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Journal articles on the topic 'Interaction analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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 (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

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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 (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 hom

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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 (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 cigaret

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Bublyk, Yevhen, Olena Borzenko, and Anna Hlazova. "Cryptocurrency energy consumption: Analysis, global trends and interaction." Environmental Economics 14, no. 2 (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 cryptocurren

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Curtis, Aaron A., Yajun Yu, Megan Carey, Yildiz E. Yilmaz, and Sevtap Savas. "A genome-wide SNP-SNP interaction analysis exploring novel interacting loci associated with the risk of recurrence in colorectal cancer." PLOS One 20, no. 6 (2025): e0321967. https://doi.org/10.1371/journal.pone.0321967.

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Background Genetic factors can influence and predict patient outcomes. The association of interactions of germline SNPs with patient outcomes is an understudied area of prognostic research. In this study, we applied the first genome-wide SNP-SNP interaction analysis in relation to colorectal cancer outcomes. Objectives Our objective was to explore interacting SNP loci at the genome-wide level that predict the risk of local or distant recurrence (RMFS) in a cohort of stage I-III colorectal cancer patients from the Canadian province of Newfoundland and Labrador. Methods The patient cohort consis

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HAYASHI, YOSHIHARU, MIME KOBAYASHI, KATSUYOSHI SAKAGUCHI, et al. "PROTEIN CLASSIFICATION USING COMPARATIVE MOLECULAR INTERACTION PROFILE ANALYSIS SYSTEM." Journal of Bioinformatics and Computational Biology 02, no. 03 (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 co

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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 (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

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Kulharia, Mahesh. "Geometrical and electro-static determinants of protein-protein interactions." Bioinformation 17, no. 10 (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-redunda

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10

JOHNSTON, RICHARD D., and GEOFFREY W. BARTON. "Structural interaction analysis." International Journal of Control 41, no. 4 (1985): 1005–13. http://dx.doi.org/10.1080/0020718508961179.

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11

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

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

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14

Kang, Seoktae, and Sunyoung Kim. "In visual arts using VR: An interaction analysis interaction analysis." Korean Society of Culture and Convergence 42, no. 5 (2020): 597–620. http://dx.doi.org/10.33645/cnc.2020.05.42.5.597.

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Khan, Raja Muhammad Ishtiaq, and Tribhuwan Kumar. "Interaction Analysis of WhatsApp Application Integration in M-Learning." Webology 19, no. 1 (2022): 795–806. http://dx.doi.org/10.14704/web/v19i1/web19056.

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Interaction analysis (IA) describes and analyzes an instructor's verbal interaction with a student (s). The use of interaction analysis has aided in the monitoring of teacher verbal behavior. WhatsApp is also extensively used in language learning, and it is the most widely used program for this function. WhatsApp Interaction was investigated using Language Related Episodes (LRE) and Interaction Analysis to utilize WhatsApp. Eight pairs of pupils were formed from the sampled students. The 16 students were all enrolled in the public university's PYP section. The investigation took place for four

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KURNIASARI, CECILIA INDRI. "Social Interaction on Patients with Schizophrenia in Psychiatric Hospital." Jurnal Ilmiah Kesehatan Keperawatan 15, no. 2 (2020): 25. http://dx.doi.org/10.26753/jikk.v15i2.335.

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Social interaction is one of important indicator in the recovery process of mental patients, especially in patients with schizophrenia. Active social interactions can help patients with schizophrenia to socialize, while less active social interactions can cause social isolation to the risk of suicide. The purpose of this study was to determine the social interaction of patients with schizophrenia in psychiatric hospital. The number of participant in this study were 52 patients. Sampling with a purposive sampling technique. Data were collected using Social Interaction Questionnaire and Behavior

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Yashin, Anatoliy, Dequing Wu, Konstantin Arbeev, et al. "Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)." Innovation in Aging 4, Supplement_1 (2020): 491. http://dx.doi.org/10.1093/geroni/igaa057.1589.

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Abstract Experimental and clinical studies of Alzheimer’s disease (AD) provide plentiful evidence of AD heterogeneity and involvement of many interacting genes and pathways in regulation of AD-related traits. However, detailed mechanisms of genetic interactions (GxG) involved in AD remain largely unknown. Uncovering hidden patterns of such interactions from human data will help better understand the nature of AD heterogeneity and find new targets for AD prevention. In this paper, we applied a newly developed method of evaluating joint GxG effects on AD to analysis of the Long Life Family Study

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Dai, Fei, Hao Chen, Zhenping Qiang, Zhihong Liang, Bi Huang, and Leiguang Wang. "Automatic Analysis of Complex Interactions in Microservice Systems." Complexity 2020 (March 31, 2020): 1–12. http://dx.doi.org/10.1155/2020/2128793.

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Interactions in microservice systems are complex due to three dimensions: numerous asynchronous interactions, the diversity of asynchronous communication, and unbounded buffers. Analyzing such complex interactions is challenging. In this paper, we propose an approach for interaction analysis using model checking techniques, which is supported by the Process Analysis Toolkit (PAT) tool. First, we use Labeled Transition Systems (LTSs) to model interaction behaviors in microservice systems as sequences of send actions under synchronous and asynchronous communications. Second, we introduce a notio

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Zhou, Xueqian, Cong Liu, Huilong Ren, and Chen Xu. "Numerical Analysis of Propeller-Induced Hydrodynamic Interaction between Ships." Journal of Marine Science and Engineering 11, no. 3 (2023): 537. http://dx.doi.org/10.3390/jmse11030537.

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The hydrodynamic interaction effects between ships are significantly pronounced in restricted waters, and this may potentially threaten the safety of ships, especially given that ship dimensions and waterway traffic have kept increasing. Although there has been a good amount of research on ship hydrodynamic interactions, the study of the effect of the propeller on the ship’s hydrodynamic interaction is very limited. In this paper, a series of RANSE-based numerical simulations are carried out to study the characteristics of the propeller in near-field interaction between ships without speed. Th

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Nemeth, Lea, Tim Blumenfeld, Ann-Katrin Denn, Anastasia Hirstein, and Frank Lipowsky. "An Exploratory Analysis of Transactive Interaction Patterns in Cooperative Learning Using Sequential Analysis." Education Sciences 13, no. 8 (2023): 790. http://dx.doi.org/10.3390/educsci13080790.

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For cooperative learning to be effective, the quality of student–student interaction is crucial. Interactions, which are transactive in nature, are positively related to students’ learning success during cooperative learning. However, little is known about typical interaction patterns during transactive interaction in face-to-face cooperative learning. Therefore, the current study aims to analyze typical interaction patterns of transactive interaction in cooperative learning. Sixty-eight students from seventh to tenth grade were randomly assigned to a total of 23 groups in their classes. The g

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Le, Thu, Daniel Bolt, Eric Camburn, Peter Goff, and Karl Rohe. "Latent Factors in Student–Teacher Interaction Factor Analysis." Journal of Educational and Behavioral Statistics 42, no. 2 (2017): 115–44. http://dx.doi.org/10.3102/1076998616676407.

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Classroom interactions between students and teachers form a two-way or dyadic network. Measurements such as days absent, test scores, student ratings, or student grades can indicate the “quality” of the interaction. Together with the underlying bipartite graph, these values create a valued student–teacher dyadic interaction network. To study the broad structure of these values, we propose using interaction factor analysis (IFA), a recently developed statistical technique that can be used to investigate the hidden factors underlying the quality of student–teacher interactions. Our empirical stu

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Sun, Zheng, Shihao Li, Fuhua Li, and Jianhai Xiang. "Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/416543.

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WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1) and the constructed transcriptome data ofF. chinensiswere used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp.

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Gustafson, Karl. "Interaction antieigenvalues." Journal of Mathematical Analysis and Applications 299, no. 1 (2004): 174–85. http://dx.doi.org/10.1016/j.jmaa.2004.06.012.

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24

Müller-Frommeyer, Lena C., and Simone Kauffeld. "Gaining insights into organizational communication dynamics through the analysis of implicit and explicit communication." Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO) 52, no. 1 (2021): 173–83. http://dx.doi.org/10.1007/s11612-021-00559-9.

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AbstractThis report in the journal Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie aims at presenting how the analysis of implicit and explicit communication in organizational interaction can advance our insights into and implications for these interactions for research and science. Communication is a central process in modern organizations. Especially recurring forms of interaction in organizations (e.g., meetings or appraisal interviews) are of great importance for personal and organizational success. In these interactions, the communication between the

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Batool, Zakia, Waqas Munir, Mohsin Abbas, Umar Waqs Khan, and Mahwish Rabia. "Bayesian approach: an alternative to the additive main effect and multiplicative interaction models for genotypes through environmental interactions." Nativa 12, no. 4 (2024): 597–604. https://doi.org/10.31413/nat.v12i4.17484.

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Genotypes of different genetic structures behave differently in various environmental conditions. Genotype-by-environment interaction (GEI) is referred to as differential responses of different genotypes across different environments; GEI is of great importance because of the higher performance of genotypes that GEI assesses. However, the presence of GEI makes analysis more complicated. To up-root these assessment complications, several methods have been proposed, such as Principal Component Analysis (PCA), Cluster Analysis, Additive Main effects, Multiplicative Interaction (AMMI) models, and

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Huang, Tianchun. "Analysis of the Irreplaceability of Offline Relationship." Lecture Notes in Education Psychology and Public Media 4, no. 1 (2023): 1067–73. http://dx.doi.org/10.54254/2753-7048/4/2022797.

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During covid 19, traditional offline social interaction becomes difficult. Benefiting from the availability of social media, people are increasingly relying on social media to socialize. Can social media interaction replace real-life interaction? Is a social media-based relationship a sufficient substitution for a real relationship? This article divides social media interaction into reciprocal and non-reciprocal and compares two different social media interactions with real-life interactions. For the reciprocal social media interaction, The first research question distinguishes computer-mediat

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Nedelkov, Dobrin, and Randall W. Nelson. "Delineating protein-protein interactions via biomolecular interaction analysis-mass spectrometry." Journal of Molecular Recognition 16, no. 1 (2003): 9–14. http://dx.doi.org/10.1002/jmr.600.

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28

Ryu, Hokyoung, and Andrew Monk. "Interaction Unit Analysis: A New Interaction Design Framework." Human-Computer Interaction 24, no. 4 (2009): 367–407. http://dx.doi.org/10.1080/07370020903038086.

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Zhang, Xizhe, Sergio Branciamore, Grigoriy Gogoshin, Andrei S. Rodin, and Arthur D. Riggs. "Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling." Proceedings of the National Academy of Sciences 114, no. 48 (2017): E10359—E10368. http://dx.doi.org/10.1073/pnas.1620425114.

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Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachro

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P, Krishnapriya. "Analysis of Pathogen-Immune Interaction Dynamics with Three Time delays." Journal of Computational Mathematica 1, no. 2 (2017): 1–17. http://dx.doi.org/10.26524/cm11.

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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 (2018): 397–401. http://dx.doi.org/10.18178/ijmmm.2018.6.6.415.

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Kolaki, Aravind I., and Basavaraj M. Gudadappanavar. "Performance Based Analysis of Framed Structure Considering Soil Structure Interaction." Bonfring International Journal of Man Machine Interface 4, Special Issue (2016): 106–11. http://dx.doi.org/10.9756/bijmmi.8165.

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Kagan, Dona M., and Donald J. Grandgenett. "Personality and interaction analysis." Research in Education 37, no. 1 (1987): 13–24. http://dx.doi.org/10.1177/003452378703700102.

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Newman, Sally, Gregory A. Morris, and Heidi Streetman. "Elder-Child Interaction Analysis." Child & Youth Services 20, no. 1-2 (1999): 129–45. http://dx.doi.org/10.1300/j024v20n01_10.

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Wu, Mengyun, and Shuangge Ma. "Robust genetic interaction analysis." Briefings in Bioinformatics 20, no. 2 (2018): 624–37. http://dx.doi.org/10.1093/bib/bby033.

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Sugai, George, and Timothy Lewis. "Teacher/Student Interaction Analysis." Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children 12, no. 4 (1989): 131–38. http://dx.doi.org/10.1177/088840648901200401.

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Klein, Ágnes, and Tünde Tancz. "Interaction Analysis in Nurseries." Acta Educationis Generalis 12, no. 3 (2022): 135–46. http://dx.doi.org/10.2478/atd-2022-0028.

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Abstract Introduction: The present research aimed to review the qualitative aspects of communication between children and their caregivers. We focused on the presence of quality indicators, on the strategies we encounter in influencing language acquisition in the interaction between children and early childhood educators. Methods: We examined the diversity, awareness and efficiency with which educators use communication tools and techniques in various preschool education situations for children under 3 years of age. In the empirical survey of day-care interactions a questionnaire was based on

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Rizkiyah, Faiqotur, and Umi Salamah. "Analysis of Classroom Verbal Interaction Using Fiacs (Flanders Interaction Analysis Categories System)." Darussalam English Journal (DEJ) 3, no. 1 (2023): 31–66. http://dx.doi.org/10.30739/dej.v3i1.2234.

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One of the primary factors that influence the teaching and learning process is interaction. Verbal interaction is the most interaction happened between teacher and students in the classroom. The quality of verbal interaction affect the result of teaching and learning itself. Thus, this study aimed to find out teacher-students talk characteristic during classroom interaction using FIACS and to find out the dominant talk between teacher and students in classroom interaction. This study was qualitative research. To achieve the purposes of this study the writer used observation which was ado

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Shruthi.K.V. "Soil-Structure Interaction Analysis for Foundation Design." International Journal of Information Technology, A Peer reviewed Refereed Research Journal 9, no. 1 (2020): 51–56. https://doi.org/10.5281/zenodo.14891230.

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The response of a structure to earthquake shaking is affected by interactions between three linked systems: the structure, the foundation, and the soil underlying and surrounding the foundation. Soil-structure interaction analysis evaluates the collective response of these systems to a specified ground motion. The terms Soil Structure Interaction (SSI) and Soil-Foundation-Structure Interaction (SFSI) are both used to describe this effect in the literature. In this paper, the foundation is considered part of the structure, and the term SSI has been adopted.

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Batool, Tahira, Aly R. Seadawy, and Syed T. R. Rizvi. "Multiple lump solutions and their interactions for an integrable nonlinear dispersionless PDE in vector fields." Nonlinear Analysis: Modelling and Control 28 (February 22, 2023): 1–24. http://dx.doi.org/10.15388/namc.2023.28.31449.

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In this article, lump solutions, lump with I-kink, lump with II- kink, periodic, multiwaves, rogue waves and several other interactions such as lump interaction with II-kink, interaction between lump, lump with I-kink and periodic, interaction between lump, lump with II-kink and periodic are derived for Pavlov equation by using appropriate transformations. Additionally, we also present 3-dimensional, 2-dimensional and contour graphs for our solutions.

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DeBlasio, Stacy L., Juan D. Chavez, Mariko M. Alexander, et al. "Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology." Journal of Virology 90, no. 4 (2015): 1973–87. http://dx.doi.org/10.1128/jvi.01706-15.

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ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topologica

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Dong, Yukun. "An Analysis of the Impacts of Interaction using Social Media Platforms on Stock Liquidity." Highlights in Business, Economics and Management 30 (April 10, 2024): 270–75. http://dx.doi.org/10.54097/6nfv2p08.

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Social media platforms have developed into a way for more and more investors to interact with each other, and investors are interacting more and more frequently on social media platforms. The study looked at the association between the number of forum posts in the Oriental Wealth stock bar as a measure of social media interaction and stock liquidity as a measure of turnover rate is examined through a panel fixed-effects model, and the analysis results suggest that the number of bar posts has a significant positive effect on stock turnover rate, which indicates that the interaction on social me

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Kang, Jee Eun, Ji Hae Jun, Jung Hyun Kwon, et al. "Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool—Liquid/Liquid Phase Separation, Oligomerization, GO Analysis: A Toolkit for Interaction Data-Based Domain Analysis." Genes 14, no. 7 (2023): 1476. http://dx.doi.org/10.3390/genes14071476.

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Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool–liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein–nucleic acid interaction (PNI) and protein–protein interaction (PPI) analysis based on domain–domain interactions (DDIs). LLPS, protein oligomerization, the stru

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Yu, Ping, Xiulan He, and Lanqun Mao. "Tuning interionic interaction for highly selective in vivo analysis." Chemical Society Reviews 44, no. 17 (2015): 5959–68. http://dx.doi.org/10.1039/c5cs00082c.

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The interionic interaction demonstrated here refers to the interaction between ions and their counterparts, which is not only composed of electrostatic attraction between oppositely charged species but also other kinds of weak interactions. This review focuses on the recent progress in the tuning of interionic interaction to improve the selectivity of biosensors for in vivo analysis.

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Botelho, João, Paulo Mascarenhas, José João Mendes, and Vanessa Machado. "Network Protein Interaction in Parkinson’s Disease and Periodontitis Interplay: A Preliminary Bioinformatic Analysis." Genes 11, no. 11 (2020): 1385. http://dx.doi.org/10.3390/genes11111385.

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Recent studies supported a clinical association between Parkinson’s disease (PD) and periodontitis. Hence, investigating possible interactions between proteins associated to these two conditions is of interest. In this study, we conducted a protein–protein network interaction analysis with recognized genes encoding proteins with variants strongly associated with PD and periodontitis. Genes of interest were collected via the Genome-Wide Association Studies (GWAS) database. Then, we conducted a protein interaction analysis, using the Search Tool for the Retrieval of Interacting Genes/Proteins (S

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Roter, Debra, and Susan Larson. "The Roter interaction analysis system (RIAS): utility and flexibility for analysis of medical interactions." Patient Education and Counseling 46, no. 4 (2002): 243–51. http://dx.doi.org/10.1016/s0738-3991(02)00012-5.

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Ollerton, Jeff, Duncan McCollin, Daphne G. Fautin, and Gerald R. Allen. "Finding NEMO: nestedness engendered by mutualistic organization in anemonefish and their hosts." Proceedings of the Royal Society B: Biological Sciences 274, no. 1609 (2006): 591–98. http://dx.doi.org/10.1098/rspb.2006.3758.

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The interaction structure of mutualistic relationships, in terms of relative specialization of the partners, is important to understanding their ecology and evolution. Analyses of the mutualistic interaction between anemonefish and their host sea anemones show that the relationship is highly nested in structure, generalist species interacting with one another and specialist species interacting mainly with generalists. This supports the hypothesis that the configuration of mutualistic interactions will tend towards nestedness. In this case, the structure of the interaction is at a much larger s

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Gunawardena, Charlotte N., Constance A. Lowe, and Terry Anderson. "Analysis of a Global Online Debate and the Development of an Interaction Analysis Model for Examining Social Construction of Knowledge in Computer Conferencing." Journal of Educational Computing Research 17, no. 4 (1997): 397–431. http://dx.doi.org/10.2190/7mqv-x9uj-c7q3-nrag.

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This study attempts to find appropriate interaction analysis/content analysis techniques that assist in examining the negotiation of meaning and co-construction of knowledge in collaborative learning environments facilitated by computer conferencing. The authors review strengths and shortcomings of existing interaction analysis techniques and propose a new model based on grounded theory building for analyzing the quality of CMC interactions and learning experiences. This new Interaction Analysis Model for Examining Social Construction of Knowledge in Computer Conferencing was developed after p

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Venkatraman, S., M. J. Doktycz, H. Qi, and J. L. Morrell-Falvey. "Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions." International Journal of Biomedical Imaging 2006 (2006): 1–10. http://dx.doi.org/10.1155/ijbi/2006/69851.

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The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Exper

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Yuan, Haiyue, Janko Ćalić, and Ahmet Kondoz. "Analysis of User Requirements in Interactive 3D Video Systems." Advances in Human-Computer Interaction 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/343197.

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The recent development of three dimensional (3D) display technologies has resulted in a proliferation of 3D video production and broadcasting, attracting a lot of research into capture, compression and delivery of stereoscopic content. However, the predominant design practice of interactions with 3D video content has failed to address its differences and possibilities in comparison to the existing 2D video interactions. This paper presents a study of user requirements related to interaction with the stereoscopic 3D video. The study suggests that the change of view, zoom in/out, dynamic video b

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