Utilização de sondas lambda em caldeiras de biomassa

Nos dias que correm a eficiência energética está na ordem do dia, havendo um esforço significativo para obter-se equipamentos cada vez mais eficientes. Uma parte significativa do consumo global de energia, bem como da emissão de gases nocivos e de efeito de estufa, está associado à climatização, quer doméstica quer industrial. Assim, é importante desenvolver tecnologias mais eficientes neste domínio. O principal objetivo deste trabalho consiste no desenvolvimento de um módulo de interface a uma sonda lambda para monitorização de combustão em caldeiras de biomassa. Este módulo permitirá medir a concentração de oxigénio presente na saída das caldeiras, possibilitando o ajuste dinâmico dos parâmetros de combustão por forma a maximizar o seu rendimento e minimizar a emissão de gases poluentes. O módulo desenvolvido é de baixo custo e apresenta uma interface bastante simples, facilitando a sua incorporação em equipamentos já existentes. Os resultados obtidos revelaram-se consistentes com os valores teóricos fornecidos pelo fabricante da sonda utilizada, podendo assim concluir-se que o trabalho foi realizado com sucesso.

Processing Argo oxygen data at the DAC level cookbook

This document does NOT address the issue of oxygen data quality control (either real-time or delayed mode). As a preliminary step towards that goal, this document seeks to ensure that all countries deploying floats equipped with oxygen sensors document the data and metadata related to these floats properly. We produced this document in response to action item 14 from the AST-10 meeting in Hangzhou (March 22-23, 2009). Action item 14: Denis Gilbert to work with Taiyo Kobayashi and Virginie Thierry to ensure DACs are processing oxygen data according to recommendations. If the recommendations contained herein are followed, we will end up with a more uniform set of oxygen data within the Argo data system, allowing users to begin analysing not only their own oxygen data, but also those of others, in the true spirit of Argo data sharing. Indications provided in this document are valid as of the date of writing this document. It is very likely that changes in sensors, calibrations and conversions equations will occur in the future. Please contact V. Thierry (vthierry@ifremer.fr) for any inconsistencies or missing information. A dedicated webpage on the Argo Data Management website (www) contains all information regarding Argo oxygen data management : current and previous version of this cookbook, oxygen sensor manuals, calibration sheet examples, examples of matlab code to process oxygen data, test data, etc..

“Deep-Arvor”: A new profiling float to extend the Argo observations down to 4000m depth.

The international Argo program, consisting of a global array of more than 3000 free-drifting profiling floats, has now been monitoring the upper 2000 meters of the ocean for several years. One of its main proposed evolutions is to be able to reach the deeper ocean in order to better observe and understand the key role of the deep ocean in the climate system. For this purpose, Ifremer has designed the new “Deep-Arvor” profiling float: it extends the current operational depth down to 4000 meters, and measures temperature and salinity for up to 150 cycles with CTD pumping continuously and 200 cycles in spot sampling mode. High resolution profiles (up to 2000 points) can be transmitted and data are delivered in near real time according to Argo requirements. Deep-Arvor can be deployed everywhere at sea without any pre-ballasting operation and its light weight (~ 26kg) makes its launching easy. Its design was done to target a cost effective solution. Predefined spots have been allocated to add an optional oxygen sensor and a connector for an extra sensor. Extensive laboratory tests were successful. The results of the first at sea experiments showed that the expected performances of the operational prototypes had been reached (i.e. to perform up to 150 cycles). Meanwhile, the industrialization phase was completed in order to manufacture the Deep-Arvor float for the pilot experiment in 2015. In this paper, we detail all the steps of the development work and present the results from the at sea experiments.

SCOR WG 142: Quality Control Procedures for Oxygen and Other Biogeochemical Sensors on Floats and Gliders. Recommendation for oxygen measurements from Argo floats, implementation of in-air-measurement routine to assure highest long-term accuracy

Recommendation for Oxygen Measurements from Argo Floats: Implementation of In-Air-Measurement Routine to Assure Highest Long-term Accuracy As Argo has entered its second decade and chemical/biological sensor technology is improving constantly, the marine biogeochemistry community is starting to embrace the successful Argo float program. An augmentation of the global float observatory, however, has to follow rather stringent constraints regarding sensor characteristics as well as data processing and quality control routines. Owing to the fairly advanced state of oxygen sensor technology and the high scientific value of oceanic oxygen measurements (Gruber et al., 2010), an expansion of the Argo core mission to routine oxygen measurements is perhaps the most mature and promising candidate (Freeland et al., 2010). In this context, SCOR Working Group 142 “Quality Control Procedures for Oxygen and Other Biogeochemical Sensors on Floats and Gliders” (www.scor-int.org/SCOR_WGs_WG142.htm) set out in 2014 to assess the current status of biogeochemical sensor technology with particular emphasis on float-readiness, develop pre- and post-deployment quality control metrics and procedures for oxygen sensors, and to disseminate procedures widely to ensure rapid adoption in the community.

A Bayesian network dealing with measurements and residuals for system monitoring

International audience

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

A service-oriented user interaction analysis framework supporting adaptive applications

A comprehensive user model, built by monitoring a user's current use of applications, can be an excellent starting point for building adaptive user-centred applications. The BaranC framework monitors all user interaction with a digital device (e.g. smartphone), and also collects all available context data (such as from sensors in the digital device itself, in a smart watch, or in smart appliances) in order to build a full model of user application behaviour. The model built from the collected data, called the UDI (User Digital Imprint), is further augmented by analysis services, for example, a service to produce activity profiles from smartphone sensor data. The enhanced UDI model can then be the basis for building an appropriate adaptive application that is user-centred as it is based on an individual user model. As BaranC supports continuous user monitoring, an application can be dynamically adaptive in real-time to the current context (e.g. time, location or activity). Furthermore, since BaranC is continuously augmenting the user model with more monitored data, over time the user model changes, and the adaptive application can adapt gradually over time to changing user behaviour patterns. BaranC has been implemented as a service-oriented framework where the collection of data for the UDI and all sharing of the UDI data are kept strictly under the user's control. In addition, being service-oriented allows (with the user's permission) its monitoring and analysis services to be easily used by 3rd parties in order to provide 3rd party adaptive assistant services. An example 3rd party service demonstrator, built on top of BaranC, proactively assists a user by dynamic predication, based on the current context, what apps and contacts the user is likely to need. BaranC introduces an innovative user-controlled unified service model of monitoring and use of personal digital activity data in order to provide adaptive user-centred applications. This aims to improve on the current situation where the diversity of adaptive applications results in a proliferation of applications monitoring and using personal data, resulting in a lack of clarity, a dispersal of data, and a diminution of user control.

Smart Textile Sensors for Healthcare Monitoring

Wearable electronic textiles are an emerging research field playing a pivotal role among several different technological areas such as sensing, communication, clothing, health monitoring, information technology, and microsystems. The possibility to realise a fully-textile platform, endowed with various sensors directly realised with textile fibres and fabric, represents a new challenge for the entire research community. Among several high-performing materials, the intrinsically conductive poly(3,4-ethylenedioxythiophene) (PEDOT), doped with poly(styrenesulfonic acid) (PSS), or PEDOT:PSS, is one of the most representative and utilised, having an excellent chemical and thermal stability, as well as reversible doping state and high conductivity. This work relies on PEDOT:PSS combined with sensible materials to design, realise, and develop textile chemical and physical sensors. In particular, chloride concentration and pH level sensors in human sweat for continuous monitoring of the wearer's hydration status and stress level are reported. Additionally, a prototype smart bandage detecting the moisture level and pH value of a bed wound to allow the remote monitoring of the healing process of severe and chronic wounds is described. Physical sensors used to monitor the pressure distribution for rehabilitation, workplace safety, or sport tracking are also presented together with a novel fully-textile device able to measure the incident X-ray dose for medical or security applications where thin, comfortable, and flexible features are essential. Finally, a proof-of-concept for an organic-inorganic textile thermoelectric generator that harvests energy directly from body heat has been proposed. Though further efforts must be dedicated to overcome issues such as durability, washability, power consumption, and large-scale production, the novel, versatile, and widely encompassing area of electronic textiles is a promising protagonist in the upcoming technological revolution.

Influencia do treinamento fisico aerobio sobre as respostas cardiovasculares e respiratorias em mulheres na menopausa com e sem terapia de reposição hormonal

Universidade Estadual de Campinas. Faculdade de Educação Física

Determinação de aflatoxina B1 em rações e aflatoxina M1 no leite de propriedades do Estado de São Paulo

A ocorrência de aflatoxina B1 (AFB1) em rações e aflatoxina M1 (AFM1) no leite cru foi avaliada em propriedades leiteiras situadas na região nordeste do Estado de São Paulo, Brasil, de outubro de 2005 a fevereiro de 2006. A análise de aflatoxinas foi efetuada utilizando-se colunas de imunoafinidade para purificação dos extratos, sendo a quantificação realizada através de cromatografia líquida de alta eficiência. A AFB1 foi detectada em 40% das rações em níveis de 1,0 a 19,5 μg.kg-1. A concentração de AFM1 em 36,7% de amostras de leite positivas variou de 0,010 a 0,645 μg.L-1. Somente uma amostra de leite estava acima do limite de tolerância adotado no Brasil (0,5 μg.L-1) para AFM1. Concluiu-se que as concentrações de aflatoxinas na ração e no leite foram relativamente baixas, embora a alta frequência das aflatoxinas nas amostras analisadas indique a necessidade de contínuo monitoramento a fim de prevenir a contaminação de ingredientes e rações destinadas ao gado leiteiro.

An Inhalation Chamber Model for Controlled Studies of Tobacco Smoke Toxicity in Rodents

Introduction: Smoking is a serious worldwide public health problem. Animal models act as a bridge between laboratory and human studies. The models applied are difficult to reproduce because of the use of different types of inhalation chambers and mainly because of the lack of continuous monitoring of smoke concentration. Objective: To develop an inhalation chamber for rats (with only the nose exposed) in which the amount of carbon monoxide (CO) can be maintained and monitored constantly. Material and methods: Male Wistar rats weighing 250 g were exposed to 50 ppm CO produced by the smoke from a filter-free cigarette. The animals were submitted to a single 2-h exposure and then sacrificed at 0, 4, 24 and 48 h. The control group was left restrained inside the small perpendicular chambers, receiving only 5 L/min of compressed air. Results: The model was able to increase HbCO levels immediately after the end of exposure (p < 0.001). with a decrease being observed from 2 h onwards when compared to the levels of the control group. Plasma cotinine increased immediately after exposure, and showed still detectable levels at 2 and 4 h (p < 0.05). Conclusion: We conclude that the presented inhalation chamber system is able to maintain a controlled CO concentration in a model in which small animals are exposed to the inhalation of cigarette smoke, permitting well-controlled studies, as well as investigations involving other toxic gases and air pollutants. (C) 2008 SEPAR. Published by Elsevier Espana, S.L. All rights reserved.

Correcting the effects of spontaneous emission on cold-trapped ions

We propose two quantum error-correction schemes which increase the maximum storage time for qubits in a system of cold-trapped ions, using a minimal number of ancillary qubits. Both schemes consider only the errors introduced by the decoherence due to spontaneous emission from the upper levels of the ions. Continuous monitoring of the ion fluorescence is used in conjunction with selective coherent feedback to eliminate these errors immediately following spontaneous emission events.

The relationship between leaf water potential and stem diameter in sorghum

Leaf water potential (psi (l)) represents a good indicator of the water status of plants, and continuous monitoring of it can be useful in research and field applications such as scheduling irrigation. Changes in stem diameter (Sd) were used for monitoring psi (l) of pot-grown sorghum [Sorghum bicolor (L.) Moench] plants in a glasshouse. This method requires occasional calibration of S-d values against psi (l). Predicted values of psi (l), based on a single calibration show a good correlation with measured psi (l), values over a period of 13 d before and after the calibration. The correlation can further be improved with shorter time intervals.

Cyclosporin monitoring in Australasia: 2002 update of consensus guidelines

Therapeutic drug monitoring of cyclosporin (CsA) has been established as part of the routine clinical treatment of patients following organ transplantation for more than 20 years, and based on contemporary knowledge, many consensus guidelines have been published to assist clinics and laboratories attain optimal strategies for patient care. This article addresses the newer directions in CsA monitoring, with particular reference to the Australasian situation that has evolved since the 1993 Australasian guideline (1). These changes have included the introduction of alternative assay methodologies, changed CsA formulation from Sandimmun to Neoral throughout Australasia, and alternatives to trough concentration (C0) monitoring, especially 2-hour concentration (C2) monitoring and associated validated dilution protocols to accurately quantitate the higher whole blood CsA concentrations. The revision was prepared following a recent survey of all Australasian CsA-monitoring laboratories (2) where discordant practices were evident.

Online titrimetric and off-gas analysis for examining nitrification processes in wastewater treatment

The two steps of nitrification, namely the oxidation of ammonia to nitrite and nitrite to nitrate, often need to be considered separately in process studies. For a detailed examination, it is desirable to monitor the two-step sequence using online measurements. In this paper, the use of online titrimetric and off-gas analysis (TOGA) methods for the examination of the process is presented. Using the known reaction stoichiometry, combination of the measured signals (rates of hydrogen ion production, oxygen uptake and carbon dioxide transfer) allows the determination of the three key process rates, namely the ammonia consumption rate, the nitrite accumulation rate and the nitrate production rate. Individual reaction rates determined with the TOGA sensor under a number of operation conditions are presented. The rates calculated directly from the measured signals are compared with those obtained from offline liquid sample analysis. Statistical analysis confirms that the results from the two approaches match well. This result could not have been guaranteed using alternative online methods. As a case study, the influences of pH and dissolved oxygen (DO) on nitrite accumulation are tested using the proposed method. It is shown that nitrite accumulation decreased with increasing DO and pH. Possible reasons for these observations are discussed. (C) 2003 Elsevier Science Ltd. All rights reserved.