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Littérature scientifique sur le sujet « Discovery from data »

Auteur : Grafiati

Publié le 10 mars 2023

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Articles de revues sur le sujet "Discovery from data"

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Mak, H. Craig. "Discovery from data repositories." Nature Biotechnology 29, no. 1 (2011): 46–47. http://dx.doi.org/10.1038/nbt0111-46.

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Pazzani, M. J. "Knowledge discovery from data?" IEEE Intelligent Systems 15, no. 2 (2000): 10–12. http://dx.doi.org/10.1109/5254.850821.

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Gama, João, and Jesus Aguilar-Ruiz. "Knowledge discovery from data streams." Intelligent Data Analysis 11, no. 1 (2007): 1–2. http://dx.doi.org/10.3233/ida-2007-11101.

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Gama, João, Jesus Aguilar-Ruiz, and Ralf Klinkenberg. "Knowledge discovery from data streams." Intelligent Data Analysis 12, no. 3 (2008): 251–52. http://dx.doi.org/10.3233/ida-2008-12301.

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Gama, João, Auroop Ganguly, Olufemi Omitaomu, Raju Vatsavai, and Mohamed Gaber. "Knowledge discovery from data streams." Intelligent Data Analysis 13, no. 3 (2009): 403–4. http://dx.doi.org/10.3233/ida-2009-0372.

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Morita, Chie, and Hiroshi Tsukimoto. "Knowledge discovery from numerical data." Knowledge-Based Systems 10, no. 7 (1998): 413–19. http://dx.doi.org/10.1016/s0950-7051(98)00040-9.

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Cook, Diane J., Lawrence B. Holder, and Surnjani Djoko. "Knowledge discovery from structural data." Journal of Intelligent Information Systems 5, no. 3 (1995): 229–48. http://dx.doi.org/10.1007/bf00962235.

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Kalenkova, Anna, Andrea Burattin, Massimiliano de Leoni, Wil van der Aalst, and Alessandro Sperduti. "Discovering high-level BPMN process models from event data." Business Process Management Journal 25, no. 5 (2019): 995–1019. http://dx.doi.org/10.1108/bpmj-02-2018-0051.

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Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.

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Gottlob, Georg, and Pierre Senellart. "Schema mapping discovery from data instances." Journal of the ACM 57, no. 2 (2010): 1–37. http://dx.doi.org/10.1145/1667053.1667055.

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Vatsavai, Ranga Raju, Olufemi A. Omitaomu, Joao Gama, Nitesh V. Chawla, Mohamed Medhat Gaber, and Auroop R. Ganguly. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 10, no. 2 (2008): 68–73. http://dx.doi.org/10.1145/1540276.1540297.

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Thèses sur le sujet "Discovery from data"

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Höppner, Frank. "Knowledge discovery from sequential data." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=96728421X.

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Cao, Huiping. "Pattern discovery from spatiotemporal data." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37381520.

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Cao, Huiping, and 曹會萍. "Pattern discovery from spatiotemporal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37381520.

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Chau, Tom. "Event level pattern discovery in multivariate continuous data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0003/NQ30594.pdf.

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El, Sayed Ahmed. "Contributions in knowledge discovery from textual data." Lyon 2, 2008. http://theses.univ-lyon2.fr/documents/lyon2/2008/el-sayed_a.

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This dissertation focuses on two key issues in text mining, namely unsupervised learning and knowledge acquisition. In spite of their relative maturity, both issues still present some major challenges that need to be addressed. First, for unsupervised learning, a well-known, unresolved challenge is to perform clustering with minimal input parameters. One natural way to reach this is to involve validity indices in the clustering process. Although of great interest, validity indices were not extensively explored in the literature, especially when dealing with high-dimensional data like text. Hence, we make three main contributions: (1) an experimental study comparing extensively 8 validity indices; (2) a context-aware method enhancing validity indices usage as stopping criteria; (3) I-CBC, an Incremental version of the CBC (Clustering By Committee) algorithm. Contributions were validated in two real-world applications: document and word clustering. Second, for knowledge acquisition, we face major issues related to ontology learning from text: low recall of the pattern-based approach, low precision of the distributional approach, context-dependency, and ontology evolution. Thus, we propose a new framework for taxonomy learning from text. The proposal is a hybrid approach which has the following advantages over the other approaches: (1) ability to capture more “flexibly” relations in text; (2) concepts better reflecting the context of the target corpus; (3) more reliable decisions during the learning process; (4) and finally evolution of the learned taxonomy without any manual effort, after its incorporation in a core of an information retrieval system<br>Cette thèse se focalise sur deux problématiques clés liées à la fouille de texte, à savoir : la classification et l'acquisition des connaissances. En dépit de leur relative maturité, ces deux problématiques présentent encore certains défis majeurs qui doivent être soulevés. En premier lieu, pour la classification, un défi bien connu et non résolu consiste à effectuer des classifications avec un minimum de paramètres en entrée. Une façon naturelle de parvenir à cette fin, est d'utiliser les indices de validité dans le processus de classification. Bien qu'ils soient d'un grand intérêt, les indices de validité n'ont pas été largement explorés dans la littérature, en particulier lorsqu'il s'agit de données de grande dimension, comme c'est le cas des données textuelles. Ainsi, concernant ce volet, nous proposons trois principales contributions : (1) une large étude expérimentale comparant huit indices de validité, (2) une méthode basée sur le contexte améliorant l'utilisation des indices de validité en tant que critère d'arrêt, (3) I-CBC, une version incrémentale de l'algorithme flou CBC (classification par comités). Ces contributions ont été validées sur deux applications du monde réel : la classification de documents et de mots. En deuxième lieu, pour l’acquisition des connaissances, nous nous sommes intéressés à des problématiques importantes liées à la construction d’ontologies à partir de texte : le faible rappel des approches basées sur les patrons, la faible précision de l’approche distributionnelle, la dépendance au contexte et l’évolution des ontologies. Nous proposons ainsi, un nouveau cadre pour l’apprentissage d’ontologies à partir du texte. Notre proposition est une approche hybride qui combine les avantages suivants par rapport aux autres approches : (1) la capacité de capturer avec plus de flexibilité des relations dans le texte, (2) des concepts qui traduisent mieux le contexte du corpus considéré, (3) des décisions plus fiables prises durant le processus d’apprentissage à travers la considération et l’inclusion de plusieurs relations sémantiques, et, enfin, (4) l’évolution de l’ontologie apprise sans aucun effort manuel considérable, après son inclusion au coeurd’un système de recherche d’information

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El, Sayed Ahmed Zighed Djamel Abdelkader. "Contributions in knowledge discovery from textual data." Lyon : Université Lumière Lyon 2, 2008. http://theses.univ-lyon2.fr/sdx/theses/lyon2/2008/el-sayed_a.

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Wang, Yang. "High-order pattern discovery and analysis of discrete-valued data sets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22245.pdf.

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Amado, Vanessa. "Knowledge discovery and data mining from freeway section traffic data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5591.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 8, 2009) Vita. Includes bibliographical references.

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PaÌirceÌir, RoÌnaÌn. "Knowledge discovery from distributed aggregate data in data warehouses and statistical databases." Thesis, University of Ulster, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274398.

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Caruccio, Loredana. "Relaxed functional dependencies: definition, discovery and applications." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3051.

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2016 - 2017<br>Functional dependencies (FDs) were conceived in the early '70s, and were mainly used to verify database design and assess data quality. However, to solve several issues in emerging application domains, such as the identification of data inconsistencies, patterns of semantically related data, query rewriting, and so forth, it has been necessary to extend the FD definition... [edited by author]<br>XVI n.s.

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Livres sur le sujet "Discovery from data"

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Gaber, Mohamed Medhat, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, and Auroop R. Ganguly, eds. Knowledge Discovery from Sensor Data. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12519-5.

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R, Ganguly Auroop, ed. Knowledge discovery from sensor data. Taylor & Francis, 2009.

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Knowledge discovery from data streams. Chapman & Hall/CRC, 2010.

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Ye, Chen, Hongzhi Wang, and Guojun Dai. Knowledge Discovery from Multi-Sourced Data. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1879-7.

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Bandyopadhyay, Sanghamitra, Ujjwal Maulik, Lawrence B. Holder, and Diane J. Cook. Advanced Methods for Knowledge Discovery from Complex Data. Springer London, 2005. http://dx.doi.org/10.1007/1-84628-284-5.

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Páircéir, Rónán. Knowledge discovery from distributed aggregate data in data warehouses and statistical databases. The Author], 2002.

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1966-, Ghosh Ashish, Dehuri Satchidananda, and Ghosh Susmita, eds. Multi-objective evolutionary algorithms for knowledge discovery from databases. Springer, 2008.

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Read, J. F. CTD data from the north east Atlantic, April 1989, collected on RRS Discovery Cruise 181. Institute of Oceanographic Sciences, Deacon Laboratory, 1991.

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Marchese, Francis T. Knowledge Visualization Currents: From Text to Art to Culture. Springer London, 2013.

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Suit, William T. Lateral and longitudinal stability and control parameters for the space shuttle Discovery as determined from flight test data. National Aeronautics and Space Administration, Langley Research Center, 1988.

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Chapitres de livres sur le sujet "Discovery from data"

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Riddle, Pat, Roman Fresnedo, and David Newman. "Framework for a Generic Knowledge Discovery Toolkit." In Learning from Data. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2404-4_33.

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Ikonomovska, Elena, and Joao Gama. "Learning Model Trees from Data Streams." In Discovery Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88411-8_8.

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Hasegawa, Hiroshi H., Takashi Washio, and Yukari Ishimiya. "“Thermodynamics” from Time Series Data Analysis." In Discovery Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46846-3_33.

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George, Dileep, Kazumi Saito, Pat Langley, Stephen Bay, and Kevin R. Arrigo. "Discovering Ecosystem Models from Time-Series Data." In Discovery Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39644-4_13.

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Adhikari, Prem Raj, and Jaakko Hollmén. "Mixture Models from Multiresolution 0-1 Data." In Discovery Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40897-7_1.

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Hasegawa, H. H., T. Washio, Y. Ishimiya, and T. Saito. "Nonequilibrium Thermodynamics from Time Series Data Analysis." In Discovery Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44418-1_35.

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Trabold, Daniel, and Tamás Horváth. "Mining Strongly Closed Itemsets from Data Streams." In Discovery Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67786-6_18.

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Rinzivillo, S., F. Turini, V. Bogorny, C. Körner, B. Kuijpers, and M. May. "Knowledge Discovery from Geographical Data." In Mobility, Data Mining and Privacy. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75177-9_10.

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Morita, Chie, and Hiroshi Tsukimoto. "Knowledge Discovery from Numerical Data." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-73.

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Pazouki, Ehsan. "Knowledge Discovery from Agricultural Data." In Encyclopedia of Smart Agriculture Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_263-1.

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Actes de conférences sur le sujet "Discovery from data"

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Wan, Mengting, Xiangyu Chen, Lance Kaplan, Jiawei Han, Jing Gao, and Bo Zhao. "From Truth Discovery to Trustworthy Opinion Discovery." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. http://dx.doi.org/10.1145/2939672.2939837.

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Metwally, Ahmed, Jia-Yu Pan, Minh Doan, and Christos Faloutsos. "Scalable community discovery from multi-faceted graphs." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363859.

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Paun, S., M. Fisher, P. Mckee, M. Poesio, and U. Kruschwitz. "Pattern discovery in big data streams." In IET Seminar on Data Analytics 2013: Deriving Intelligence and Value from Big Data. Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0233.

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Guo, Zhen, Zhongfei Zhang, Shenghuo Zhu, Yun Chi, and Yihong Gong. "Knowledge Discovery from Citation Networks." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.137.

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Yu, Kui, Xindong Wu, Hao Wang, and Wei Ding. "Causal Discovery from Streaming Features." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.82.

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Burl, M. C., D. DeCoste, B. L. Enke, D. Mazzoni, W. J. Merline, and L. Scharenbroich. "Automated Knowledge Discovery from Simulators." In Proceedings of the 2006 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.8.

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Hashemi, Ray R., Charles Epperson, Alexander A. Tyler, and John F. Young. "Knowledge discovery from sparse pharmacokinetic data." In the 2000 ACM symposium. ACM Press, 2000. http://dx.doi.org/10.1145/335603.335699.

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Panigrahi, Sangram, Kesari Verma, Priyanka Tripathi, and Rika Sharma. "Knowledge Discovery from Earth Science Data." In 2014 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2014. http://dx.doi.org/10.1109/csnt.2014.85.

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Osipov, Gennady S. "Workflows and Their Discovery from Data." In 2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems. IEEE, 2007. http://dx.doi.org/10.1109/kimas.2007.369835.

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SHIMIZU, AKIFUMI, and KENTARO YANO. "GENE DISCOVERY METHODS FROM LARGE-SCALE GENE EXPRESSION DATA." In From Quantum Information to Bio-Informatics. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304061_0040.

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Rapports d'organisations sur le sujet "Discovery from data"

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Ng, Andrew Y., and Christopher D. Manning. Discovery of Deep Structure from Unlabeled Data. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada614158.

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Capraro, Gerard T., and Gerald B. Berdan. Intensive Knowledge Discovery from Heterogeneous Distributed Data and Knowledge. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada397959.

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Peterka, Tom, Deborah Bard, Janine Bennett, et al. ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493245.

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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.

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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.

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Brosh, Arieh, Gordon Carstens, Kristen Johnson, et al. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7592644.bard.

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Feed inputs represent the largest variable cost of producing meat and milk from ruminant animals. Thus, strategies that improve the efficiency of feed utilization are needed to improve the global competitiveness of Israeli and U.S. cattle industries, and mitigate their environmental impact through reductions in nutrient excretions and greenhouse gas emissions. Implementation of innovative technologies that will enhance genetic merit for feed efficiency is arguably one of the most cost-effective strategies to meet future demands for animal-protein foods in an environmentally sustainable manner. While considerable genetic variation in feed efficiency exist within cattle populations, the expense of measuring individual-animal feed intake has precluded implementation of selection programs that target this trait. Residual feed intake (RFI) is a trait that quantifies between-animal variation in feed intake beyond that expected to meet energy requirements for maintenance and production, with efficient animals being those that eat less than expected for a given size and level of production. There remains a critical need to understand the biological drivers for genetic variation in RFI to facilitate development of effective selection programs in the future. Therefore, the aim of this project was to determine the biological basis for phenotypic variation in RFI of growing and lactating cattle, and discover metabolic biomarkers of RFI for early and more cost-effective selection of cattle for feed efficiency. Objectives were to: (1) Characterize the phenotypic relationships between RFI and production traits (growth or lactation), (2) Quantify inter-animal variation in residual HP, (3) Determine if divergent RFIphenotypes differ in HP, residual HP, recovered energy and digestibility, and (4) Determine if divergent RFI phenotypes differ in physical activity, feeding behavior traits, serum hormones and metabolites and hepatic mitochondrial traits. The major research findings from this project to date include: In lactating dairy cattle, substantial phenotypic variation in RFI was demonstrated as cows classified as having low RMEI consumed 17% less MEI than high-RMEI cows despite having similar body size and lactation productivity. Further, between-animal variation in RMEI was found to moderately associated with differences in RHP demonstrating that maintenance energy requirements contribute to observed differences in RFI. Quantifying energetic efficiency of dairy cows using RHP revealed that substantial changes occur as week of lactation advances—thus it will be critical to measure RMEI at a standardized stage of lactation. Finally, to determine RMEI in lactating dairy cows, individual DMI and production data should be collected for a minimum of 6 wk. We demonstrated that a favorably association exists between RFI in growing heifers and efficiency of forage utilization in pregnant cows. Therefore, results indicate that female progeny from parents selected for low RFI during postweaning development will also be efficient as mature females, which has positive implications for both dairy and beef cattle industries. Results from the beef cattle studies further extend our knowledge regarding the biological drivers of phenotypic variation in RFI of growing animals, and demonstrate that significant differences in feeding behavioral patterns, digestibility and heart rate exist between animals with divergent RFI. Feeding behavior traits may be an effective biomarker trait for RFI in beef and dairy cattle. There are differences in mitochondrial acceptor control and respiratory control ratios between calves with divergent RFI suggesting that variation in mitochondrial metabolism may be visible at the genome level. Multiple genes associated with mitochondrial energy processes are altered by RFI phenotype and some of these genes are associated with mitochondrial energy expenditure and major cellular pathways involved in regulation of immune responses and energy metabolism.

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Sloan, Steven, Shelby Peterie, Richard Miller, Julian Ivanov, J. Schwenk, and Jason McKenna. Detecting clandestine tunnels by using near-surface seismic techniques. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40419.

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Geophysical detection of clandestine tunnels is a complex problem that has been met with limited success. Multiple methods have been applied spanning several decades, but a reliable solution has yet to be found. This report presents shallow seismic data collected at a tunnel test site representative of geologic settings found along the southwestern U.S. border. Results demonstrate the capability of using compressional wave diffraction and surface-wave backscatter techniques to detect a purpose-built subterranean tunnel. Near-surface seismic data were also collected at multiple sites in Afghanistan to detect and locate subsurface anomalies (e.g., data collected over an escape tunnel discovered in 2011 at the Sarposa Prison in Kandahar, Afghanistan, which allowed more than 480 prisoners to escape, and data from another shallow tunnel recently discovered at an undisclosed location). The final example from Afghanistan is the first time surface-based seismic methods have detected a tunnel whose presence and location were not previously known. Seismic results directly led to the discovery of the tunnel. Interpreted tunnel locations for all examples were less than 2 m of the actual location. Seismic surface wave backscatter and body-wave diffraction methods show promise for efficient data acquisition and processing for locating purposefully hidden tunnels within unconsolidated sediments.

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.

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Floyd, Jason, and Daniel Madrzykowski. Analysis of a Near Miss in a Garden Apartment Fire – Georgia 2022. UL's Fire Safety Research Institute, 2022. http://dx.doi.org/10.54206/102376/rsfd6862.

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On February 9, 2022, Cobb County Fire and Emergency Services responded to a fire in a ground floor unit in a garden apartment building. At arrival, the fire was a post-flashover fire in a bedroom. Initial fire control was attempted by an interior fire attack team which was unable to quickly locate the fire. Exterior suppression through the bedroom window was started prior to discovery of the fire by the interior team. Shortly after fire discovery by the internal team, a mayday was called. Four firefighters from the interior fire attack team received first and second degree burns. This report analyzes photographic, video, and written documentation from the incident to evaluate the timeline of the incident and to assess the fire conditions present. Computer modeling using the Fire Dynamics Simulator (FDS) was performed to provide further insight into the fire conditions and the impact of decisions and actions on the fire ground. Additionally, data from a full scale fire test of a similar fire in a similar structure was used to provide additional insight. Four FDS simulations were performed in support of the analysis. These included a simulation of the event as it unfolded and three simulations looking at the impact of alternate tactics which included: initial exterior attack prior to entry, the use of a smoke curtain to protect the building exit path, and interior only attack. FDS simulations provided insight on the heat present in the apartment during the fire and the impact of the interior and exterior suppression on conditions inside the apartment. Full scale test data of a similar fire showed similar behavior to the FDS predictions and gives credence to the FDS results. Results of the analysis suggest that injuries resulted from the length of time the interior attack team was present inside the apartment before actions were taken to reduce the severity of the fire. Six contributing factors were identified including size-up, communication and accountability, delayed exterior attack, lack of entry hall protection, the apartment layout and construction, thermal imager use, and mayday procedures and training. The last contributing factor was a positive contribution that helped avoid more serious injuries. Based on the contributing factors, five recommendations were made that include improved size-up, exterior fire control to prevent exterior spread, protection of exit pathways, basing fire ground tactics on known information, and recognizing when a change in tactics is needed.

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Ma, Yunxing, Julia Brettschneider, and Joanna Collingwood. A systematic review and meta-analysis of cerebrospinal fluid amyloid and tau levels in patients progressing from Mild Cognitive Impairment to Alzheimer’s Disease. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.7.0020.

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Review question / Objective: Reported levels of amyloid-beta and tau in human cerebrospinal fluid (CSF) are evaluated to discover if these biochemical markers can predict the transition from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD). A systematic review and quantitative meta-analyses are performed to test relationships between three potential biomarkers in CSF (Aβ(1-42), T-tau, and P-tau181) and the evolution of AD in longitudinal evaluations of levels relative to baseline, using prior-published experimental data. The primary focus of the analysis is on the period describing the transition of a patient from MCI to AD, where it is critical to discover the main biomarker characteristics that differentiate patient outcomes for those who have a stable form of MCI, and those who progress to a confirmed diagnosis of AD. A secondary purpose of the review was to examine the status of iron in CSF as a function of disease status.

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