Escenarios prospectivos: una revisión de la literatura usando el paquete de R Bibliometrix
Resumen
Objetivo. Realizar un estudio de las tendencias actuales del uso de los escenarios prospectivos en las diferentes áreas de las ciencias utilizando la herramienta bibliometrix de R, que permita tener un punto de partida para investigaciones futuras. Diseño/Metodología/Enfoque. El estudio abarca 1056 artículos de investigación publicados en el periodo comprendido entre los años 1977 y 2021 recopilados de la base de datos Scopus. A partir de ello, se conformó un archivo BibTeX para el procesamiento, visualización y análisis de los datos a través de un enfoque cuantitativo mediante Bibliometrix. Resultados/Discusión. Se percibió una alta heterogeneidad en los trabajos, pero sobre todo destacan los estudios referentes al sector de la energía. De igual modo la presente investigación permitió identificar los principales países, revistas científicas, direcciones y autores que abordan el estudio de los escenarios prospectivo. Conclusiones. Los escenarios prospectivos como herramienta de planificación se encuentran en una evolución constante, diversificándose y profundizando en disímiles áreas del conocimiento fundamentalmente en el sector de la energía. Las investigaciones en esta rama de la ciencia todavía no están completamente desarrolladas. Originalidad/Valor. La escaza literatura referente a estudios bibliométricos sobre escenarios prospectivo principalmente con el uso de la herramienta bibliometrix de R mediante una mirada estadística, posibilita realizar un trabajo por primera vez en lo que respecta a la base de datos Scopus.
Palabras clave
Texto completo:
PDFReferencias
Aazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674-4682. https://doi.org/10.1109/TII.2018.2855198
Abellana, D. P. M., Rivero, D. M. C., Aparente, M. E., & Rivero, A. (2020). Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: A Philippine scenario. Journal of Tourism Futures, 7(1), 78-97. https://doi.org/10.1108/JTF-07-2019-0070
Aigner, W., Miksch, S., Müller, W., Schumann, H., & Tominski, C. (2007). Visualizing time-oriented data—A systematic view. Computers and Graphics (Pergamon), 31(3), 401-409. https://doi.org/10.1016/j.cag.2007.01.030
Al Janabi, M. A. M. (2021). Multivariate portfolio optimization under illiquid market prospects: A review of theoretical algorithms and practical techniques for liquidity risk management. Journal of Modelling in Management, 16(1), 288-309. https://doi.org/10.1108/JM2-07-2019-0178
Alshehhi, K., Zawbaa, S. B., Abonamah, A. A., & Tariq, M. U. (2021). EMPLOYEE RETENTION PREDICTION IN CORPORATE ORGANIZATIONS USING MACHINE LEARNING METHODS. Academy of Entrepreneurship Journal, 27(SpecialIssue 2), 1-23. https://www.abacademies.org/articles/employee-retention-prediction-in-corporate-organizations-using-machine-learning-methods-10145.html
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Banik, S., & Huq, M. R. (2020). Geolocation based decision making for appropriate land use using machine learning model. International Journal of Scientific and Technology Research, 9(2), 3084-3090. https://www.ijstr.org/final-print/feb2020/Geolocation-Based-Decision-Making-For-Appropriate-Land-Use-Using-Machine-Learning-Model.pdf
Bourgeois, R., Penunia, E., Bisht, S., & Boruk, D. (2017). Foresight for all: Co-elaborative scenario building and empowerment. Technological Forecasting and Social Change, 124, 178-188. https://doi.org/10.1016/j.techfore.2017.04.018
Cahlik, T. (2000). Comparison of the maps of science. Scientometrics, 49(3), 373-387. https://link.springer.com/article/10.1023/A:1010581421990
Callon, M., Courtial, J.-P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22(1). https://link.springer.com/article/10.1007/BF02019280
Camón Luis, E., & Celma, D. (2020). Circular Economy. A review and bibliometric analysis. Sustainability, 12(16), 6381. https://doi.org/10.3390/su12166381
Celik, I. B., Ghia, U., Roache, P. J., Freitas, C. J., Coleman, H., & Raad, P. E. (2008). Procedure for estimation and reporting of uncertainty due to discretization in CFD applications. Journal of Fluids Engineering, Transactions of the ASME, 130(7), 0780011-0780014. https://doi.org/10.1115/1.2960953
Chévez, P. J., Martini, I., & Discoli, C. (2019). Methodology developed for the construction of an urban-energy diagnosis aimed to assess alternative scenarios: An intra-urban approach to foster cities’ sustainability. Applied Energy, 237, 751-778. https://doi.org/10.1016/j.apenergy.2019.01.037
Chu, X., Cui, W., Zhao, L., Cao, S., & Ge, Y. (2021). Probabilistic flutter analysis of a long-span bridge in typhoon-prone regions considering climate change and structural deterioration. Journal of Wind Engineering and Industrial Aerodynamics, 215. https://doi.org/10.1016/j.jweia.2021.104701
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of informetrics, 5(1), 146-166. https://doi.org/10.1016/j.joi.2010.10.002
Czasonis, M., Kritzman, M., Pamir, B., & Turkington, D. (2020). Enhanced scenario analysis. Journal of Portfolio Management, 46(4), 69-79. https://doi.org/10.3905/jpm.2020.1.125
de Abreu, D. T. M. P., Maturana, M. C., & Martins, M. R. (2020). Development of accidental scenarios involving human errors for risk assessment in restricted waters. Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 2A-2020. American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/OMAE2020-18996
Del Pilar García-Chitiva, M. (2021). Collaborative learning in higher education processes mediated by internet. Revista Electronica Educare, 25(2). https://doi.org/10.15359/ree.25-2.23
Del Rio Cortina, J., Arias, C. R., Espitia, N. L., & Arbeláez, D. C. (2019). Prospective and territorial development in island areas: Empirical evidence from Bocachica-Colombia. Problems and Perspectives in Management, 17(4), 176-189. https://doi.org/10.21511/ppm.17(4).2019.15
Della Corte, V., Del Gaudio, G., Sepe, F., & Sciarelli, F. (2019). Sustainable tourism in the open innovation realm: A bibliometric analysis. Sustainability (Switzerland), 11(21). https://doi.org/10.3390/su11216114
Dias, M. A. de P., Vianna, J. N. de S., & Felby, C. (2016). Sustainability in the prospective scenarios methods: A case study of scenarios for biodiesel industry in Brazil, for 2030. Futures, 82, 1-14. https://doi.org/10.1016/j.futures.2016.06.005
Duintjer Tebbens, R. J., Pallansch, M. A., Cochi, S. L., Ehrhardt, D. T., Farag, N. H., Hadler, S. C., … Thompson, K. M. (2018). Modeling Poliovirus Transmission in Pakistan and Afghanistan to Inform Vaccination Strategies in Undervaccinated Subpopulations. Risk Analysis, 38(8), 1701-1717. https://doi.org/10.1111/risa.12962
Durance, P., & Godet, M. (2010). Scenario building: Uses and abuses. Technological Forecasting and Social Change, 77(9), 1488-1492. https://doi.org/10.1016/j.techfore.2010.06.007
Erdogan, B., Abbott, C., & Aouad, G. (2010). Construction in year 2030: Developing an information technology vision. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1924), 3551-3565. https://doi.org/10.1098/rsta.2010.0076
Erdogan, B., Abbott, C., Aouad, G., & Kazi, A. S. (2009). Construction it in 2030: A scenario planning approach. Electronic Journal of Information Technology in Construction, 14, 540-555. https://www.itcon.org/paper/2009/35
García-Gusano, D., Iribarren, D., & Garraín, D. (2017). Prospective analysis of energy security: A practical life-cycle approach focused on renewable power generation and oriented towards policy-makers. Applied Energy, 190, 891-901. https://doi.org/10.1016/j.apenergy.2017.01.011
García-Madurga, M.-Á., Esteban-Navarro, M.-Á., & Morte-Nadal, T. (2021). Covid key figures and new challenges in the horeca sector: The way towards a new supply-chain. Sustainability (Switzerland), 13(12). https://doi.org/10.3390/su13126884
Garcia-Verdugo, R., Erbach, M., & Schnell, O. (2017). Need for Outcome Scenario Analysis of Clinical Trials in Diabetes. Journal of Diabetes Science and Technology, 11(2), 327-334. https://doi.org/10.1177/1932296816670925
Godet, M. (1983). Reducing the blunders in forecasting. Futures, 15(3), 181-192. https://doi.org/10.1016/0016-3287(83)90164-7
Godet, M. (1986). Introduction to la prospective. Seven key ideas and one scenario method. Futures, 18(2), 134-157. https://doi.org/10.1016/0016-3287(86)90094-7
Godet, M. (1990). Integration of scenarios and strategic management. Using relevant, consistent and likely scenarios. Futures, 22(7), 730-739. https://doi.org/10.1016/0016-3287(90)90029-H
Godet, M. (2000). The Art of Scenarios and Strategic Planning: Tools and Pitfalls. Technological Forecasting and Social Change, 65(1), 3-22. https://doi.org/10.1016/s0040-1625(99)00120-1
Godet, M. (2010). Future memories. Technological Forecasting and Social Change, 77(9), 1457-1463. https://doi.org/10.1016/j.techfore.2010.06.008
Godet, M., Chapuy, P., & Comyn, G. (1994). Global scenarios. Geopolitical and economic context to the year 2000. Futures, 26(3), 275-288. https://doi.org/10.1016/0016-3287(94)90015-9
Gomes, C. F. S., Costa, H. G., & de Barros, A. P. (2017). Sensibility analysis of MCDA using prospective in Brazilian energy sector. Journal of Modelling in Management, 12(3), 475-497. https://doi.org/10.1108/JM2-01-2016-0005
Hensher, D. A., & Rose, J. M. (2007). Development of commuter and non-commuter mode choice models for the assessment of new public transport infrastructure projects: A case study. Transportation Research Part A: Policy and Practice, 41(5), 428-443. https://doi.org/10.1016/j.tra.2006.09.006
Iribarren, D., Martín-Gamboa, M., Navas-Anguita, Z., García-Gusano, D., & Dufour, J. (2020). Influence of climate change externalities on the sustainability-oriented prioritisation of prospective energy scenarios. Energy, 196. https://doi.org/10.1016/j.energy.2020.117179
Ivars-Baidal, J. A., Celdrán-Bernabeu, M. A., Mazón, J.-N., & Perles-Ivars, Á. F. (2019). Smart destinations and the evolution of ICTs: A new scenario for destination management? Current Issues in Tourism, 22(13), 1581-1600. https://doi.org/10.1080/13683500.2017.1388771
Jin, Y., Lu, H., Jin, H., Zhao, C., & Peng, M. (2019). Optimization of virtual mobility management based on NFV and SFC. IEEE/CIC Int. Conf. Commun. China, ICCC, 267-272. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICCChina.2018.8641114
Jouan, J., Ridier, A., & Carof, M. (2020). Legume production and use in feed: Analysis of levers to improve protein self-sufficiency from foresight scenarios. Journal of Cleaner Production, 274. https://doi.org/10.1016/j.jclepro.2020.123085
Kagungan, D., Karomani, Yulianti, D., Meutia, I. F., Neta, Y., & Zainal, A. G. (2021). Prospective study of the tourism industry strategy in the new normal era. International Journal of Entrepreneurship, 25(2). https://www.abacademies.org/articles/prospective-study-of-the-tourism-industry-strategy-in-the-new-normal-era-10628.html
Kopyto, M., Lechler, S., von der Gracht, H. A., & Hartmann, E. (2020). Potentials of blockchain technology in supply chain management: Long-term judgments of an international expert panel. Technological Forecasting and Social Change, 161. https://doi.org/10.1016/j.techfore.2020.120330
Kovačić, M., Gračan, D., & Jugović, A. (2015). The scenario method of nautical tourism development—A case study of Croatia. Pomorstvo, 29(2), 125-132. https://hrcak.srce.hr/149658
Krivtchik, G. (2020). COSI7: The new CEA reference electro-nuclear simulation tool. En Margulis M. & Blaise P. (Eds.), Int. Conf. Phys. React.: Transit. Scalable Nucl. Future, PHYSOR (Vol. 2020-March, pp. 2362-2370). EDP Sciences - Web of Conferences. https://doi.org/10.1051/epjconf/202124713001
Kułacz, L., & Kliks, A. (2021). Brain-inspired data transmission in dense wireless network. Sensors (Switzerland), 21(2), 1-20. https://doi.org/10.3390/s21020576
Lamé, G., Jouini, O., & Stal-Le Cardinal, J. (2019). Methods and contexts: Challenges of planning with scenarios in a hospital’s division. Futures, 105, 78-90. https://doi.org/10.1016/j.futures.2018.09.005
Lehoux, P., Miller, F. A., & Williams-Jones, B. (2020). Anticipatory governance and moral imagination: Methodological insights from a scenario-based public deliberation study. Technological Forecasting and Social Change, 151. https://doi.org/10.1016/j.techfore.2019.119800
Leite, D. E. S., & de Freitas Chagas Junior, M. (2020). Generative sensing and seizing (gss) model: An adaptive dynamic development model as a tool for strategic orientation in aerospace organizations. En Pretorius L. & Pretorius M.W. (Eds.), Towards Digit. World Ind. X.0—Proc. Int. Conf. Int. Assoc. Manag. Technol., IAMOT (pp. 1087-1096). University of Pretoria. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092624528&partnerID=40&md5=af869634de8857b01165e9a862e72501
Li, W., & Gao, S. (2018). Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China’s cement industry. Energy, 165, 33-54. https://doi.org/10.1016/j.energy.2018.09.152
Li, X., Yi, W., Chi, H.-L., Wang, X., & Chan, A. P. C. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86, 150-162. https://doi.org/10.1016/j.autcon.2017.11.003
Lu, C., Li, W., & Gao, S. (2020). Driving determinants and prospective prediction simulations on carbon emissions peak for China’s heavy chemical industry. Journal of Cleaner Production, 251.. https://doi.org/10.1016/j.jclepro.2019.119642
Ma, H., Dibildox, G., Schultz, C., Regar, E., & van Walsum, T. (2015). PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions. International Journal of Computer Assisted Radiology and Surgery, 10(6), 695-705. https://doi.org/10.1007/s11548-015-1185-2
Mabea, G. A., & Macatangay, R. E. (2021). Towards electricity markets’ integration and investment in transmission capacity: East african community power markets. Economics of Energy and Environmental Policy, 10(1), 211-228. https://doi.org/10.5547/2160-5890.10.1.GMAB
Marchais-Roubelat, A., & Roubelat, F. (2008). Designing action based scenarios. Futures, 40(1), 25-33. https://doi.org/10.1016/j.futures.2007.06.008
Martín-Gamboa, M., Iribarren, D., García-Gusano, D., & Dufour, J. (2017). A review of life-cycle approaches coupled with data envelopment analysis within multi-criteria decision analysis for sustainability assessment of energy systems. Journal of Cleaner Production, 150, 164-174. https://doi.org/10.1016/j.jclepro.2017.03.017
Massara, S., Tetart, P., Garzenne, C., & Lecarpentier, D. (2006). Prospective scenarios of nuclear energy evolution over the XXIst century. PHYSOR-2006 Amer. Nuclear Soc. Topical Meet. Reactor Phys., 2006. Vancouver, BC. https://www.scopus.com/inward/record.uri?eid=2-s2.0-33847198995&partnerID=40&md5=e2368f9292f3e4851c837e1fac437a1b
Maturana, M. C., & Martins, M. R. (2019). Technique for Early Consideration of Human Reliability: Applying a Generic Model in an Oil Tanker Operation to Study Scenarios of Collision. Journal of Offshore Mechanics and Arctic Engineering, 141(5). https://doi.org/10.1115/1.4043414
McKiernan, P. (2017). Prospective thinking; scenario planning meets neuroscience. Technological Forecasting and Social Change, 124, 66-76. https://doi.org/10.1016/j.techfore.2016.10.069
Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de La Informacion, 29(1). https://doi.org/10.3145/epi.2020.ene.03
Mousa, M., Massoud, H. K., & Ayoubi, R. M. (2020). Organizational learning, authentic leadership and individual-level resistance to change: A study of Egyptian academics. Management Research, 18(1), 5-28. https://doi.org/10.1108/MRJIAM-05-2019-0921
Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: A bibliometric review and future research directions. Maritime Policy and Management, 577-597. https://doi.org/10.1080/03088839.2020.1788731
Nieto-Chaupis, H. (2019). Prospective End-to-End Internet of Bio-Nano Things Network Based in a Machine Learning Algorithm to Restore the Flux of Insuline in Prediabetic Patientshubernietochaupis@gmail.com. IEEE Chil. Conf. Electr., Electron. Eng., Inf. Commun. Technol., CHILECON. Presentado en 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019. https://doi.org/10.1109/CHILECON47746.2019.8988030
Niknejad, N., Ismail, W., Bahari, M., Hendradi, R., & Salleh, A. Z. (2021). Mapping the research trends on blockchain technology in food and agriculture industry: A bibliometric analysis. Environmental Technology and Innovation, 21. https://doi.org/10.1016/j.eti.2020.101272
Ogden, N. H., St.-Onge, L., Barker, I. K., Brazeau, S., Bigras-Poulin, M., Charron, D. F., Thompson, R. A. (2008). Risk maps for range expansion of the Lyme disease vector, Ixodes scapularis, in Canada now and with climate change. International Journal of Health Geographics, 7. https://doi.org/10.1186/1476-072X-7-24
Oliveira, A. S., de Barros, M. D., de Carvalho Pereira, F., Gomes, C. F. S., & da Costa, H. G. (2018). Prospective scenarios: A literature review on the Scopus database. Futures, 100, 20-33. https://doi.org/10.1016/j.futures.2018.03.005
Pappu, A., Patil, V., Jain, S., Mahindrakar, A., Haque, R., & Thakur, V. K. (2015). Advances in industrial prospective of cellulosic macromolecules enriched banana biofibre resources: A review. International Journal of Biological Macromolecules, 79, 449-458. https://doi.org/10.1016/j.ijbiomac.2015.05.013
Prommer, L., Tiberius, V., & Kraus, S. (2020). Exploring the future of startup leadership development. Journal of Business Venturing Insights, 14. https://doi.org/10.1016/j.jbvi.2020.e00200
Ragab, R., & Prudhomme, C. (2002). Climate change and water resources management in arid and semi-arid regions: Prospective and challenges for the 21st century. Biosystems Engineering, 81(1), 3-34. https://doi.org/10.1006/bioe.2001.0013
Ramboarison-Lalao, L., & Gannouni, K. (2019). Liberated firm, a leverage of well-being and technological change? A prospective study based on the scenario method. Technological Forecasting and Social Change, 140, 129-139. https://doi.org/10.1016/j.techfore.2018.04.011
Ratnoo, A., & Shima, T. (2011). Line of sight guidance for defending a moving target. Israel Annu. Conf. Aerosp. Sci., 1, 496-519. Tel-Aviv and Haifa: Technion Israel Institute of Technology. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904471711&partnerID=40&md5=4b272c97f5bc7881bc429d26fdc31330
Rivière, M., & Caurla, S. (2020). Representations of the forest sector in economic models. OEconomia, 10(3), 521-553. https://doi.org/10.4000/oeconomia.9418
Rodríguez-Soler, R., Uribe-Toril, J., & De Pablo Valenciano, J. (2020). Worldwide trends in the scientific production on rural depopulation, a bibliometric analysis using bibliometrix R-tool. Land Use Policy, 97, 104787. https://doi.org/10.1016/j.landusepol.2020.104787
Rosato, A., Panella, M., Andreotti, A., Mohammed, O. A., & Araneo, R. (2021). Two-stage dynamic management in energy communities using a decision system based on elastic net regularization. Applied Energy, 291. https://doi.org/10.1016/j.apenergy.2021.116852
Roubelat, F. (2006). Scenarios to challenge strategic paradigms: Lessons from 2025. Futures, 38(5), 519-527. https://doi.org/10.1016/j.futures.2005.09.001
Roubelat, F., Brassett, J., McAllum, M., Hoffmann, J., & Kera, D. (2015). Probing ephemeral futures: Scenarios as fashion design. Futures, 74, 27-36. https://doi.org/10.1016/j.futures.2015.05.004
Roux, C., Schalbart, P., Assoumou, E., & Peuportier, B. (2016). Integrating climate change and energy mix scenarios in LCA of buildings and districts. Applied Energy, 184, 619-629. https://doi.org/10.1016/j.apenergy.2016.10.043
Sanz-García, A., Pérez-Romero, M., Pastor, J., Sola, R. G., Vega-Zelaya, L., Vega, G., … Ortega, G. J. (2019). Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach. Journal of Neural Engineering, 16(2). https://doi.org/10.1088/1741-2552/ab039f
Schwarz, J. O., Ram, C., & Rohrbeck, R. (2019). Combining scenario planning and business wargaming to better anticipate future competitive dynamics. Futures, 105, 133-142. https://doi.org/10.1016/j.futures.2018.10.001
Seck, G. S., Krakowski, V., Assoumou, E., Maïzi, N., & Mazauric, V. (2020). Embedding power system’s reliability within a long-term Energy System Optimization Model: Linking high renewable energy integration and future grid stability for France by 2050. Applied Energy, 257. https://doi.org/10.1016/j.apenergy.2019.114037
Sharma, P., Singh, R., Tamang, M., Singh, A. K., & Singh, A. K. (2021). Journal of teaching in travel &tourism: A bibliometric analysis. Journal of Teaching in Travel and Tourism, 21(2), 155-176. https://doi.org/10.1080/15313220.2020.1845283
Shi, K., Zhou, Y., & Zhang, Z. (2021). Mapping the research trends of household waste recycling: A bibliometric analysis. Sustainability (Switzerland), 13(11). https://doi.org/10.3390/su13116029
Silva, C. A. V. (2018). Prospective scenarios for regional development in Bolivar, Colombia: Great Vision study 2014-2064. Urbe, 10(3), 521-557. https://doi.org/10.1590/2175-3369.010.003.AO04
Singh, M. K., Deep, S., & Banerjee, R. (2017). Risk management in construction projects as per Indian scenario. International Journal of Civil Engineering and Technology, 8(3), 127-136. https://iaeme.com/Home/article_id/IJCIET_08_03_013
Singh, R., Sibi, P. S., Sharma, P., Tamang, M., & Singh, A. K. (2021). Twenty Years of Journal of Quality Assurance in Hospitality & Tourism: A Bibliometric Assessment. Journal of Quality Assurance in Hospitality and Tourism. https://doi.org/10.1080/1528008X.2021.1884931
Straub, J., Mohammad, A., Berk, J., & Nervold, A. K. (2013). Above the cloud computing: Applying cloud computing principles to create an orbital services model. Proc SPIE Int Soc Opt Eng, 8739. Baltimore, MD. https://doi.org/10.1117/12.2018466
Sun, H., Fang, L., Wang, Y., Chi, Y., & Liu, R. (2014). A low contact resistance graphene field effect transistor with single-layer-channel and multi-layer-contact. Proc. IEEE/ACM Int. Symp. Nanoscale Archit., NANOARCH, 139-144. Paris: IEEE Computer Society. https://doi.org/10.1109/NANOARCH.2014.6880502
Tahir, A., & Massoud, A. (2017). Load shedding and forecasting in distribution systems with PV-based distributed generation and electric vehicles. En Facta M., Riyadi M.A., Prasetijo A.B., Widianto E.D., & Eridani D. (Eds.), Proc. - Int. Conf. Inf. Technol., Comput., Electr. Eng., ICITACEE (Vol. 2018-January, pp. 71-76). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICITACEE.2017.8257678
Tang, J., Shim, B., Chang, T.-H., & Quek, T. Q. S. (2019a). Incorporating URLLC and Multicast eMBB in Sliced Cloud Radio Access Network. IEEE Int Conf Commun, 2019-May. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICC.2019.8761648
Tang, J., Shim, B., Chang, T.-H., & Quek, T. Q. S. (2019b). Incorporating URLLC and Multicast eMBB in Sliced Cloud Radio Access Network. IEEE Int Conf Commun, 2019-May. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICC.2019.8761648
Vaccaro, R., & Rocco, M. V. (2021). Quantifying the impact of low carbon transition scenarios at regional level through soft-linked energy and economy models: The case of South-Tyrol Province in Italy. Energy, 220. https://doi.org/10.1016/j.energy.2020.119742
Walton, S., O’Kane, P., & Ruwhiu, D. (2019). Developing a theory of plausibility in scenario building: Designing plausible scenarios. Futures, 111, 42-56. https://doi.org/10.1016/j.futures.2019.03.002
White, G. R. T., & Samuel, A. (2019). Programmatic Advertising: Forewarning and avoiding hype-cycle failure. Technological Forecasting and Social Change, 144, 157-168. https://doi.org/10.1016/j.techfore.2019.03.020
Wright, A. (2005). The role of scenarios as prospective sensemaking devices. Management Decision, 43(1), 86-101. https://doi.org/10.1108/00251740510572506
Wu, H., Duan, H., Zheng, L., Wang, J., Niu, Y., & Zhang, G. (2016). Demolition waste generation and recycling potentials in a rapidly developing flagship megacity of South China: Prospective scenarios and implications. Construction and Building Materials, 113, 1007-1016. https://doi.org/10.1016/j.conbuildmat.2016.03.130
Xiao, Y., Wu, H., Wang, G., & Mei, H. (2021). Mapping the worldwide trends on energy poverty research: A bibliometric analysis (1999–2019). International Journal of Environmental Research and Public Health, 18(4), 1-22. https://doi.org/10.3390/ijerph18041764
Xie, H., Zhang, Y., Wu, Z., & Lv, T. (2020). A bibliometric analysis on land degradation: Current status, development, and future directions. Land, 9(1). https://doi.org/10.3390/LAND9010028
Yu, J., & Muñoz-Justicia, J. (2020). A bibliometric overview of twitter-related studies indexed in web of science. Future Internet, 12(5). https://doi.org/10.3390/FI12050091
Zahraoui, Y., Basir Khan, M. R., Alhamrouni, I., Mekhilef, S., & Ahmed, M. (2021). Current status, scenario, and prospective of renewable energy in algeria: A review. Energies, 14(9). https://doi.org/10.3390/en14092354
Zhou, X., Yang, T., Shi, P., Yu, Z., Wang, X., & Li, Z. (2017). Prospective scenarios of the saltwater intrusion in an estuary under climate change context using Bayesian neural networks. Stochastic Environmental Research and Risk Assessment, 31(4), 981-991. https://doi.org/10.1007/s00477-017-1399-7
Zolfani, S. H., Yazdani, M., Zavadskas, E. K., & Hasheminasab, H. (2020). Prospective madm and sensitivity analysis of the experts based on causal layered analysis (CLA). E a M: Ekonomie a Management, 23(3), 208-223. https://doi.org/10.15240/tul/001/2020-3-013
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.
Revista indizada en: Scopus, Web of Science (Emerging Sources Citation Index), DIALNET, EBSCO (Academic Search Complete,
Academic Search Premier, Academic Search Ultimate, Fuente Académica Plus), PROQUEST (Library and Information Science
Abstracts, Library Science), REDIB, CLASE, BIBLAT, INFOBILA, Ulrichs Web, Latindex, DOAJ, Index Copernicus, JournalsTOC,
ERIH Plus, E-LIS, MIAR, e-Libros, BASE,
Google Scholar, y otros.
Redes Sociales
Indicadores de impacto según Google Scholar:
Índice h: 8; Índice i10: 3
Revista certificada por el CITMA
Revista. Bibliotecas. Anales de investigación by Biblioteca Nacional de Cuba José Martí is licensed under aCreative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional License.
Creado a partir de la obra en anales.bnjm.cu
ISSN: 0006-176X, EISSN: 1683-8947