Готові списки джерел за темами / Biological Early Warning System

Добірка наукової літератури з теми "Biological Early Warning System"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Biological Early Warning System"

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Kim, Sung-Yong, Ki-Yong Kwon, and Won-Don Lee. "Biological Early Warning System for Toxicity Detection." Journal of the Korean Institute of Information and Communication Engineering 14, no. 9 (September 30, 2010): 1979–86. http://dx.doi.org/10.6109/jkiice.2010.14.9.1979.

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Yang, 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.

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Balk, F., P. C. Okkerman, C. A. M. van Helmond, F. Noppert, and I. van der Putte. "Biological Early Warning Systems for Surface Water and Industrial Effluents." Water Science and Technology 29, no. 3 (February 1, 1994): 211–13. http://dx.doi.org/10.2166/wst.1994.0104.

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Within the framework of the International Rhine Action Programme and the EC ACE-Programme in the field of the environment (regulation EC.224/87) the sensitivity and reliability of biological early warning systems are being tested. The effectiveness of these systems for continuous water quality monitoring is being assessed, using surface water and industrial effluents. The systems tested are a fish and a waterflea early warning system. From the results it is concluded that both types of biological early warning systems in combination with physico-chemical monitoring increase the effectiveness of monitoring pollution levels in surface water. Fish early warning systems can be important tools in reducing water pollution by industries.
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de Zwart, Dick, Kees J. M. Kramer, and Henk A. Jenner. "Practical experiences with the biological early warning system “mosselmonitor”." Environmental Toxicology & Water Quality 10, no. 4 (November 1995): 237–47. http://dx.doi.org/10.1002/tox.2530100403.

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Lee, Jong-Chan, and Won-Don Lee. "Biological Early Warning Systems using UChoo Algorithm." Journal of the Korean Institute of Information and Communication Engineering 16, no. 1 (January 31, 2012): 33–40. http://dx.doi.org/10.6109/jkiice.2012.16.1.033.

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Sluyts, Hilde, François Van Hoof, Anja Cornet, and Jozef Paulussen. "A dynamic new alarm system for use in biological early warning systems." Environmental Toxicology and Chemistry 15, no. 8 (August 1996): 1317–23. http://dx.doi.org/10.1002/etc.5620150809.

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Leynen, M., T. Van den Berckt, J. M. Aerts, B. Castelein, D. Berckmans, and F. Ollevier. "The use of Tubificidae in a biological early warning system." Environmental Pollution 105, no. 1 (April 1999): 151–54. http://dx.doi.org/10.1016/s0269-7491(98)00144-4.

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Grekov, Aleksandr N., Aleksey A. Kabanov, Elena V. Vyshkvarkova, and Valeriy V. Trusevich. "Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning." Sensors 23, no. 5 (March 1, 2023): 2687. http://dx.doi.org/10.3390/s23052687.

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The use of bivalve mollusks as bioindicators in automated monitoring systems can provide real-time detection of emergency situations associated with the pollution of aquatic environments. The behavioral reactions of Unio pictorum (Linnaeus, 1758) were employed in the development of a comprehensive automated monitoring system for aquatic environments by the authors. The study used experimental data obtained by an automated system from the Chernaya River in the Sevastopol region of the Crimean Peninsula. Four traditional unsupervised machine learning techniques were implemented to detect emergency signals in the activity of bivalves: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier factor (LOF). The results showed that the use of the elliptic envelope, iForest, and LOF methods with proper hyperparameter tuning can detect anomalies in mollusk activity data without false alarms, with an F1 score of 1. A comparison of anomaly detection times revealed that the iForest method is the most efficient. These findings demonstrate the potential of using bivalve mollusks as bioindicators in automated monitoring systems for the early detection of pollution in aquatic environments.
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Amorim, João, Miguel Fernandes, Vitor Vasconcelos, and Luis Oliva Teles. "Stress test of a biological early warning system with zebrafish (Danio rerio)." Ecotoxicology 26, no. 1 (October 7, 2016): 13–21. http://dx.doi.org/10.1007/s10646-016-1736-5.

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Chen, Qiuwen, Jinfeng Ma, Zijian Wang, and Guoxian Huang. "Biological early warning and emergency management support system for water pollution accident." Transactions of Tianjin University 18, no. 3 (June 2012): 201–5. http://dx.doi.org/10.1007/s12209-012-1662-4.

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Дисертації з теми "Biological Early Warning System"

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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.

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Jadi, Amr. "An early warning system for risk management." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/9659.

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Risk management in healthcare has solved a wide range of healthcare-related issues in Saudi Arabia. However, the limitation of risk management teams working under special conditions (needing to solve critical health-related issues) has highlighted the urgent need for an early risk warning system (ERWS) in healthcare. The influences of changing weather conditions demand that diabetic patients and doctors in Saudi Arabia have a continuous check on health conditions. The number of diabetic patients is increasing rapidly in Saudi Arabia. Hence, risk management teams in healthcare must be supported with a system that alerts to changes before the changes become a significant risk/problem. Our proposed approach does the following: 1) predicts changes in BP and blood sugar level within hospital environment at runtime. 2) Continually checks patient health status with respect to health condition at runtime. 3) Alerts to the changes as detected (e.g. risk or unknown parameter), and also provides feedback for patient and doctor. We present a computational model that defines the interaction and communication of the system components and describes the prediction and checking process in our proposed approach. We designed the architecture for our proposed approach with respect to the computational model. The thesis proposes an early risk warning system approach, which predicts and checks patient health conditions with respect to the ideal conditions according to medical standards. The health status of a patient will be communicated to doctors and patients on an emergency note if the predicted values are outside normal conditions. In this way, the risk can be mitigated before the occurrence of damage to patient health at runtime. To implement the proposed approach, neural networks is used for developing the prediction component using Java programming. The results of this research successfully predicted the health condition of a patient by checking outputs against medical standards. The risks defined in this research include hyperglycaemia, hypoglycaemia, hypertension and hypotension. Appropriate results were obtained for almost every patient when checked with four input parameters for 200 patients. Consistent results were produced by the risk prediction component and the alerts were generated after every five (5) seconds to communicate to the patients and doctors at runtime. Health status of all 200 patients can also be seen to check the changes in health conditions in the hospital environment. Finally, a case study with different scenarios based on changes in patient health status with respect to ideal conditions revealed evaluated the approach.
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Barbosa, 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.

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Tese (doutorado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade e Gestão Pública, Programa de Pós-Graduação em Administração, 2017.
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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.
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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.

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A biological early warning system (EWS) was developed to screen wastewater containing nitrification inhibitors and identify nitrifying bacteria activity reduction without relying on absolute values of sensor signals. To do so, numerous sensors were evaluated using a tiered approach to aid the analysis and made it easier to convey the current state of the technology. The research then produced a framework for the development of an EWS and the applicability of sensors to the wastewater matrix. The research identified a need for the development of a strategy and guidance that can help in the prevention and detection of nitrification inhibitors. Initial tests focussed on sewer biofilm N2O emissions, however, despite average nitrification rates of 19.5 g-NH4 + - N.m- 2 .d- 1 the response was unreliable due to inadequate control. To address this, a circulating floating bed biofilm reactor (CFBBR) was designed as a sidestream. The CFBBR biofilm’s toxicity response was compared to the sewer biofilm, a 2850 mg.L- 1 MLSS culture and a 10.5 mg.L- 1 MLSS culture (with equivalent biomass concentration to the CFBBR biofilm). The cultures responded differently with an inhibitory effect scale of Cu2+ > ATU > Ni2+ > Cr6+ for CFBBR biofilm, ATU > Cu2+ > Ni2+ > Cr6+ for 2850 mg L- 1 MLSS, ATU > Ni2+ > Cr6+ > Cu2+ for 10.5 mg.L- 1 MLSS and ATU > Cu2+ > Cr6+ > Ni2+ for sewer biofilm. This was firstly attributed to suspended growth nitrification stimulation by Cu2+ doses up to ~45 mg.L- 1 resulting in a lower inhibitory effect. Secondly, very high Cr6+ and Ni2+ doses were required for biofilm nitrification inhibition, due to diffusion limitations and slow transport through cell membranes. The CFBBR biofilm response to heavy metals was characterised through N2O and CO2 spikes and a post shock emissions recovery period was observed with the trend Ni2+ > Cr6+ > Cu2+ . A 10 minute hydraulic retention time allowed quick detection and steady state nitrification rates of 0.4 g-NH4 + -N.m- 2 .d- 1 despite high organic loading rates. Additionally, a suspended growth based monitor (Nitritox) was assessed as an inlet works toxicity detector. Incorporation of a Nitritox with a CFBBR based sewer monitor offered increased robustness over a CFBBR only system and was shown to be viable system in catchments >200,000 population equivalent. This information is useful to water utilities so that they can plan for and experiment with upset early warning protocols. It is also useful to manufacturers as they can determine product performance needs.
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Conner, Christine. "Evaluating the Impact of an Early Warning Scoring System in a Community Hospital Setting." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/4846.

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Failure to recognize and respond to early signs of deterioration in hospitalized patients can have significant implications associated with delays in treatment. This lack of recognition was the impetus for rapid response teams in the United States and the recommendation by the Institute of Healthcare Improvement for use of early warning scores. This project was designed to evaluate the pilot implementation of an early warning score on 2 units in a community hospital in the Northeast. The practice-focused question was used to explore how patient outcomes changed following implementation of an early warning score (EWS) compared to patient outcomes associated with a rapid response team alone. The translating evidence into practice model informed this project. Supporting evidence from existing hospital data was collected for rapid response, code blue, and mortality. Analysis using the chi-square test of homogeneity compared post-implementation with baseline data. The findings indicated the differences between the proportions were not statistically significant, indicating the metrics did not change appreciably following the implementation of the early warning score. While the evaluation analytics of this pilot did not demonstrate significant change in the outcome measures post-implementation, the results may be useful for the facility when performing a future evaluation of the EWS. It is possible that the results of the 2 units were not representative of the facility, and it is therefore recommended to repeat the evaluation using data from the entire facility for a longer period. Increasing the capacity for early recognition in decline has implications for social change through improvement in safety and quality of health care for all hospitalized patients.
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Ceolin, Junior Tarcisio. "CORRELAÇÃO DE ALERTAS EM UM INTERNET EARLY WARNING SYSTEM." Universidade Federal de Santa Maria, 2014. http://repositorio.ufsm.br/handle/1/5439.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Intrusion 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.
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Persson, Elias, and 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.

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Pukhanov, 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.

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Excessive droughts on the African continent have caused the Swedish Meteorological and Hydrological Institute to launch a program of gathering data in hopes of producing models for rainfalls and droughts. A sensor capable of gathering such data has already been chosen, however there remains the problem of conveniently retrieving data from each of the sensors spread over a large area of land. To accomplish this goal, a small, cheap and efficient wireless capable module would need to be used. A possible candidate is the new WiFi-module from Espress if designated ESP8266. It is an extremely cheap and versatile wireless SoC that is able to perform the task of a wireless communications adapter for the sensor unit. The point of this thesis is to investigate the suitability of IEEE 802.11 for the task, and produce a piece of firmware for the ESP8266. The firmware shall enable it to be attached to a sensor and operate as a wireless mesh node in a self-organizing WLAN sensor network, enabling data retrieval via WiFi multi-hop deliveries.
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Boulton, Christopher Andrew. "Early warning signals of environmental tipping points." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18568.

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This thesis examines how early warning signals perform when tested on climate systems thought to exhibit future tipping point behaviour. A tipping point in a dynamical system is a large and sudden change to the state of the system, usually caused by changes in external forcing. This is due to the state the system occupies becoming unstable, causing the system to settle to a new stable state. In many cases, there is a degree of irreversibility once the tipping point has been passed, preventing the system from reverting back to its original state without a large reversal in forcing. Passing tipping points in climate systems, such as the Amazon rainforest or the Atlantic Meridional Overturning Circulation, is particularly dangerous as the effects of this will be globally felt. Fortunately there is potential for early warning signals, designed to warn that the system is approaching a tipping point. Generally, these early warning signals are based on analysis of the time series of the system, such as searching for ‘critical slowing down’, usually estimated by an increasing lag-1 autocorrelation (AR(1)). The idea here is that as a system’s state becomes less stable, it will start to react more sluggishly to short term perturbations. While early warning signals have been tested extensively in simple models and on palaeoclimate data, there has been very little research into how these behave in complex models and observed data. Here, early warning signals are tested on climate systems that show tipping point behaviour in general circulation models. Furthermore, it examines why early warning signals might fail in certain cases and provides prospect for more ‘system specific indicators’ based on properties of individual tipping elements. The thesis also examines how slowing down in a system might affect ecosystems that are being driven by it.
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Gadelha, 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.

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Книги з теми "Biological Early Warning System"

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Oshima, Kevin H. Ultrafiltration-based extraction for biological agents in early warning systems. Denver, Colo: Awwa Research Foundation, 2006.

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Love, Nancy G. Upset early warning systems for biological treatment processes: Source and effect relationships. Alexandria, VA: WERF, 2005.

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3

W, Long Maurice, ed. Airborne early warning system concepts. Boston: Artech House, 1992.

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4

Erica, Dodd, De Decker Ludgard, and 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.

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5

Ateya, Eltayeb Haj. Conflict early warning system for Sudan. Khartoum]: Peace Research Institute, University of Khartoum, 2006.

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6

Noveria, Mita. Pengembangan "early warning system" dalam menghadapi krisis. [Jakarta]: Puslitbang Kependudukan dan Ketenagakerjaan, Lembaga Ilmu Pengetahuan Indonesia, 2000.

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7

Abela, Tony. Malta's early warning system during World War II. [Hamrun, Malta]: SKS, 2014.

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8

Raharjo, Yulfita. Pengembangan indikator untuk 'early warning system' dalam menghadapi krisis. [Jakarta]: Puslitbang Kependudukan dan Ketenagakerjaan, Lembaga Ilmu Pengetahuan Indonesia, 2000.

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9

Chʻoe, Kong-pʻil. The early warning system for currency crises in Korea. Seoul: Korea Institute of Finance, 2001.

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10

C, Lozar Robert, and 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.

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Частини книг з теми "Biological Early Warning System"

1

Kramer, Kees J. M., and Edwin M. Foekema. "The “Musselmonitor®” as Biological Early Warning System." In 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.

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Kramer, Kees J. M. "Continuous Monitoring of Waters by Biological Early Warning Systems." In 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.

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Kholodkevich, Sergey V., Tatiana V. Kuznetsova, Svetlana V. Sladkova, Anton S. Kurakin, Alexey V. Ivanov, Vasilii A. Lyubimtsev, Eugenii L. Kornienko, and Valery P. Fedotov. "Industrial Operation of the Biological Early Warning System BioArgus for Water Quality Control Using Crayfish as a Biosensor." In Sustainable Development Goals Series, 127–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57488-8_10.

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4

Gruber, David, and William J. Rasnake. "The Use of a Biological Early Warning System to Minimize Risks Associated with Drinking Water Sources and Wastewater Discharges." In Hazardous and Industrial Waste Proceedings, 253–62. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003075905-33.

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Musavi, Syed Hyder Abbas. "Early Warning System." In 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.

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Taneja, Aarti, Aniket Desai, and Ravi S. Jakka. "Earthquake Early Warning System." In Lecture Notes in Civil Engineering, 617–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6233-4_44.

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Musavi, Syed Hyder Abbas. "Early Warning System Architecture." In 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.

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8

Tondre, Françoise. "European Warning System." In 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.

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Wu, Peng, Lei Gao, and Qiong Wang. "Early Warning System for Finance." In 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.

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Chatziadam, Panos, Ioannis G. Askoxylakis, Nikolaos E. Petroulakis, and Alexandros G. Fragkiadakis. "Early Warning Intrusion Detection System." In Trust and Trustworthy Computing, 222–23. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08593-7_22.

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Тези доповідей конференцій з теми "Biological Early Warning System"

1

Kim, Sung Yong, Ki Yong Kwon, and Won Don Lee. "A Biological Early Warning System for Toxicity Detection." In 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE, 2009. http://dx.doi.org/10.1109/ncm.2009.358.

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2

Huo, Jianling, SongTang Liu, Lei Sun, Lei Yang, Yuze Song, and Chao Li. "Research on Biological Disaster Early Warning and Decision Support System of Nuclear Power Plant." In 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9728403.

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3

Yingrong Li, Dong-Hun Seo, and Won Don Lee. "A new classifier applied to biological early warning systems for toxicity detection." In 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.

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4

Li, Yingrong, Dong-Hun Seo, and Won Don Lee. "A New Classification Application of Biological Early Warning Systems for Toxicity Detection." In 2008 International Symposium on Computer Science and its Applications (CSA). IEEE, 2008. http://dx.doi.org/10.1109/csa.2008.78.

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5

Zhou, Zixian, Zhiwen Cui, Shenxin Yin, and Tribikram Kundu. "A rapid acoustic source localization technique in early warning of building material damage- a numerical study." In Health Monitoring of Structural and Biological Systems XVI, edited by Paul Fromme and Zhongqing Su. SPIE, 2022. http://dx.doi.org/10.1117/12.2612324.

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6

Simek, Olga, Curtis Davis, Andrew Heier, Sanjeev Mohindra, Kyle O'Brien, John Passarelli, and Frederick Waugh. "XLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2018. http://dx.doi.org/10.24251/hicss.2018.118.

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7

Nanni, Stefania, and Gianluca Mazzini. "Sensornet Early-warning System Integration." In 7th International Conference on Sensor Networks. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006533100770084.

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8

Liu, Xiaoxu, Lin Cao, and Xiaoli Huang. "Highway Early Warning Information System." In 2010 2nd International Conference on Information Engineering and Computer Science (ICIECS). IEEE, 2010. http://dx.doi.org/10.1109/iciecs.2010.5677660.

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Serkov, Alexander, Sergei Nikitin, Vladimir Kravchenko, and Vladimir Knyazev. "Thunderstorm hazards early warning system." In 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.

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Clark, Rob, and Doug Burghart. "Early Warning Frost Detection System." In 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.

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Звіти організацій з теми "Biological Early Warning System"

1

VAN DER Schalie, Willian H., David E. Trader, Mark W. Widder, Tommy R. Shedd, and 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, March 2005. http://dx.doi.org/10.21236/ada432455.

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2

Goldberg, Lawrence, and Dennis Kimko. An Army Enlistment Early Warning System. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada418476.

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3

Salisbury, J. B. Earthquake early warning system for Alaska: fact sheet. Alaska Division of Geological & Geophysical Surveys, May 2020. http://dx.doi.org/10.14509/30454.

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4

Wright, Mark T., Daniel T. Gottuk, Jennifer T. Wong, Susan L. Rose-Pehrsson, and Sean Hart. Prototype Early Warning Fire Detection System: Test Series 1 Results. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada382542.

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5

Wright, Mark T., Daniel T. Gottuk, Jennifer T. Wong, Hung Pham, and Susan L. Rose-Pehrsson. Prototype Early Warning Fire Detection System: Test Series 2 Results. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383972.

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6

Geng, Xin, Manuel A. Hernandez, and 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.

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7

Dandge, Ajay Ramlal, and Vishwas Vaidya. Early Warning System for Light Commercial Engines using EMOS (Engine MOnitoring System) Controller. Warrendale, PA: SAE International, September 2010. http://dx.doi.org/10.4271/2010-32-0120.

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8

Nishino, Akihiko. Propose of Architecture Design for Early Warning System with Space and Terrestrial Infrastructure. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317284.

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Pradhan, N. S., N. Bajracharya, S. R. Bajracharya, S. K. Rai, and 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.

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Pradhan, N. S., N. Bajracharya, S. R. Bajracharya, S. K. Rai, and 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.

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