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Katalin Turnacker

References Arnheim, Rudolf. 1985. A film mint müvészet [Film as Art]. Budapest: Gondolat Kiadó. Balázs, Béla. 1984. A látható ember [The Visible Man]. Budapest: Gondolat Kiadó. Bíró, Yvette. 2003. Nem tiltott határátlépések [Not Forbidden Trespassings]. Budapest: Osiris Kiadó. Casetti, Francesco. 1999. Theories of Cinema, 1945-1995. Austin: University of Texas Press. Csabai, Márta and Erős Ferenc, eds. 2002. Test-beszédek: köznapi és tudományos diskurzusok a testről [Body

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Thorbjörn Broddason

Effects of Television on Children’s Leisure-Time Reading: A Test of Three Explanatory Models’, Human Communication Research, 23(1): 4-35. Kubey, Robert and Mihaly Csikszentmihalyi (1990) Television and the Quality of Life: How Viewing Shapes Everyday Experience. Hillsdale: Lawrence Erlbaum Associates, Publishers. Kubey, Robert and Mihaly Csikszentmihalyi (2002) ‘Television Addiction Is No Mere Metaphor’, Scientific American Feature Article, http://www.sciam.com/2002/0202issue/0202kubey.html (accessed December 20, 2005

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Martina Ohlídalová, Karel Křenek, Jana Tvrzníková, Michal Pech and Radka Šefců

Abstract

In 2017, the National Museum commemorated the bicentenary of the discovery of the Manuscript of Dvůr Králové and the Manuscript of Zelená Hora by further material research into both works and especially by an exhibition of their originals. The main aims of this research into the manuscripts included the documentation and evaluation of their current physical condition and the mapping of the effect of the microchemical analyses performed in the context of the disputes over the authenticity of the manuscripts between the middle of the 19th century and the 1970s. For the achievement of these objectives, a detailed documentation of all the pages of the manuscripts in different types of lighting (visible direct, lateral, transmitted, ultraviolet, infrared), optical microscopy, and the identification of the degradation productions of damaged places by means of X-ray fluorescence analysis and Raman spectroscopy were used. This provided new information on the current physical condition of the manuscripts and documentation of the damage caused by historical microchemical testing. In addition, some previously unpublished historical tests were identified, thus offering a new perspective on some current damage of the two manuscripts.

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Jin Zhang, Yanyan Wang, Yuehua Zhao and Xin Cai

regarded as major factors in determining the quality of studies. There are multiple categories of statistical methods, such as inferential statistical methods, predictive statistical methods, parametric statistical methods, and nonparametric statistical methods. Each category contains a lot of statistical approaches. For instance, ANOVA test is an inferential statistical method, while Pearson’s correlation is a predictive statistical method. These various statistical methods have different characteristics and are used differently in studies. Therefore, it is meaningful

Open access

Xiao Hu

1 Introduction Studies of cultural heritages are invaluable in many disciplines including archeology, history, anthropology, linguistics, and religious studies. Among tangible cultural heritage (i.e., those with physical presence), many are non-movable such as monuments, architecture, and archeological sites. To observe non-movable heritages, people have to travel to the heritage site. Moreover, most non-movable heritages are exposed to the natural environment with limited protection, suffering from deformation and deterioration caused by weather or other

Open access

Dar-Zen Chen and Chien-Hsiang Chou

were the main transition patterns analyzed in this study. Because business markets are competitive, assuming proper control of patent characteristics related to transition patterns is desired by diverse types of stakeholders to potentially facilitate monitoring and predicting rivals’ transition patterns at early stages and to provide valuable assistance in adapting strategies ( Aharonson & Schilling, 2016 ). A series of statistical testing procedures was used to conduct a preliminary study to gain insight into the significance and directionality of technology

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Maria Esteva, Ramona L. Walls, Andrew B. Magill, Weijia Xu, Ruizhu Huang, James Carson and Jawon Song

ontological efforts to describe biological data ( Smith et al., 2007 ). Our solution was to devise a generic data model to accommodate all our test cases and many more. Our generic data model ( Fig. 1A) has three key components: 1) processual entities, which include things such as collecting specimens, carrying out assays, and analyzing data; 2) material entities such as physical specimens, reagents, and probes; and 3) data entities, including both individual data objects and datasets. We include a project entity as the umbrella under which the other entities are grouped

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Will R. Thomas, Benjamin Galewsky, Sandeep Puthanveetil Satheesan, Gregory Jansen, Richard Marciano, Shannon Bradley, Jong Lee, Luigi Marini and Kenton McHenry

folder number for the underlying physical record, filename and path assigned at the time of capture, SHA256 fixity information, and format information captured by the identity program in the ImageMagick suite. Figure 1 Storage Pool. C = Cassandra Figure 2 Expanded Storage Pool. We rely on Apache Spark ( Zaharia et al., 2016 ) to distribute the training of the machine-learning models. Training and testing data are stored in Cassandra, as Spark integrates with it as a persistence layer. Although Spark has an ML library (Meng, et al., 2016), the

Open access

Danchen Zhang and Daqing He

. Esfandiari et al. [ 11 ] gave a comprehensive review of the studies in this area. According to their summarization, most studies regarded diagnosis prediction as a classification task. For example, Yeh et al. [ 12 ] used the history of patients’ diseases, blood test results, and physical examination results as the features and used trained classifiers to predict the probability of getting a cerebrovascular disease. Besides, other studies tried to predict the diagnosis with regression methods, clustering methods, association rules, or hybrid systems [ 11 ]. For example

Open access

Liang Hong, Mengqi Luo, Ruixue Wang, Peixin Lu, Wei Lu and Long Lu

physical health data based on artificial ant colony optimization. This method is determined through testing to be an effective and efficient approach to clustering health and medical data for further analysis. Paul and Hoque (2010) proposed to use the background knowledge of medical domain in the clustering process to predict the likelihood of diseases. The developed algorithm can handle both continuous and discrete data and perform clustering based on anticipated likelihood attributes with core attributes of disease in data point. In this paper, its effectiveness has