Literatura académica sobre el tema "Biological Early Warning System"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Biological Early Warning System".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Biological Early Warning System"
Kim, Sung-Yong, Ki-Yong Kwon y Won-Don Lee. "Biological Early Warning System for Toxicity Detection". Journal of the Korean Institute of Information and Communication Engineering 14, n.º 9 (30 de septiembre de 2010): 1979–86. http://dx.doi.org/10.6109/jkiice.2010.14.9.1979.
Texto completoYang, Haiqing. "Biological Early Warning System for Prawn Aquiculture". Procedia Environmental Sciences 10 (2011): 660–65. http://dx.doi.org/10.1016/j.proenv.2011.09.106.
Texto completoBalk, F., P. C. Okkerman, C. A. M. van Helmond, F. Noppert y I. van der Putte. "Biological Early Warning Systems for Surface Water and Industrial Effluents". Water Science and Technology 29, n.º 3 (1 de febrero de 1994): 211–13. http://dx.doi.org/10.2166/wst.1994.0104.
Texto completode Zwart, Dick, Kees J. M. Kramer y Henk A. Jenner. "Practical experiences with the biological early warning system “mosselmonitor”". Environmental Toxicology & Water Quality 10, n.º 4 (noviembre de 1995): 237–47. http://dx.doi.org/10.1002/tox.2530100403.
Texto completoLee, Jong-Chan y Won-Don Lee. "Biological Early Warning Systems using UChoo Algorithm". Journal of the Korean Institute of Information and Communication Engineering 16, n.º 1 (31 de enero de 2012): 33–40. http://dx.doi.org/10.6109/jkiice.2012.16.1.033.
Texto completoSluyts, Hilde, François Van Hoof, Anja Cornet y Jozef Paulussen. "A dynamic new alarm system for use in biological early warning systems". Environmental Toxicology and Chemistry 15, n.º 8 (agosto de 1996): 1317–23. http://dx.doi.org/10.1002/etc.5620150809.
Texto completoLeynen, M., T. Van den Berckt, J. M. Aerts, B. Castelein, D. Berckmans y F. Ollevier. "The use of Tubificidae in a biological early warning system". Environmental Pollution 105, n.º 1 (abril de 1999): 151–54. http://dx.doi.org/10.1016/s0269-7491(98)00144-4.
Texto completoGrekov, Aleksandr N., Aleksey A. Kabanov, Elena V. Vyshkvarkova y Valeriy V. Trusevich. "Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning". Sensors 23, n.º 5 (1 de marzo de 2023): 2687. http://dx.doi.org/10.3390/s23052687.
Texto completoAmorim, João, Miguel Fernandes, Vitor Vasconcelos y Luis Oliva Teles. "Stress test of a biological early warning system with zebrafish (Danio rerio)". Ecotoxicology 26, n.º 1 (7 de octubre de 2016): 13–21. http://dx.doi.org/10.1007/s10646-016-1736-5.
Texto completoChen, Qiuwen, Jinfeng Ma, Zijian Wang y Guoxian Huang. "Biological early warning and emergency management support system for water pollution accident". Transactions of Tianjin University 18, n.º 3 (junio de 2012): 201–5. http://dx.doi.org/10.1007/s12209-012-1662-4.
Texto completoTesis sobre el tema "Biological Early Warning System"
Wu, Jun. "An early warning system for currency crises /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECON%202007%20WU.
Texto completoJadi, Amr. "An early warning system for risk management". Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/9659.
Texto completoBarbosa, Jorge Henrique de Frias. "Early Warning System para distress bancário no Brasil". reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/24912.
Texto completoSubmitted by Raquel Almeida (raquel.df13@gmail.com) on 2017-10-24T17:50:03Z No. of bitstreams: 1 2017_JorgeHenriquedeFriasBarbosa.pdf: 3875803 bytes, checksum: 2fd1608eb0ac0d76f29b924898b06b59 (MD5)
Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2017-10-31T12:15:52Z (GMT) No. of bitstreams: 1 2017_JorgeHenriquedeFriasBarbosa.pdf: 3875803 bytes, checksum: 2fd1608eb0ac0d76f29b924898b06b59 (MD5)
Made available in DSpace on 2017-10-31T12:15:52Z (GMT). No. of bitstreams: 1 2017_JorgeHenriquedeFriasBarbosa.pdf: 3875803 bytes, checksum: 2fd1608eb0ac0d76f29b924898b06b59 (MD5) Previous issue date: 2017-10-31
Esta tese é composta por três artigos que cobrem tópicos sobre o tema de early warning system para crises bancárias e distress bancário: uma pesquisa bibliométrica sobre early warning system (EWS) para crises bancárias e distress, um estudo empírico que estima um early warning system para distress de bancos brasileiros com regressão logística e um estudo empírico que constrói um early warning system com técnicas de aprendizagem de máquina supervisionada. O primeiro artigo apresenta um panorama do estado da literatura sobre EWS para crises bancárias e distress bancário por meio de uma revisão bibliométrica da literatura apresentando as principais ideias, principais conceitos, principais relacionamentos com outros tipos de crises, principais métodos utilizados, principais indicadores de crises e de distress. Foi realizada uma pesquisa em nas bases da Scopus e da Web of Science, onde, a partir de critérios de seleção, foram encontrados 124 artigos que foram devidamente classificados e codificados mediante importantes critérios para a área de estudo. Foi apresentado a evolução dos estudos na área, as gerações e tipos de EWS e os principais indicadores micro e macroprudencias apresentados pelos estudos da amostra. Como um resultado das lacunas da literatura na área é proposta uma agenda estruturada, visando guiar novos estudos por meio da apresentação de lacunas com grande potencial para ser explorada e reforçar o estado da arte em EWS. Adicionalmente, os resultados demonstram que mais estudos são necessários em EWS com relação à determinação dos horizontes de tempo para as previsões do modelo, com relação a estudos que tratam da América do Sul, América Central e África. Futuros estudos também devem considerar a possibilidade de utilização de modelos de aprendizagem de máquina, inteligência artificial e métodos computacionais, pois ainda existem poucos estudos e os resultados são promissores. O segundo artigo contribuiu com algumas inovações, como a construção e utilização de uma nova base dados de eventos de distress de bancos brasileiros, incluindo 179 eventos considerados como distress bancário de acordo com a definição de ?, incluindo 8 casos de RAET, 9 casos de intervenção, um caso de PROER, 11 casos de privatizações, 32 casos de incorporação e fusão, 13 casos de transformação em outros tipos de instituições financeiras, 32 caso de transformação de bancos em outros tipos de instituições, 21 casos de cancelamento e 52 casos de liquidação extrajudicial. Foi construído um painel de dados a partir de 54.087 balancetes de 359 bancos, englobando o período de julho de 1994 a novembro de 2016, juntamente com dados do setor bancário brasileiro e dados macroeconômicos. Para tratar do problema de eventos raros. O presente estudo utilizou a abordagem SMOTE (Synthetic Minority Over-sampling Technique) que pode aumentar a performance do modelo em termos da área sob a curva ROC (Area under the Receiver Operating Characteristic curve - AUC), uma técnica que que maximiza a área sob a curva ROC (AUC - area under the curve). Outra contribuição do segundo estudo foia comparação de modelos de acordo com o horizonte de tempo das previsões, característica importante para um EWS. Verificou-se que o modelo com o horizonte de tempo de 6 meses foi o modelo com maior área sob a curva ROC, para os dados da amostra utilizada, considerando-se o período de julho de 1994 até novembro de 2016. No terceiro artigo, foram utilizadas duas técnicas de aprendizagem de máquina supervisionada para construir EWSs: random forest e SVM (support vector machines) que obtiveram resultados superiores ao modelo de regressão logística apresentado no segundo estudo. Ambos os modelos de aprendizagem de máquina superam a regressão logística, em termos de acurácia, área sob a curva AUC (Area Under the Curve –AUC), sensibilidade (valor preditivo positivo) e especificidade (valor preditivo negativo). E o modelo random forest também superou o SVM em termos de acurácia, área sob a curva (AUC), sensibilidade e especificidade. Verificou-se também que os modelos random forest apresentaram melhor qualidade de previsão com as janelas de tempo de 32 e 34 meses, mostrando-se adequados às necessidades das autoridades.
This thesis consistis of three articles covering topics in early warning system (EWS) for bank crises and distress: an empirical study that estimates an early warning system for distress of Brazilian banks with logistic regression and an empirical study that builds an early warning system with techniques Of supervised machine learning. The first article presents an overview of the literature on EWS for bank crises and bank distress through a bibliometric review of the literature presenting the main ideas, main concepts, main relationships with other types of crises, main methods used, main crisis indicators And distress. A survey was carried out in the databases of Scopus and the Web of Science, where, based on selection criteria, 124 articles were found that were duly classified and codified by important criteria for the study area. The evolution of the studies in the area, the generations and types of EWS and the main micro and macroprudential indicators presented by the sample studies were presented. As a result of the literature gaps in the area, a structured agenda is proposed, aimed at guiding new studies through the presentation of gaps with great potential to be explored and to reinforce the state of the art in EWS. In addition, the results demonstrate that more studies are needed in EWS regarding the determination of time horizons for model predictions, in relation to studies dealing with South America, Central America and Africa. Future studies should also consider the possibility of using machine learning models, artificial intelligence and computational methods, as there are still few studies and the results are promising. The article contributed some innovations such as the construction and use of a new database of distress events of Brazilian banks, including 179 events considered as bank distress according to the definition of ?, including 8 cases of RAET (Temporary Special Administration Scheme), 9 cases of intervention, one PROER (The Program of Incentives for the Restructuring and Strengthening of the National Financial System) case, 11 cases of privatization, 32 cases of incorporation and merger, 13 cases of transformation in other types of financial institutions, 32 cases of transformation of banks into other types of institutions, 21 cases of cancellation and 52 cases of extrajudicial liquidation. A data panel was constructed from 54,087 balance sheets of 359 banks, covering the period from July 1994 to November 2016, together with data from the Brazilian banking sector and macroeconomic data. In order to address the problem of rare events, the present study used the Synthetic Minority Over-sampling Technique (SMOTE) approach that can increase the model’s performance in terms of the Area under the Receiver Operating Characteristic curve (AUC), a technique that maximizes the area under the ROC curve (AUC). Another contribution of the second study was the comparison of models according to the time horizon of the forecasts, an important feature for an EWS. It was verified that the model with the time horizon of 6 months was the model with the largest area under the ROC curve, for the data of the sample used, considering the period from July 1994 to November 2016. In the third article, two supervised machine learning techniques were used to construct EWSs: random forest and SVM (support vector machines) that obtained results superior to the logistic regression model presented in the second study. Both models of machine learning outperform logistic regression in terms of accuracy, area under the AUC curve, sensitivity (positive predictive value) and specificity (negative predictive value). And the random forest model also surpassed the SVM in terms of accuracy, area under the curve (AUC), sensitivity and specificity. It was also verified that the random forest models presented better quality of prediction with the forecast time horizons of 32 and 34 months, being adapted to the needs of the authorities.
Black, Gary. "Pollution prevention in wastewater networks : development of a biological early warning device". Thesis, Cranfield University, 2016. http://dspace.lib.cranfield.ac.uk/handle/1826/10290.
Texto completoConner, Christine. "Evaluating the Impact of an Early Warning Scoring System in a Community Hospital Setting". ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/4846.
Texto completoCeolin, Junior Tarcisio. "CORRELAÇÃO DE ALERTAS EM UM INTERNET EARLY WARNING SYSTEM". Universidade Federal de Santa Maria, 2014. http://repositorio.ufsm.br/handle/1/5439.
Texto completoIntrusion Detection Systems (IDS) are designed to monitor the computer network infrastructure against possible attacks by generating security alerts. With the increase of components connected to computer networks, traditional IDS are not capable of effectively detecting malicious attacks. This occurs either by the distributed amount of data that traverses the network or the complexity of the attacks launched against the network. Therefore, the design of Internet Early Warning Systems (IEWS) enables the early detection of threats in the network, possibly avoiding eventual damages to the network resources. The IEWS works as a sink that collects alerts from different sources (for example, from different IDS), centralizing and correlating information in order to provide a holistic view of the network. This way, the current dissertation describes an IEWS architecture for correlating alerts from (geographically) spread out IDS using the Case-Based Reasoning (CBR) technique together with IP Georeferencing. The results obtained during experiments, which were executed over the implementation of the developed technique, showed the viability of the technique in reducing false-positives. This demonstrates the applicability of the proposal as the basis for developing advanced techniques inside the extended IEWS architecture.
Sistemas de Detecção de Instrução (Intrusion Detection Systems IDS) são projetados para monitorar possíveis ataques à infraestruturas da rede através da geração de alertas. Com a crescente quantidade de componentes conectados na rede, os IDS tradicionais não estão sendo suficientes para a efetiva detecção de ataques maliciosos, tanto pelo volume de dados como pela crescente complexidade de novos ataques. Nesse sentido, a construção de uma arquitetura Internet Early Warning Systems (IEWS) possibilita detectar precocemente as ameaças, antes de causar algum perigo para os recursos da rede. O IEWS funciona como um coletor de diferentes geradores de alertas, possivelmente IDS, centralizando e correlacionado informações afim de gerar uma visão holística da rede. Sendo assim, o trabalho tem como objetivo descrever uma arquitetura IEWS para a correlação de alertas gerados por IDS dispersos geograficamente utilizando a técnica Case-Based Reasoning (CBR) em conjunto com Georreferenciamento de endereços IP. Os resultados obtidos nos experimentos, realizados sobre a implementação da técnica desenvolvida, mostraram a viabilidade da técnica na redução de alertas classificados como falsos-positivos. Isso demonstra a aplicabilidade da proposta como base para o desenvolvimento de técnicas mais apuradas de detecção dentro da arquitetura de IEWS estendida.
Persson, Elias y Martin Hautamäki. ""Buddy Tracker", an early warning system for recreational divers". Thesis, Karlstads universitet, Fakulteten för teknik- och naturvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-6386.
Texto completoPukhanov, Alexander. "WiFi Extension for Drought Early-Warning Detection System Components". Thesis, Linköpings universitet, Elektroniska Kretsar och System, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123436.
Texto completoBoulton, Christopher Andrew. "Early warning signals of environmental tipping points". Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18568.
Texto completoGadelha, Juliana Rodrigues. "Sea anemones stress responses in three different climatic scenarios as early warning systems for environmental change". Doctoral thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/19133.
Texto completoLibros sobre el tema "Biological Early Warning System"
Oshima, Kevin H. Ultrafiltration-based extraction for biological agents in early warning systems. Denver, Colo: Awwa Research Foundation, 2006.
Buscar texto completoLove, Nancy G. Upset early warning systems for biological treatment processes: Source and effect relationships. Alexandria, VA: WERF, 2005.
Buscar texto completoW, Long Maurice, ed. Airborne early warning system concepts. Boston: Artech House, 1992.
Buscar texto completoErica, Dodd, De Decker Ludgard y University of Victoria (B.C.). Centre for Studies in Religion and Society., eds. Art as an early-warning system. Victoria, B.C: University of Victoria, Centre for Studies in Religion and Society, 2000.
Buscar texto completoAteya, Eltayeb Haj. Conflict early warning system for Sudan. Khartoum]: Peace Research Institute, University of Khartoum, 2006.
Buscar texto completoNoveria, Mita. Pengembangan "early warning system" dalam menghadapi krisis. [Jakarta]: Puslitbang Kependudukan dan Ketenagakerjaan, Lembaga Ilmu Pengetahuan Indonesia, 2000.
Buscar texto completoAbela, Tony. Malta's early warning system during World War II. [Hamrun, Malta]: SKS, 2014.
Buscar texto completoRaharjo, Yulfita. Pengembangan indikator untuk 'early warning system' dalam menghadapi krisis. [Jakarta]: Puslitbang Kependudukan dan Ketenagakerjaan, Lembaga Ilmu Pengetahuan Indonesia, 2000.
Buscar texto completoChʻoe, Kong-pʻil. The early warning system for currency crises in Korea. Seoul: Korea Institute of Finance, 2001.
Buscar texto completoC, Lozar Robert y Construction Engineering Research Laboratory, eds. Environmental Early Warning System (EEWS): Topic area brief documentation. Champaign, Ill: US Army Corps of Engineers, Construction Engineering Research Laboratory, 1987.
Buscar texto completoCapítulos de libros sobre el tema "Biological Early Warning System"
Kramer, Kees J. M. y Edwin M. Foekema. "The “Musselmonitor®” as Biological Early Warning System". En Biomonitors and Biomarkers as Indicators of Environmental Change 2, 59–87. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1305-6_4.
Texto completoKramer, Kees J. M. "Continuous Monitoring of Waters by Biological Early Warning Systems". En Rapid Chemical and Biological Techniques for Water Monitoring, 197–219. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745427.ch3e.
Texto completoKholodkevich, Sergey V., Tatiana V. Kuznetsova, Svetlana V. Sladkova, Anton S. Kurakin, Alexey V. Ivanov, Vasilii A. Lyubimtsev, Eugenii L. Kornienko y Valery P. Fedotov. "Industrial Operation of the Biological Early Warning System BioArgus for Water Quality Control Using Crayfish as a Biosensor". En Sustainable Development Goals Series, 127–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57488-8_10.
Texto completoGruber, David y William J. Rasnake. "The Use of a Biological Early Warning System to Minimize Risks Associated with Drinking Water Sources and Wastewater Discharges". En Hazardous and Industrial Waste Proceedings, 253–62. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003075905-33.
Texto completoMusavi, Syed Hyder Abbas. "Early Warning System". En Early Warning-Based Multihazard and Disaster Management Systems, 31–40. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, 2020.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429319907-4.
Texto completoTaneja, Aarti, Aniket Desai y Ravi S. Jakka. "Earthquake Early Warning System". En Lecture Notes in Civil Engineering, 617–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6233-4_44.
Texto completoMusavi, Syed Hyder Abbas. "Early Warning System Architecture". En Early Warning-Based Multihazard and Disaster Management Systems, 41–61. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, 2020.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429319907-5.
Texto completoTondre, Françoise. "European Warning System". En Early Warning Systems for Natural Disaster Reduction, 465–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55903-7_60.
Texto completoWu, Peng, Lei Gao y Qiong Wang. "Early Warning System for Finance". En Diversity of Managerial Perspectives from Inside China, 85–101. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-555-6_6.
Texto completoChatziadam, Panos, Ioannis G. Askoxylakis, Nikolaos E. Petroulakis y Alexandros G. Fragkiadakis. "Early Warning Intrusion Detection System". En Trust and Trustworthy Computing, 222–23. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08593-7_22.
Texto completoActas de conferencias sobre el tema "Biological Early Warning System"
Kim, Sung Yong, Ki Yong Kwon y Won Don Lee. "A Biological Early Warning System for Toxicity Detection". En 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE, 2009. http://dx.doi.org/10.1109/ncm.2009.358.
Texto completoHuo, Jianling, SongTang Liu, Lei Sun, Lei Yang, Yuze Song y Chao Li. "Research on Biological Disaster Early Warning and Decision Support System of Nuclear Power Plant". En 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9728403.
Texto completoYingrong Li, Dong-Hun Seo y Won Don Lee. "A new classifier applied to biological early warning systems for toxicity detection". En 2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT). IEEE, 2008. http://dx.doi.org/10.1109/icadiwt.2008.4664373.
Texto completoLi, Yingrong, Dong-Hun Seo y Won Don Lee. "A New Classification Application of Biological Early Warning Systems for Toxicity Detection". En 2008 International Symposium on Computer Science and its Applications (CSA). IEEE, 2008. http://dx.doi.org/10.1109/csa.2008.78.
Texto completoZhou, Zixian, Zhiwen Cui, Shenxin Yin y Tribikram Kundu. "A rapid acoustic source localization technique in early warning of building material damage- a numerical study". En Health Monitoring of Structural and Biological Systems XVI, editado por Paul Fromme y Zhongqing Su. SPIE, 2022. http://dx.doi.org/10.1117/12.2612324.
Texto completoSimek, Olga, Curtis Davis, Andrew Heier, Sanjeev Mohindra, Kyle O'Brien, John Passarelli y Frederick Waugh. "XLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat". En Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2018. http://dx.doi.org/10.24251/hicss.2018.118.
Texto completoNanni, Stefania y Gianluca Mazzini. "Sensornet Early-warning System Integration". En 7th International Conference on Sensor Networks. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006533100770084.
Texto completoLiu, Xiaoxu, Lin Cao y Xiaoli Huang. "Highway Early Warning Information System". En 2010 2nd International Conference on Information Engineering and Computer Science (ICIECS). IEEE, 2010. http://dx.doi.org/10.1109/iciecs.2010.5677660.
Texto completoSerkov, Alexander, Sergei Nikitin, Vladimir Kravchenko y Vladimir Knyazev. "Thunderstorm hazards early warning system". En 2015 Second International Scientific-Practical Conference Problems of Infocommunications Science and Technology (PIC S&T). IEEE, 2015. http://dx.doi.org/10.1109/infocommst.2015.7357294.
Texto completoClark, Rob y Doug Burghart. "Early Warning Frost Detection System". En Regional Conference on Permafrost 2021 and the 19th International Conference on Cold Regions Engineering. Reston, VA: American Society of Civil Engineers, 2021. http://dx.doi.org/10.1061/9780784483589.017.
Texto completoInformes sobre el tema "Biological Early Warning System"
VAN DER Schalie, Willian H., David E. Trader, Mark W. Widder, Tommy R. Shedd y Linda M. Brennan. A Residual Chlorine Removal Method to Allow Drinking Water Monitoring by Biological Early Warning Systems. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2005. http://dx.doi.org/10.21236/ada432455.
Texto completoGoldberg, Lawrence y Dennis Kimko. An Army Enlistment Early Warning System. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2003. http://dx.doi.org/10.21236/ada418476.
Texto completoSalisbury, J. B. Earthquake early warning system for Alaska: fact sheet. Alaska Division of Geological & Geophysical Surveys, mayo de 2020. http://dx.doi.org/10.14509/30454.
Texto completoWright, Mark T., Daniel T. Gottuk, Jennifer T. Wong, Susan L. Rose-Pehrsson y Sean Hart. Prototype Early Warning Fire Detection System: Test Series 1 Results. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2000. http://dx.doi.org/10.21236/ada382542.
Texto completoWright, Mark T., Daniel T. Gottuk, Jennifer T. Wong, Hung Pham y Susan L. Rose-Pehrsson. Prototype Early Warning Fire Detection System: Test Series 2 Results. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2000. http://dx.doi.org/10.21236/ada383972.
Texto completoGeng, Xin, Manuel A. Hernandez y Carlos Martins-Filho. Excessive food price variability early warning system: Incorporating exogenous covariates. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134592.
Texto completoDandge, Ajay Ramlal y Vishwas Vaidya. Early Warning System for Light Commercial Engines using EMOS (Engine MOnitoring System) Controller. Warrendale, PA: SAE International, septiembre de 2010. http://dx.doi.org/10.4271/2010-32-0120.
Texto completoNishino, Akihiko. Propose of Architecture Design for Early Warning System with Space and Terrestrial Infrastructure. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317284.
Texto completoPradhan, N. S., N. Bajracharya, S. R. Bajracharya, S. K. Rai y D. Shakya. Community Based Flood Early Warning System for the Hindu Kush Himalaya: Resource Manual. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2016. http://dx.doi.org/10.53055/icimod.626.
Texto completoPradhan, N. S., N. Bajracharya, S. R. Bajracharya, S. K. Rai y D. Shakya. Community Based Flood Early Warning System for the Hindu Kush Himalaya: Resource Manual. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2016. http://dx.doi.org/10.53055/icimod.626.
Texto completo