Relevant bibliographies by topics / Interaction analysis

Academic literature on the topic 'Interaction analysis'

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Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Interaction analysis"

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Bataeva, Ekaterina V. "Аction-analysis and interaction-analysis of cybercommunication." Sociological Journal 21, no. 1 (2015): 6–22. http://dx.doi.org/10.19181/socjour.2015.21.1.1247.

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Han, Ying, Liang Cheng, and Weiju Sun. "Analysis of Protein-Protein Interaction Networks through Computational Approaches." Protein & Peptide Letters 27, no. 4 (March 17, 2020): 265–78. http://dx.doi.org/10.2174/0929866526666191105142034.

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The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.
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JOHNSTON, RICHARD D., and GEOFFREY W. BARTON. "Structural interaction analysis." International Journal of Control 41, no. 4 (April 1985): 1005–13. http://dx.doi.org/10.1080/0020718508961179.

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Malmqvist, Magnus, and Russ Granzow. "Biomolecular Interaction Analysis." Methods 6, no. 2 (June 1994): 95–98. http://dx.doi.org/10.1006/meth.1994.1012.

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Gambari, Roberto. "Biospecific Interaction Analysis." American Journal of PharmacoGenomics 1, no. 2 (2001): 119–35. http://dx.doi.org/10.2165/00129785-200101020-00005.

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Stockley, Peter G. "Biomolecular interaction analysis." Trends in Biotechnology 14, no. 2 (February 1996): 39–41. http://dx.doi.org/10.1016/0167-7799(96)80916-4.

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HAYASHI, YOSHIHARU, MIME KOBAYASHI, KATSUYOSHI SAKAGUCHI, NAO IWATA, MASAKI KOBAYASHI, YO KIKUCHI, and YOSHIMASA TAKAHASHI. "PROTEIN CLASSIFICATION USING COMPARATIVE MOLECULAR INTERACTION PROFILE ANALYSIS SYSTEM." Journal of Bioinformatics and Computational Biology 02, no. 03 (September 2004): 497–510. http://dx.doi.org/10.1142/s0219720004000703.

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We recently introduced a new molecular description factor, interaction profile Factor (IPF) that is useful for evaluating molecular interactions. IPF is a data set of interaction energies calculated by the Comparative Molecular Interaction Profile Analysis system (CoMIPA). CoMIPA utilizes AutoDock 3.0 docking program, and the system has shown to be a powerful tool in clustering the interacting properties between small molecules and proteins. In this report, we describe the application of CoMIPA for protein clustering. A sample set of 15 proteins that share less than 20% homology and have no common functional motifs in primary structure were chosen. Using CoMIPA, we were able to cluster proteins that bound to the same small molecule. Other structural homology-based clustering programs such as PSI-BLAST or PFAM were unable to achieve the same classification. The results are striking because it is difficult to find any common features in the active sites of these proteins that share the same ligand. CoMIPA adds new dimensions for protein classification and has the potential to be a helpful tool in predicting and analyzing molecular interactions.
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Lin, C. Y., and C. S. Lin. "Investigation of genotype-environment interaction by cluster analysis in animal experiments." Canadian Journal of Animal Science 74, no. 4 (December 1, 1994): 607–12. http://dx.doi.org/10.4141/cjas94-089.

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The conventional ANOVA (F ratio of GE interaction mean squares to error mean square) provides a means to test if GE interaction is significant, but it does not tell us which factor levels are significantly different or how they are interacting. To answer the latter question, plant researchers developed a technique to group genotypes for similarity of GE interactions and through the resulting groups to explore the GE interaction structure. The basic idea of the technique is to stratify genotypes (or environments) into subgroups such that GE interactions among genotypes (or environments) are homogeneous within groups but heterogeneous among groups. This technique is introduced in this paper using an animal experiment as an example for illustration. The possibilities and limitations of applying this technique to animal data are also discussed. Key words: Genotype-environment interaction, cluster analysis
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Kang, Seoktae, and Sunyoung Kim. "In visual arts using VR: An interaction analysis interaction analysis." Korean Society of Culture and Convergence 42, no. 5 (May 30, 2020): 597–620. http://dx.doi.org/10.33645/cnc.2020.05.42.5.597.

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Lin, H. J., M. S. Lin, P. S. Ruan, S. W. Chen, J. D. Lee, and J. R. Wang. "Transient Pressure Analysis and Air-water Interaction in Churn Flow." International Journal of Materials, Mechanics and Manufacturing 6, no. 6 (December 2018): 397–401. http://dx.doi.org/10.18178/ijmmm.2018.6.6.415.

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Dissertations / Theses on the topic "Interaction analysis"

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Telliskivi, Tanel. "Wheel-rail Interaction Analysis." Doctoral thesis, KTH, Machine Design, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3532.

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A general approach to numerically simulating wear in rollingand sliding contacts is presented in this thesis. A simulationscheme is developed that calculates the wear at a detailedlevel. The removal of material follows Archard’s wear law,which states that the reduction of volume is linearlyproportional to the sliding distance, the normal load and thewear coefficient. The target application is the wheel-railcontact.

Careful attention is paid to stress properties in the normaldirection of the contact. A Winkler method is used to calculatethe normal pressure. The model is calibrated either withresults from Finite Element simulations (which can include aplastic material model) or a linear-elastic contact model. Thetangential tractions and the sliding distances are calculatedusing a method that incorporates the effect of rigid bodymotion and tangential deformations in the contact zone.Kalker’s Fastsim code is used to validate the tangentialcalculation method. Results of three different sorts ofexperiments (full-scale, pin-on-disc and disc-on-disc) wereused to establish the wear and friction coefficients underdifferent operating conditions.

The experimental results show that the sliding velocity andcontact pressure in the contact situation strongly influencethe wear coefficient. For the disc-on-disc simulation, therewas good agreement between experimental results and thesimulation in terms of wear and rolling friction underdifferent operating conditions. Good agreement was alsoobtained in regard to form change of the rollers. In thefull-scale simulations, a two-point contact was analysed wherethe differences between the contacts on rail-head to wheeltread and rail edge to wheel flange can be attributed primarilyto the relative velocity differences in regard to bothmagnitude and direction. Good qualitative agreement was foundbetween the simulated wear rate and the full-scale test resultsat different contact conditions.

Keywords:railway rail, disc-on-disc, pin-on-disc,Archard, wear simulation, Winkler, rolling, sliding

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Hanook, Sharoon. "Analysis of Removable Interaction." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1413761250.

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Dowling, Michelle Veronica. "Semantic Interaction for Symmetrical Analysis and Automated Foraging of Documents and Terms." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104682.

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Sensemaking tasks, such as reading many news articles to determine the truthfulness of a given claim, are difficult. These tasks require a series of iterative steps to first forage for relevant information and then synthesize this information into a final hypothesis. To assist with such tasks, visual analytics systems provide interactive visualizations of data to enable faster, more accurate, or more thorough analyses. For example, semantic interaction techniques leverage natural or intuitive interactions, like highlighting text, to automatically update the visualization parameters using machine learning. However, this process of using machine learning based on user interaction is not yet well defined. We begin our research efforts by developing a computational pipeline that models and captures how a system processes semantic interactions. We then expanded this model to denote specifically how each component of the pipeline supports steps of the Sensemaking Process. Additionally, we recognized a cognitive symmetry in how analysts consider data items (like news articles) and their attributes (such as terms that appear within the articles). To support this symmetry, we also modeled how to visualize and interact with data items and their attributes simultaneously. We built a testbed system and conducted a user study to determine which analytic tasks are best supported by such symmetry. Then, we augmented the testbed system to scale up to large data using semantic interaction foraging, a method for automated foraging based on user interaction. This experience enabled our development of design challenges and a corresponding future research agenda centered on semantic interaction foraging. We began investigating this research agenda by conducting a second user study on when to apply semantic interaction foraging to better match the analyst's Sensemaking Process.
Doctor of Philosophy
Sensemaking tasks such as determining the truthfulness of a claim using news articles are complex, requiring a series of steps in which the relevance of each piece of information within the articles is first determined. Relevant pieces of information are then combined together until a conclusion may be reached regarding the truthfulness of the claim. To help with these tasks, interactive visualizations of data can make it easier or faster to find or combine information together. In this research, we focus on leveraging natural or intuitive interactions, such organizing documents in a 2-D space, which the system uses to perform machine learning to automatically adjust the visualization to better support the given task. We first model how systems perform such machine learning based on interaction as well as model how each component of the system supports the user's sensemaking task. Additionally, we developed a model and accompanying testbed system for simultaneously evaluating both data items (like news articles) and their attributes (such as terms within the articles) through symmetrical visualization and interaction methods. With this testbed system, we devised and conducted a user study to determine which types of tasks are supported or hindered by such symmetry. We then combined these models to build an additional testbed system that implemented a searching technique to automatically add previously unseen, relevant pieces of information to the visualization. Using our experience in implementing this automated searching technique, we defined design challenges to guide future implementations, along with a research agenda to refine the technique. We also devised and conducted another user study to determine when such automated searching should be triggered to best support the user's sensemaking task.
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Sarker, Biswajit. "Organized Chaos! : Untangling multigenerational group interactions in a gamified science center." Thesis, Uppsala universitet, Människa-datorinteraktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263555.

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This inductive study investigates interactions within groups of visitors during a science center visit. Using simplified interaction analysis of recorded videos; I explore the group dynamics in terms of what determines who takes the lead while multigenerational groups interact with different types of experiments. From the observations, I suggest that the age of different group members and specific design aspects of the experiments play the most important roles in the emergence of leadership. Teenagers in a group tend to take the leadership and dominate during a group interaction, while young children like to explore freely leading the group from one experiment to the next without focusing on finishing them properly. As for the design aspects, if an experiment requires cognitive skills then adults and teenagers take the lead but if an experiment requires physical skills and provides immediate feedback then young children take the lead. I also suggest, instead of guiding the young children in the group, adults tend to become observers during engagements. This study will be useful for researchers and interaction designers who are focusing their work on the behavior of multigenerational groups in science center or museum settings.
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Lan, Chinchun. "Analysis of soil-root interaction." Connect to resource, 1985. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1119365654.

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Khouly, Mohamed A. "Analysis of soil-reinforcement interaction /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487863429092366.

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Campagna, Anne. "Structural analysis of protein interaction networks." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/84111.

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Interactions between proteins give rise to many functions in cells. In the lastdecade, highthroughput experiments have identified thousands of protein interactions, which are often represented together as large protein interaction networks. However, the classical way of representing interaction networks, as nodes and edges, is too limited to take dynamic properties such as compatible and mutually exclusive interactions into account. In this work, we study protein interaction networks using structural information. More specifically, the analysis of protein interfaces in threedimensional protein structures enables us to identify which interfaces are compatible and which are not. Based on this principle, we have implemented a method, which aims at the analysis of protein interaction networks from a structural point of view by (1) predicting possible binary interactions for proteins that have been found in complex experimentally and (2) identifying possible mutually exclusive and compatible complexes. We validated our method by using positive and negative reference sets from literature and set up an assay to benchmark the identification of compatible and mutually exclusive structural interactions. In addition, we reconstructed the protein interaction network associated with the G proteincoupled receptor Rhodopsin and defined related functional submodules by combining interaction data with structural analysis of the network. Besides its established role in vision, our results suggest that Rhodopsin triggers two additional signaling pathways towards (1) cytoskeleton dynamics and (2) vesicular trafficking.
Las funciones de las proteínas resultan de la manera con la que interaccionan entre ellas. Los experimentos de alto rendimiento han permitido identificar miles de interacciones de proteínas que forman parte de redes grandes y complejas. En esta tesis, utilizamos la información de estructuras de proteínas para estudiar las redes de interacciones de proteínas. Con esta información, se puede entender como las proteínas interaccionan al nivel molecular y con este conocimiento se puede identificar las interacciones que pueden ocurrir al mismo tiempo de las que están incompatibles. En base a este principio, hemos desarrollado un método que permite estudiar las redes de interacciones de proteínas con un punto de vista mas dinámico de lo que ofrecen clásicamente. Además, al combinar este método con minería de la literatura y Los datos de la proteomica hemos construido la red de interacciones de proteínas asociada con la Rodopsina, un receptor acoplado a proteínas G y hemos identificado sus sub--‐módulos funcionales. Estos análisis surgieron una novel vıa de señalización hacia la regulación del citoesqueleto y el trafico vesicular por Rodopsina, además de su papel establecido en la visión.
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Thorpe, Christopher John. "Structural analysis of MHC : peptide interaction." Thesis, Birkbeck (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321649.

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Greville-Harris, G. "Child-infant interaction : A micro-analysis." Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371040.

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Adhikari, Bishwo. "Genomic Analysis of Nematode-Environment Interaction." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2578.

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The natural environments of organisms present a multitude of biotic and abiotic challenges that require both short-term ecological and long-term evolutionary responses. Though most environmental response studies have focused on effects at the ecosystem, community and organismal levels, the ultimate controls of these responses are located in the genome of the organism. Soil nematodes are highly responsive to, and display a wide variety of responses to changing environmental conditions, making them ideal models for the study of organismal interactions with their environment. In an attempt to examine responses to environmental stress (desiccation and freezing), genomic level analyses of gene expression during anhydrobiosis of the Antarctic nematode Plectus murrayi was undertaken. An EST library representative of the desiccation induced transcripts was established and the transcripts differentially expressed during desiccation stress were identified. The expressed genome of P. murrayi showed that desiccation survival in nematodes involves differential expression of a suite of genes from diverse functional areas, and constitutive expression of a number of stress related genes. My study also revealed that exposure to slow desiccation and freezing plays an important role in the transcription of stress related genes, improves desiccation and freezing survival of nematodes. Deterioration of traits essential for biological control has been recognized in diverse biological control agents including insect pathogenic nematodes. I studied the genetic mechanisms behind such deterioration using expression profiling. My results showed that trait deterioration of insect pathogenic nematode induces substantial overall changes in the nematode transcriptome and exhibits a general pattern of metabolic shift causing massive changes in metabolic and other processes. Finally, through field observations and molecular laboratory experiments the validity of the growth rate hypothesis in natural populations of Antarctic nematodes was tested. My results indicated that elemental stoichiometry influences evolutionary adaptations in gene expression and genome evolution. My study, in addition to providing immediate insight into the mechanisms by which multicellular animals respond to their environment, is transformative in its potential to inform other fundamental ecological and evolutionary questions, such as the evolution of life-history patterns and the relationship between community structure and ecological function in ecosystems.
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Books on the topic "Interaction analysis"

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Milnes, Peter D. Cultural interaction analysis. 2nd ed. Guildford, W.A: Belco Consulting, 2008.

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Norris, Sigrid. Identity in interaction: Introducing multimodal interaction analysis. Berlin: Walter de Gruyter, 2011.

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Discourses in interaction. Amsterdam: John Benjamins Pub. Company, 2010.

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Quasthoff, Uta M. Narrative interaction. Philadelphia, PA: John Benjamins Pub., 2005.

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Tabea, Becker, ed. Narrative interaction. Amsterdam: John Benjamins Pub., 2004.

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Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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Hare, A. Paul. Dramaturgical analysis of social interaction. New York: Praeger, 1988.

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H, Blumberg Herbert, ed. Dramaturgical analysis of social interaction. New York: Praeger, 1988.

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1953-, Vidmar Thomas J., and McKean Joseph W. 1944-, eds. Drug interaction and lethality analysis. Boca Raton, Fla: CRC Press, 1988.

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Sequence organization in interaction. Cambridge, UK: Cambridge University Press, 2007.

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Book chapters on the topic "Interaction analysis"

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Miller, Elizabeth R. "Interaction Analysis." In The Palgrave Handbook of Applied Linguistics Research Methodology, 615–38. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-137-59900-1_27.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Interaction." In Clinical Data Analysis on a Pocket Calculator, 139–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27104-0_25.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Interaction." In Clinical Data Analysis on a Pocket Calculator, 231–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27104-0_41.

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Shi, Dan. "Multimodal interaction analysis." In Multimodality and Classroom Languaging Dynamics, 27–48. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003183457-3.

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Clift, Rebecca, Paul Drew, and Ian Hutchby. "Conversation analysis." In The Pragmatics of Interaction, 40–54. Amsterdam: John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/hoph.4.02cli.

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Bainbridge, William Sims. "Text Analysis." In Human–Computer Interaction Series, 151–76. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5604-8_7.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Interaction." In Statistical Analysis of Clinical Data on a Pocket Calculator, 49–50. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1211-9_18.

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Lin, Dennis, Vuong Le, and Thomas Huang. "Human–Computer Interaction." In Visual Analysis of Humans, 493–510. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-997-0_25.

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Eriksson, Kjell-Ove, and Makonnen Belew. "Hydrophobic Interaction Chromatography." In Methods of Biochemical Analysis, 165–81. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470939932.ch6.

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Huertas-Rosero, Alvaro Francisco, and C. J. van Rijsbergen. "Quantum-Like Uncertain Conditionals for Text Analysis." In Quantum Interaction, 138–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24971-6_14.

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Conference papers on the topic "Interaction analysis"

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Lettner, Florian, Christian Grossauer, and Clemens Holzmann. "Mobile interaction analysis." In the 16th international conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2628363.2628384.

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Shiri, Maryam Shiri, Jameleddine Hassine Hassine, and Juergen Rilling. "Feature interaction analysis." In the twenty-second IEEE/ACM international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1321631.1321703.

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Cakir, Murat Perit, and Gerry Stahl. "Interaction analysis of dual-interaction CSCL environments." In the 9th international conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1600053.1600055.

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Basilio, Samuel da Costa Alves, Marcelo Ferreira Moreno, and Eduardo Barrére. "Supporting interaction and audience analysis in interactive TV systems." In the 11th european conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2465958.2465977.

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Basilio, Samuel da Costa Alves, Marcelo Ferreira Moreno, and Eduardo Barrére. "Interaction and audience analysis in interactive digital TV systems." In the 18th Brazilian symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2382636.2382712.

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Browne, Jeffrey, Bongshin Lee, Sheelagh Carpendale, Nathalie Riche, and Timothy Sherwood. "Data analysis on interactive whiteboards through sketch-based interaction." In the ACM International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2076354.2076383.

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Chu, Sidney K., Samuel Guanglin Xu, Feng Xu, and Nelson L. S. Tang. "Gene-gene Interaction Analysis by IAC (Interaction Analysis by Chi-Square) - A Novel Biological Constraint-based Interaction Analysis Framework." In 7th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005654601420150.

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"ISS Plasma Interaction Analysis." In 55th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.iac-04-t.2.03.

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Viccione, Giacomo, Vittorio Bovolin, and Eugenio Pugliese Carratelli. "Simulating fluid-structure interaction with SPH." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756099.

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Ramos, Paulo, João T. Mexia, Francisco Carvalho, Ricardo Covas, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Interaction in Balanced Cross Nested Designs." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636918.

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Reports on the topic "Interaction analysis"

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Isaac, Daron, and Michael Iverson. Automated Fluid-Structure Interaction Analysis. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada435321.

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Wood, Sheila J., Russ Granzow, William Petri, and Jr. Recognition Using Biospecific Interaction Analysis. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada241879.

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Zhenkun, Lou. Functional Analysis of Chk2-Kiaa0170 Interaction. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada449840.

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Lou, Zhenkun. Functional Analysis of Chk2-Kiaa0170 Interaction. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada429482.

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Liu, Antony K. Wavelet Analysis of Air-sea Interaction. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada629299.

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Terrill, Eric J. CBLAST Data Analysis: Air-Sea Interaction Floats. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada495437.

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J. Keifer and M. Taylor. PERMANENT ROCKBOLT AND TEMPORARY CHANNEL INTERACTION ANALYSIS. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/883422.

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SIbert, Linda E., James N. Templeman, and Robert J. Jacob. Evaluation and Analysis of Eye Gaze Interaction. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada389984.

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Liu, Wing K. Multiresolution Analysis of Compressible Viscous Flow-Structure Interaction. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada377739.

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Zhao, Qing, Lili Zhou, Qiaoyue Ren, Xuejing Lu, and Li Hu. Culture–Sex Interaction in Trait Empathy — A Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2020. http://dx.doi.org/10.37766/inplasy2020.11.0097.

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