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Artykuły w czasopismach na temat „Hybrid model”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Yadav, Chetan, and Mr Shivendra Singh Chaudhary. "Employees’ Attitude Towards Hybrid Work Model." International Journal of Research Publication and Reviews 6, no. 5 (2025): 10024–29. https://doi.org/10.55248/gengpi.6.0525.1866.

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STONIČ, DAVID. "PREDSTAVITEV MODELA ZA PREPOZNAVANJE HIBRIDNIH GROŽENJ." 20 YEARS OF CONTEMPORARY MILITARY CHALLENGES/20 LET SODOBNIH VOJAŠKIH IZZIVOV, VOLUME 2018, ISSUE 20/4 (October 15, 2018): 105–18. http://dx.doi.org/10.33179/bsv.99.svi.11.cmc.20.4.5.

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Povzetek V spremenjenem varnostnem okolju moramo razviti ustrezne koncepte in orodja za pravočasno prepoznavanje in opredelitev hibridnih groženj, da bi se lahko uspešno odzvali. Oblikovali smo model, ki grožnje poveže z akterjem. Ovrednoteni obveščevalni podatki so vstopne informacije v izdelan model za ocenjevanje hibridne ogroženosti nacionalne varnosti. Izhodna informacija je identificirana hibridna grožnja. Ob izpolnitvi pogoja hibridne ogroženosti lahko nacionalnovarnostni sistem začne izvajati ukrepe, ki bi zmanjšali zmožnosti delovanja hibridnega akterja. Zavedanje o obstoju hibridnih

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Turgut, Mehmet, and Alp Ustundag. "A Hybrid Risk Evaluation Model for Automotive Production." International Journal of Machine Learning and Computing 4, no. 5 (2014): 458–62. http://dx.doi.org/10.7763/ijmlc.2014.v4.454.

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Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden, and Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence." International Journal of Machine Learning and Computing 10, no. 2 (2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.

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P Khade, P., and M. S Palaspagar. "Two Fluid Anisotropic Model with Hybrid Scale Factor." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 832–37. http://dx.doi.org/10.21275/sr221017220146.

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Zhang, Wei, and Youxian Sun. "Hybrid Automaton Model and Control of Hybird Systems." Developments in Chemical Engineering and Mineral Processing 9, no. 1-2 (2008): 143–50. http://dx.doi.org/10.1002/apj.5500090217.

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Athambawa, Aiman, Md Gapar Md Johar, and Ali Khathibi. "Secure cloud adoption model: novel hybrid reference model." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 936–43. https://doi.org/10.11591/ijeecs.v27.i2.pp936-943.

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This article discusses research conducted to conceptualise a secure cloud adoption model. The study surveyed SMEs in the Sri Lankan information technology industry using a questionnaire to determine cloud computing adoption factors. The study used Rogers' diffusion of innovation (DOI), Tornatzky and Fleischer's technology-organization-environment (TOE) framework, Venkatesh and Bala's technology acceptance model 3 (TAM3), and Venkatesh, Thong, and Xu's Unified theory of acceptance and use of technology 2 (UTAUT2) as the theoretical foundation for evaluating the reference model.

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Baker, Jim. "A Hybrid Model for a Hybrid Science." Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology 1, no. 1 (2008): 1. http://dx.doi.org/10.1002/wnan.32.

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Wang, Jing, and Nianlong Luo. "A New Hybrid Popular Model for Personalized Tag Recommendation." Journal of Computers 11, no. 2 (2016): 116–23. http://dx.doi.org/10.17706/jcp.11.2.116-123.

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Hasan, M. Jamil, M. Umma Kulsum, Umakanta Sarker, et al. "Assessment of GGE, AMMI, Regression, and Its Deviation Model to Identify Stable Rice Hybrids in Bangladesh." Plants 11, no. 18 (2022): 2336. http://dx.doi.org/10.3390/plants11182336.

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Twenty-six rice hybrids were evaluated at three locations. Data were normally distributed after running the Shapiro–Wilk test. Plant height and effective tillers/hills showed leptokurtic distribution, indicating these traits were controlled by fewer genes, whereas the rest of the attributes had platykurtic distribution, indicating these traits were controlled by many genes. Most of the traits were significant for variety, locations, and variety × locations. For yield stability, the data were analyzed using additive main effect and multiplicative interaction (AMMI), genotype and genotype–enviro

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Bojović, Žana, and Jelena Stojkanović. "Flipped classroom: A model of hybrid teaching." Zbornik radova Pedagoskog fakulteta Uzice, no. 24 (2022): 11–28. http://dx.doi.org/10.5937/zrpfu2224011b.

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In the field of education, special attention has been paid recently to improving the quality of the teaching process as well as readiness to adapt to many challenges of modern society, especially in extraordinary circumstances such as the COVID-19 pandemic. One of the alternative ways of teaching is the model of the Flipped Classroom, whose basic settings come from the Theory of Self-Determination. The essence of the Flipped classroom is in creating a stimulating learning environment in accordance with one's own abilities and interests, outside the classic classrooms. The didactic-methodical i

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12

Zhang, Junlong, Kim Geok Soh, Xiaorong Bai, Mohd Ashraff Mohd Anuar, and Wensheng Xiao. "Optimizing learning outcomes in physical education: A comprehensive systematic review of hybrid pedagogical models integrated with the Sport Education Model." PLOS ONE 19, no. 12 (2024): e0311957. https://doi.org/10.1371/journal.pone.0311957.

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Background There is a notable gap in systematic reviews concerning hybrid pedagogical models (PMs) integrated with the Sport Education Model (SEM) and their impact on students’ outcomes. Purpose Which hybrid PMs incorporating SEM are currently the mainstream choices in research, and what are the main factors supporting their integration? How does SEM function as a foundational model in these hybrid teaching approaches? What learning outcomes are optimized through the hybrid models that combine SEM with other PMs? Methods A systematic search was conducted in major databases in December 2023 fol

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Andreichenko, D. K., K. P. Andreichenko, and I. A. Batraeva. "Hybrid Automation Extended Model." Izvestiya of Saratov University. New Series. Series: Mathematics. Mechanics. Informatics 19, no. 1 (2019): 94–104. http://dx.doi.org/10.18500/1816-9791-2019-19-1-94-104.

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Haire, Mark S., and David P. Knobles. "Geoacoustic inversion hybrid model." Journal of the Acoustical Society of America 109, no. 5 (2001): 2394. http://dx.doi.org/10.1121/1.4744447.

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Dobrev, I., F. Massouh, and M. Rapin. "Actuator surface hybrid model." Journal of Physics: Conference Series 75 (July 1, 2007): 012019. http://dx.doi.org/10.1088/1742-6596/75/1/012019.

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Kurbatova, P., N. Eymard, and V. Volpert. "Hybrid Model of Erythropoiesis." Acta Biotheoretica 61, no. 3 (2013): 305–15. http://dx.doi.org/10.1007/s10441-013-9188-2.

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Yamaguchi, Kazuhiro, and Kensuke Okada. "Hybrid cognitive diagnostic model." Behaviormetrika 47, no. 2 (2020): 497–518. http://dx.doi.org/10.1007/s41237-020-00111-x.

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Schnack, E. "A hybrid BEM model." International Journal for Numerical Methods in Engineering 24, no. 5 (1987): 1015–25. http://dx.doi.org/10.1002/nme.1620240512.

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Jinmei, Shi, Leau Yu-Beng, Li Kun, and Henry Obit Joe. "A comprehensive review on hybrid network traffic prediction model." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1450–59. https://doi.org/10.11591/ijece.v11i2.pp1450-1459.

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Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes

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20

Shahadat, Hossain, Sarowar Alam Md, Sabrina Alam Sadia, Bikash Sarker Md., and Amraul Islam Md. "Genotype-by-Environment interactions in Maize hybrids using GGE-biplot and AMMI model." Journal of Agricultural Science & Engineering Innovation (JASEI) 2, no. 2 (2022): 3–11. https://doi.org/10.5281/zenodo.6418442.

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Evaluation of the productivity and performance of maize hybrid genotypes in variable environments is a basic demand for releasing varieties. The purpose of this research was to assess the yield, performance, and stability of maize hybrids and to identify suitable environments for maize production in Bangladesh. The maize hybrid Sunshine produced the highest yield (11.24 t ha<sup>-1</sup> to 12.85 t ha<sup>-1</sup>) followed by Kaveri 60 (7.04 t ha<sup>-1</sup> to 12.72 t ha<sup>-1</sup>). Genotype ×environment interaction effects have influential effects on the yield of maize hybrids. Th

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21

Pullagura, Lokaiah. "Analysis of Train Delay Prediction System based on Hybrid Model." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 2900–2903. http://dx.doi.org/10.5373/jardcs/v12sp7/20202433.

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T., Fahad Iqbal. "User Interest Prediction Model for Hybrid Tag Recommender Automation Systems." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 4175–85. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020131.

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Subramanian, Suresh, and Y. Angeline Christobel. "A Hybrid Machine Learning Model to Predict Heart Disease Accurately." Indian Journal of Science and Technology 15, no. 12 (2022): 527–34. http://dx.doi.org/10.17485/ijst/v15i12.104.

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Dash, Ritesh, Chinmaya Behera, Pratik Ranjan Behera, Manas Ranjan Sarangi, and Kunjan Kumar Mohapatra. "A Review on Hybrid Energy System and A Model Simulation." International Journal of Scientific Research 3, no. 5 (2012): 150–53. http://dx.doi.org/10.15373/22778179/may2014/46.

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Roshdi Ibrahim, Rana, Aly Sherif Abdel Fayad, and Mohamed Badawy. "Hybrid Model for Predicting the Productivity of Concrete Batching Plants." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 848–53. http://dx.doi.org/10.21275/sr221219042912.

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Chuentawat, Ronnachai, Nittaya Kerdprasop, and Kittisak Kerdprasop. "The Forecast of PM10 Pollutant by Using a Hybrid Model." International Journal of Future Computer and Communication 6, no. 3 (2017): 128–32. http://dx.doi.org/10.18178/ijfcc.2017.6.3.504.

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Sen, Amit Prakash, Nirmal Kumar Rout, Tuhinansu Pradhan, and Amrit Mukherjee. "Hybrid Deep CNN Model for the Detection of COVID-19." Indian Journal Of Science And Technology 15, no. 41 (2022): 2121–28. http://dx.doi.org/10.17485/ijst/v15i41.1421.

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Clewley, Robert. "Hybrid Models and Biological Model Reduction with PyDSTool." PLoS Computational Biology 8, no. 8 (2012): e1002628. http://dx.doi.org/10.1371/journal.pcbi.1002628.

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Drašković, Bojan, Veselinka Zečević, Zdravko Hojka, et al. "Application of AMMI model in zoning of FAO 400-500 maize hybrids." Selekcija i semenarstvo 27, no. 1 (2021): 41–49. http://dx.doi.org/10.5937/selsem2101041d.

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Identification of high yielding and stable genotypes is one of the main goals in all breeding programmes. Estimation of hybrids is often aggravated due to the presence of genotype x environment (GE) interaction. One of the ways to eliminate negative effect of this interaction is the application of reliable statistical models such as AMMI model, which singles out high yielding and stable genotypes that have positive reaction to the improvement of production environments. This research aimed to establish specific maize hybrids interactions in different environments in two years by AMMI analysis.

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Weidmüller, Lisa. "Human, Hybrid, or Machine? Exploring the Trustworthiness of Voice-Based Assistants." Human-Machine Communication 4 (2022): 85–110. http://dx.doi.org/10.30658/hmc.4.5.

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This study investigates how people assess the trustworthiness of perceptually hybrid communicative technologies such as voice-based assistants (VBAs). VBAs are often perceived as hybrids between human and machine, which challenges previously distinct definitions of human and machine trustworthiness. Thus, this study explores how the two trustworthiness models can be combined in a hybrid trustworthiness model, which model (human, hybrid, or machine) is most applicable to examine VBA trustworthiness, and whether this differs between respondents with different levels of prior experience with VBAs

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Shrestha, J., S. Subedi, R. Acharya, S. Sharma, and M. Subedi. "Grain Yield Stability of Maize (Zea mays L.) Hybrids using Ammi Model and GGE Biplot Analysis." SAARC Journal of Agriculture 19, no. 2 (2022): 107–21. http://dx.doi.org/10.3329/sja.v19i2.57675.

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Six maize hybrids; Khumal Hybrid-2, KML-5(A) × KYM-33, KML-8(A) × KYM-33, KWM-91 × KWM-93, KWM-92 × KWM-93, Super-951 were evaluated in a randomized complete block design (RCBD) with three replications at Bhotechaur (Sindhupalchowk), Mandan Deupur (Kavrepalachowk), and Khumaltar (Lalitpur), Nepal to identify stable and superior hybrids. Hybrids were found significant (p<0.01) for grain yield. The effects of environment and genotype × environment (G × E) interactions on grain yield were found to be significant (p<0.01). The combined analysis showed that KWM-91 × KWM-93 produced the highes

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McCartney, Kevin R., Sunil Kumar, Sharlene E. Sing, and Sarah M. Ward. "Using invaded-range species distribution modeling to estimate the potential distribution of Linaria species and their hybrids in the U.S. northern Rockies." Invasive Plant Science and Management 12, no. 02 (2019): 97–111. http://dx.doi.org/10.1017/inp.2019.15.

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AbstractInvasive populations of Dalmation toadflax [Linaria dalmatica (L.) Mill.] and yellow toadflax (Linaria vulgaris Mill.) are widespread throughout the Intermountain West, where gene flow between these nonnative species is producing vigorous and fertile hybrids. These hybrid toadflax populations are less responsive to herbicides than either parent species, and biocontrol agents routinely released on L. dalmatica and L. vulgaris often fail to establish on hybrid hosts. Early detection of hybrid Linaria populations is therefore essential for effective management, but resources are limited f

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Grauslyte, L., D. Gaffey, and S. Shepherd. "HYBRID MODEL FOR TRAINING HYBRID EXTRACORPOREAL MEMBRANE OXYGENATION SPECIALISTS." Journal of Cardiothoracic and Vascular Anesthesia 36 (December 2022): S45. http://dx.doi.org/10.1053/j.jvca.2022.09.072.

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Bessières, Dominique. "The hybridity of public communication: on old component still a sign of modernity in France." SOCIOLOGIA DELLA COMUNICAZIONE, no. 61 (July 2021): 36–51. http://dx.doi.org/10.3280/sc2021-061004.

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Public communication, as the communication of hybrid public organizations, appears to be recognized today in France. It is concerned by the theme of hybridi-ty because of the persistent specificities of the bureaucratic model, together with its forms of professionalization and the development of public management inspired by the private sector. So how can we scientifically understand this communication, whose organizational springs and models are hybrids between public, political and private?

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Mallick, Javed, Swapan Talukdar, Nabil Ben Kahla, et al. "A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors." Water 13, no. 19 (2021): 2632. http://dx.doi.org/10.3390/w13192632.

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The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operat

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ŞENEL, MİNE, BİLGİN ŞENEL, and SEVDA ÜRKER GÖKÇE. "STRATEGY DEVELOPMENT WITH A’WOT HYBRID MODEL." INTERNATIONAL REFEREED JOURNAL OF RESEARCH ON ECONOMICS MANAGEMENT, no. 11 (March 30, 2017): 1–24. http://dx.doi.org/10.17373/uheyad.2017.1.0001.

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Pons, R., A. Subias, and L. Travé-Massuyès. "Hybrid Causal Model Based Diagnosis." IFAC Proceedings Volumes 45, no. 20 (2012): 678–83. http://dx.doi.org/10.3182/20120829-3-mx-2028.00188.

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Del Vecchio, Carmen, Osvaldo Barbarisi, and Alessandra Parisio. "Hybrid Model for Crane Scheduling." IFAC Proceedings Volumes 41, no. 2 (2008): 15837–42. http://dx.doi.org/10.3182/20080706-5-kr-1001.02677.

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Globa, Anastasia. "Hybrid Model for Tutorial Engagement." Voprosy obrazovaniya / Educational Studies Moscow, no. 3 (2022): 7–35. http://dx.doi.org/10.17323/1814-9545-2022-3-7-35.

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Technology has increasingly become a key component of teaching and learning, particularly with the rapid shift to delivery through online platforms as a result of the ongoing COVID-19 pandemic. In 2020–2022, students who studied online often struggled to achieve key learning outcomes and receive lower marks than their on-campus peers, demonstrating reduced engagement in an online environment. A shared concern identified within the School of Architecture, Design, and Planning at the University of Sydney, was the ability to deliver a sense of physical experience to students who are studying onli

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Wu, Xunxun, Chang-Dong Wang, and Pengfei Jiao. "Hybrid-order Stochastic Block Model." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4470–77. http://dx.doi.org/10.1609/aaai.v35i5.16574.

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Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lower-order topological structure and are affected by the fragmentation issue,

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Udoewa, Victor. "Hybrid Expeditionary Service Learning Model." International Journal for Service Learning in Engineering, Humanitarian Engineering and Social Entrepreneurship 17, no. 1 (2022): 20–36. http://dx.doi.org/10.24908/ijsle.v17i1.14810.

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Service learning is an educational model that incorporates instruction, learning, service, and reflection. Many critics have highlighted weaknesses of service learning including its inability to address structural issues, build authentic relationships with communities, assess learning, and meet community goals. Expeditionary learning is a newer learning methodology that includes a focus on social justice, provides more time for building relationships and understanding community goals, and utilizes a portfolio-based assessment approach. Because Expeditionary Learning can address some of the wea

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Lysetsyi, Yu M. "SYSTEM MODEL OF HYBRID WARFARE." "Scientific Notes of Taurida V.I. Vernadsky University", series "Public Administration", no. 1 (2022): 111–16. http://dx.doi.org/10.32838/tnu-2663-6468/2022.1/19.

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Vikas, Om, and Pooja Arora. "Ranking Strategy Using Hybrid Model." International Journal of Computer Applications 5, no. 10 (2010): 10–15. http://dx.doi.org/10.5120/950-1327.

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Srivastava, Praveen Ranjan, and Prajwal Eachempati. "A Hybrid Portfolio Selection Model." International Journal of Intelligent Information Technologies 16, no. 3 (2020): 100–116. http://dx.doi.org/10.4018/ijiit.2020070105.

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It is generally observed that investors approach asset managers and financial analysts to recommend a customized portfolio based on certain personalized preferences. The article discusses a methodology to build a hybrid personalized multi-criteria model in the Indian stock market context suiting investor preferences. The analytical hierarchy process (AHP) was used to compute the criteria weights and data envelopment analysis (DEA) was adopted to screen the best portfolios which were subsequently ranked by a fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and e

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Jeon, Youtaek, and HyungJun Cho. "Model based hybrid decision tree." Journal of the Korean Data And Information Science Society 30, no. 3 (2019): 515–24. http://dx.doi.org/10.7465/jkdi.2019.30.3.515.

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Anderson, John R., and Jonathan Betz. "A hybrid model of categorization." Psychonomic Bulletin & Review 8, no. 4 (2001): 629–47. http://dx.doi.org/10.3758/bf03196200.

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Müller, Bjørn, and Matt Lamont. "Hybrid Velocity Model Prestack Imaging." ASEG Extended Abstracts 2004, no. 1 (2004): 1–4. http://dx.doi.org/10.1071/aseg2004ab102.

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Hein, Rafał, and Cezary Orlikowski. "Hybrid Reduced Model of Rotor." Archive of Mechanical Engineering 60, no. 3 (2013): 319–33. http://dx.doi.org/10.2478/meceng-2013-0021.

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Abstract In the paper, the authors describe the method of reduction of a model of rotor system. The proposed approach makes it possible to obtain a low order model including e.g. non-proportional damping or the gyroscopic effect. This method is illustrated using an example of a rotor system. First, a model of the system is built without gyroscopic and damping effects by using the rigid finite element method. Next, this model is reduced. Finally, two identical, low order, reduced models in two perpendicular planes are coupled together by means of gyroscopic and damping interaction to form one m

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Bock, Marj, Mari Caballero, and Kelly O'Neal-Hixson. "The TCSEFP Hybrid eMentoring Model." Educational Renaissance 9, no. 1 (2020): 23–30. http://dx.doi.org/10.33499/edren.v9i1.143.

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This article describes a hybrid eMentoring model used to support fellows during an 11-month fellowship program, the Teachers College Special Education Fellowship Program (TCSEFP). The TCSEFP is a residency in teaching (RIT) induction program that leads to the completion of a high incidence special education endorsement and a master’s degree in special education. During the fellowship, each participant receives extensive, explicit mentoring from onsite and off-site mentors. This hybrid eMentoring model, developed as a part of the TCSEFP, is facilitated in part via distance technologies. &

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A.A. BARTOSH. "The Model of Hybrid Warfare." Military Thought 28, no. 002 (2019): 1–19. http://dx.doi.org/10.21557/mth.54208773.

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