Relevant bibliographies by topics / Interaction analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Interaction analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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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, № 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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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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Alqaseer, Ahlam Mohammed. "Interaction Analysis of Smoking with Liver Function Tests." International Journal Of Pharmaceutical And Bio-Medical Science 02, no. 11 (November 17, 2022): 513–17. http://dx.doi.org/10.47191/ijpbms/v2-i11-07.

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Introduction: Smoking has been linked to harmful health impacts on several body organs including the cardiovascular system. Study objectives: The primary goal of the current study is to assess how smoking models affect the level of liver enzymes. Methods: Thirty male albino rats served as the subjects for this experimental study. We used male albino rats (Rattusratas) that were 6–8 weeks old and weighed 50–180 g. Rats were randomly allocated to one of three groups (n = 10): group 1 served as a negative control and was solely exposed to fresh air; group 2 was exposed to the most popular cigarette brands available in Jordan (red LM cigarettes) at a rate of 1 cigarette per rat per day for 30 days. Rats in group 3 were given flavored water pipes for 30 days, once a day for the entire body, after completely burning 20 g of one moassal. The smoking device was digital. LDH, AST, ALT, and liver enzymes were assessed in study groups. The collected data were examined using SPSS version 20. Results: Both exposures to cigarette smoking and water pipe smoking resulted in a considerable rise in liver enzymes. Interesting results showed that quitting smoking restored liver enzymes to levels close to those of the control group. Conclusion: Increased levels of liver enzymes are a sign that smoking causes liver damage, which can be reversed by quitting.
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Bublyk, Yevhen, Olena Borzenko, and Anna Hlazova. "Cryptocurrency energy consumption: Analysis, global trends and interaction." Environmental Economics 14, no. 2 (August 18, 2023): 49–59. http://dx.doi.org/10.21511/ee.14(2).2023.04.

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The rapid spread of cryptocurrencies is one of the most relevant trends today. One of the significant risks of their spread is the increase in energy consumption, which has a negative impact on the environment due to carbon emissions. This requires the development of a scientific toolkit for assessing relationships and predicting the impact of cryptocurrencies on energy consumption, which is the aim of this paper.With the correlational regression analysis, the model of the dependence of spending on IT sector, energy consumption of Bitcoin, Ethereum and global capitalization of the cryptocurrency market was conducted, based on statistical data from Statista.com, Сoinmarketcap.com and International Data Corporation. To check the possible relationship, tests for the adequacy of the results obtained (Fisher’s test, Student’s t-test) confirmed the correctness of coefficients for independent variables.The results showed a significant direct correlation (Multiple R is 95%) of spending on IT sector, energy consumption and global capitalization of the cryptocurrency market. The established relationships allowed predicting that Bitcoin energy consumption may reach 142 Terawatt hours per year in 2026. And its impact on environment by mining in 2022 was at least 27.4 Mt of CO2 emission.As a proposal, a conclusion was made on the expediency of linking mining to the use of certain sources of electricity production, such as “residual” natural gas, nuclear power, renewable energy sources. The obtained results and conclusions may be used as a basis for political decisions in the field of energy efficiency and climate change mitigation.
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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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Sainyakit, Paulus, and Yan Imam Santoso. "A Classroom Interaction Analysis of Teacher and Students by Using FIACS." Acitya: Journal of Teaching and Education 6, no. 1 (January 23, 2024): 157–67. http://dx.doi.org/10.30650/ajte.v6i1.3825.

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Classroom interaction plays an important role in the teaching and learning process. It is because it involves the interaction between teacher, student and student-student in which they influence each other in the classroom interaction. It is also said that classroom interaction gives a chance to ask, to guess, to think even to the course material in order to make interaction between the teacher and students their selves in the classroom. This research is aimed to analyze the classroom interaction by using FIACS. This research is included as a quantitative content analysis design. This research involves a teacher and the 2nd semester students of STKIP Terang Bangsa. The observation is employed in this research as the method of collecting data. After that, the data would be calculated by using a formula. The results show that the Teacher Initiated category is the highest dominant in the classroom interaction. It has 3.422 or 95,50% out of 3.583 interactions in the classroom. Then, the Student-Initiated category has 161 or 4,50% of the total. Lastly, the Student-student initiated category has no interactions. Due to the limitations of the research, the researcher suggests that future research on this topic should involve more participants in interacting not only teacher to student but student to student, so the conclusion can be drawn more valid.
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Kulharia, Mahesh. "Geometrical and electro-static determinants of protein-protein interactions." Bioinformation 17, no. 10 (October 31, 2021): 851–60. http://dx.doi.org/10.6026/97320630017851.

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Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.
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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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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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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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<p>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.</p><p>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.</p><p>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.</p><p><b>Keywords:</b>railway rail, disc-on-disc, pin-on-disc,Archard, wear simulation, Winkler, rolling, sliding</p>
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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.<br>Doctor of Philosophy<br>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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Ralph, Alan. "The verbal interaction analysis system: A valid measure of competence in interpersonal interactions." Thesis, Ralph, Alan (1988) The verbal interaction analysis system: A valid measure of competence in interpersonal interactions. PhD thesis, Murdoch University, 1988. https://researchrepository.murdoch.edu.au/id/eprint/50427/.

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This thesis proposes a novel approach to the assessment of persons experiencing difficulties with interpersonal interactions. Conversation skills were selected as the focus of the research, these being an important element in such interactions. A broad review of psychological research associated with 4 different perspectives on this topic indicated a variety of approaches to defining, assessing and remediating problems Social, developmental, with interpersonal interactions. educational and clinical perspectives were explored. Clinically useful measurement procedures either evaluated the global effect of persons or evaluated specific structural components of their behaviour. The former provided little information about how to intervene to bring about improvements, while the latter failed to identify behaviour which consistently correlated with other indicators of competent performance. Valid measures from other domains were examined for their application to the assessment of conversation skills. An initial study analysed data from a sample of university students and job seekers to provide a benchmark for subsequent comparison with a newly developed measurement system. Results were interpreted to support previous research findings that component analysis measures were not appropriate. Sequential and interaction analysis concepts were adapted to develop the Verbal Interaction Analysis System. The VIAS measured the effect of verbal behaviour in interpersonal interactions by focusing on whether the topic of conversation was maintained and if contributions were made which developed the current topic. The VIAS was applied to a number of different dyadic interactions beginning with a reanalysis of the initial study. Scores obtained with this measure resulted in improved correlations with global judgements of competence and predicted high- and low- competent subjects. Subsequent experiments supported the validity of the VIAS. The VIAS was administered to a group of low-competent subjects during a social-skills training programme and was used in a clinical case study with a person experiencing difficulties with interpersonal interactions. In addition, experiments were carried out with university students to investigate the effects on the VIAS of manipulating the behaviour of the confederate or the subject during initial interactions. Results indicated that the VIAS (1) was easy to use with high inter-rater agreement. (2) was sensitive to improvements in the competence of one category of low-competent subjects during a social skills training programme, (3) assisted in the selection of target IV behaviour in a single-subject clinical intervention, and (4) was sensitive to manipulations of behaviour such as changing levels of responsibility for maintaining the interaction. The major advantage of the System was its flexibility. Empirically determined adjustments to the scoring procedure produced valid scores from a different type of interaction, as exemplified by a job interview. The potential of the System as a research tool is discussed firstly, in the context of determining the demands made in different interpersonal interactions and secondly, for its relevance as an aid to improving maintenance and transfer (generalisation) of treatment gains.
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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.<br>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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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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Quasthoff, Uta M. Narrative interaction. Philadelphia, PA: John Benjamins Pub., 2005.

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

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Robert, Englebretson, ed. Stancetaking in discourse: Subjectivity, evaluation, interaction. Amsterdam: John Benjamins Pub., 2007.

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

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Hare, A. Paul. 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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1946-, Kalton Nigel J., Saab E. 1946-, and Montgomery-Smith Stephen 1963-, eds. Interaction between functional analysis, harmonic analysis, and probability. New York: Marcel Dekker, 1996.

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Georgakopoulou, Alexandra. Small stories, interaction, and identities. Philadelphia: John Benjamins Pub. Co., 2007.

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Kerbrat-Orecchioni, Catherine. Le discours en interaction. Paris: A. Colin, 2005.

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Book chapters on the topic "Interaction analysis"

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Jones, Rodney H. "Strategic Interaction." In Discourse Analysis, 21–26. 3rd ed. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003377405-7.

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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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Jin, Ying. "Interaction analysis." In Doctor–patient Communication in Chinese and Western Medicine, 34–56. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003161929-3.

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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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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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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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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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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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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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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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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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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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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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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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"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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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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Briscoe, William John, Igor I. Strakovsky, and Ronald L. Workman. A Data Analysis Center for Electromagnetic and Hadronic Interaction. Office of Scientific and Technical Information (OSTI), May 2015. http://dx.doi.org/10.2172/1213477.

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