusion. IEEE Engineering in Medicine and Biology Magazine, 23(5), 65-83. doi:10.1109/memb.2004.1360410JACKSON, A. S. N., REINSBERG, S. A., SOHAIB, S. A., CHARLES-EDWARDS, E. M., JHAVAR, S., CHRISTMAS, T. J., … DEARNALEY, D. P. (2009). Dynamic contrast-enhanced MRI for prostate cancer localization. The British Journal of Radiology, 82(974), 148-156. doi:10.1259/bjr/89518905Leach, M. O., Brindle, K. M., Evelhoch, J. L., Griffiths, J. R., Horsman, M. R., Jackson, A., … Workman, P. (2005). The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. British Journal of Cancer, 92(9), 1599-1610. doi:10.1038/sj.bjc.6602550Tofts, P. S., Brix, G., Buckley, D. L., Evelhoch, J. L., Henderson, E., Knopp, M. V., … Weisskoff, R. M. (1999). Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: Standardized quantities and symbols. Journal of Magnetic Resonance Imaging, 10(3), 223-232. doi:10.1002/(sici)1522-2586(199909)10:33.0.co;2-sPort, R. E., Knopp, M. V., & Brix, G. (2001). Dynamic contrast-enhanced MRI using Gd-DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magnetic Resonance in Medicine, 45(6), 1030-1038. doi:10.1002/mrm.1137McGrath, D. M., Bradley, D. P., Tessier, J. L., Lacey, T., Taylor, C. J., & Parker, G. J. M. (2009). Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magnetic Resonance in Medicine, 61(5), 1173-1184. doi:10.1002/mrm.21959Yang, C., Karczmar, G. S., Medved, M., Oto, A., Zamora, M., & Stadler, W. M. (2009). Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magnetic Resonance in Medicine, 61(4), 851-859. doi:10.1002/mrm.21912Meng, R., Chang, S. D., Jones, E. C., Goldenberg, S. L., & Kozlowski, P. (2010). Comparison between Population Average and Experimentally Measured Arterial Input Function in Predicting Biopsy Results in Prostate Cancer. Academic Radiology, 17(4), 520-525. doi:10.1016/j.acra.2009.11.006Sourbron, S. P., & Buckley, D. L. (2011). Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Physics in Medicine and Biology, 57(2), R1-R33. doi:10.1088/0031-9155/57/2/r1Lüdemann, L., Prochnow, D., Rohlfing, T., Franiel, T., Warmuth, C., Taupitz, M., … Beyersdorff, D. (2009). Simultaneous Quantification of Perfusion and Permeability in the Prostate Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging with an Inversion-Prepared Dual-Contrast Sequence. Annals of Biomedical Engineering, 37(4), 749-762. doi:10.1007/s10439-009-9645-xPrats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Jackson, J. E. (1991). A Use’s Guide to Principal Components. Wiley Series in Probability and Statistics. doi:10.1002/0471725331Bruwer, M.-J., MacGregor, J. F., & Noseworthy, M. D. (2008). Dynamic contrast-enhanced MRI diagnostics in oncology via principal component analysis. Journal of Chemometrics, 22(11-12), 708-716. doi:10.1002/cem.1143Gujral, P., Amrhein, M., Bonvin, D., Vallée, J.-P., Montet, X., & Michoux, N. (2009). Classification of magnetic resonance images from rabbit renal perfusion. Chemometrics and Intelligent Laboratory Systems, 98(2), 173-181. doi:10.1016/j.chemolab.2009.06.004Fortuna, J., Elzibak, A. H., Fan, Z., MacGregor, J. F., & Noseworthy, M. D. (2012). Liver functional magnetic resonance imaging analysis using a latent variables approach. Journal of Chemometrics, 26(5), 170-179. doi:10.1002/cem.2430Buckley, D. L. (2002). Uncertainty in the analysis of tracer kinetics using dynamic contrast-enhancedT1-weighted MRI. Magnetic Resonance in Medicine, 47(3), 601-606. doi:10.1002/mrm.10080Henderson, E., Sykes, J., Drost, D., Weinmann, H.-J., Rutt, B. K., & Lee, T.-Y. (2000). Simultaneous MRI measurement of blood flow, blood volume, and capillary permeability in mammary tumors using two different contrast agents. Journal of Magnetic Resonance Imaging, 12(6), 991-1003. doi:10.1002/1522-2586(200012)12:63.0.co;2-1Tauler, R., Smilde, A., & Kowalski, B. (1995). Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution. Journal of Chemometrics, 9(1), 31-58. doi:10.1002/cem.1180090105Tauler, R. (1995). Multivariate curve resolution applied to second order data. Chemometrics and Intelligent Laboratory Systems, 30(1), 133-146. doi:10.1016/0169-7439(95)00047-xPiqueras, S., Duponchel, L., Tauler, R., & de Juan, A. (2011). Resolution and segmentation of hyperspectral biomedical images by Multivariate Curve Resolution-Alternating Least Squares. Analytica Chimica Acta, 705(1-2), 182-192. doi:10.1016/j.aca.2011.05.020De Juan, A., & Tauler, R. (2003). Chemometrics applied to unravel multicomponent processes and mixtures. Analytica Chimica Acta, 500(1-2), 195-210. doi:10.1016/s0003-2670(03)00724-4Jaumot, J., Gargallo, R., de Juan, A., & Tauler, R. (2005). A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB. Chemometrics and Intelligent Laboratory Systems, 76(1), 101-110. doi:10.1016/j.chemolab.2004.12.007Windig, W., & Guilment, J. (1991). Interactive self-modeling mixture analysis. Analytical Chemistry, 63(14), 1425-1432. doi:10.1021/ac00014a016Gallagher, N. B., Shaver, J. M., Martin, E. B., Morris, J., Wise, B. M., & Windig, W. (2004). Curve resolution for multivariate images with applications to TOF-SIMS and Raman. Chemometrics and Intelligent Laboratory Systems, 73(1), 105-117. doi:10.1016/j.chemolab.2004.04.003Windig, W. (2009). Two-Way Data Analysis: Detection of Purest Variables. Comprehensive Chemometrics, 275-307. doi:10.1016/b978-044452701-1.00048-xChtioui, Y., Bertrand, D., & Barba, D. (1998). Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision. Journal of the Science of Food and Agriculture, 76(1), 77-86. doi:10.1002/(sici)1097-0010(199801)76:13.0.co;2-9Wang, M., Zhou, X., King, R. W., & Wong, S. T. (2007). BMC Bioinformatics, 8(1), 32. doi:10.1186/1471-2105-8-32Sánchez, F. C., Toft, J., van den Bogaert, B., & Massart, D. L. (1996). Orthogonal Projection Approach Applied to Peak Purity Assessment. Analytical Chemistry, 68(1), 79-85. doi:10.1021/ac950496gMultivariate curve resolution homepage http://www.mcrals.info/De Juan, A., Maeder, M., Hancewicz, T., & Tauler, R. (2005). Local rank analysis for exploratory spectroscopic image analysis. Fixed Size Image Window-Evolving Factor Analysis. Chemometrics and Intelligent Laboratory Systems, 77(1-2), 64-74. doi:10.1016/j.chemolab.2004.11.006Keller, H. R., & Massart, D. L. (1991). Peak purity control in liquid chromatography with photodiode-array detection by a fixed size moving window evolving factor analysis. Analytica Chimica Acta, 246(2), 379-390. doi:10.1016/s0003-2670(00)80976-9De Juan, A., Maeder, M., Hancewicz, T., Duponchel, L., & Tauler, R. (s. f.). Chemometric Tools for Image Analysis. Infrared and Raman Spectroscopic Imaging, 65-109. doi:10.1002/9783527628230.ch2De Juan, A., Maeder, M., Hancewicz, T., & Tauler, R. (2008). Use of local rank-based spatial information for resolution of spectroscopic images. Journal of Chemometrics, 22(5), 291-298. doi:10.1002/cem.1099Orton, M. R., d’ Arcy, J. A., Walker-Samuel, S., Hawkes, D. J., Atkinson, D., Collins, D. J., & Leach, M. O. (2008). Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. 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    Modeling of macro fiber composite actuated laminate plates and aerofoils

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    © 2019 Sage Publications . The final, definitive version of this paper has been published in the Journal of Intelligent Material Systems and Structures by Sage Publications Ltd. All rights reserved. It is available at: https://doi.org/10.1177/1045389X19888728This article investigates the modeling of macro fiber composite-actuated laminate plates with distributed actuator patches. The investigation details an analytical and finite element modeling, with experimental validation of the bending strain and deflection of an epoxy E-glass fiber composite laminate. An analytical approach is also developed to estimate the plate deflection from the experimental strain measurements. The analytical method uses direct integration of single dimensional plate bending moments obtained by strain-induced shear moments from the macro fiber composite actuators. Finite element analysis software was used with the composite laminate modeled in ANSYS ACP. The results from both analytical and numerical models show good agreement with the experimental results, with strain values agreeing within 20 ppm and the maximum difference in deflection not exceeding 0.1 mm between models. Finally, an application of the analytical model for developing morphing aerofoil designs is demonstrated.Peer reviewe

    Intelligent Packaging Systems: Sensors and Nanosensors to Monitor Food Quality and Safety

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    IndexaciĂłn: Web of Science y Scopus.The application of nanotechnology in different areas of food packaging is an emerging field that will grow rapidly in the coming years. Advances in food safety have yielded promising results leading to the development of intelligent packaging (IP). By these containers, it is possible to monitor and provide information of the condition of food, packaging, or the environment. This article describes the role of the different concepts of intelligent packaging. It is possible that this new technology could reach enhancing food safety, improving pathogen detection time, and controlling the quality of food and packaging throughout the supply chain.https://www.hindawi.com/journals/js/2016/4046061/cta

    Intelligent Integrated Management for Telecommunication Networks

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    As the size of communication networks keeps on growing, faster connections, cooperating technologies and the divergence of equipment and data communications, the management of the resulting networks gets additional important and time-critical. More advanced tools are needed to support this activity. In this article we describe the design and implementation of a management platform using Artificial Intelligent reasoning technique. For this goal we make use of an expert system. This study focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We propose a new paradigm where the intelligent network management is integrated into the conceptual repository of management information called Managed Information Base (MIB). This paper outlines the development of an expert system prototype based in our propose GDMO+ standard and describes the most important facets, advantages and drawbacks that were found after prototyping our proposal
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