Monitoring Infection Risk for Air and Soil Borne Fungal Plant Pathogens using Antibody and DNA Techniques and Mathematical Models describing Environmental Parameters

Economic losses resulting from disease development can be reduced by accurate and early detection of plant pathogens. Early detection can provide the grower with useful information on optimal crop rotation patterns, varietal selections, appropriate control measures, harvest date and post harvest handling. Classical methods for the isolation of pathogens are commonly used only after disease symptoms. This frequently results in a delay in application of control measures at potentially important periods in crop production. This paper describes the application of both antibody and DNA based systems to monitor infection risk of air and soil borne fungal pathogens and the use of this information with mathematical models describing risk of disease associated with environmental parameters.

Integrating a mobile accessible electronic system into dockside monitoring: How can small-scale fisheries data collection programs transition from paper-based to digital data collection?

Thesis (Master's)--University of Washington, 2016-06

Design, modelling, simulation and integration of cyber physical systems: methods and applications

The main drivers for the development and evolution of Cyber Physical Systems (CPS) are the reduction of development costs and time along with the enhancement of the designed products. The aim of this survey paper is to provide an overview of different types of system and the associated transition process from mechatronics to CPS and cloud-based (IoT) systems. It will further consider the requirement that methodologies for CPS-design should be part of a multi-disciplinary development process within which designers should focus not only on the separate physical and computational components, but also on their integration and interaction. Challenges related to CPS-design are therefore considered in the paper from the perspectives of the physical processes, computation and integration respectively. Illustrative case studies are selected from different system levels starting with the description of the overlaying concept of Cyber Physical Production Systems (CPPSs). The analysis and evaluation of the specific properties of a sub-system using a condition monitoring system, important for the maintenance purposes, is then given for a wind turbine.

The development of a photoacoustic Instrument for the monitoring of oil and other hydrocarbons in water.

The present methods for the detection of oil in discharge water are based either on chemical analysis of intermittent samples or bypass pipelines with instrumentation to detect either dissolved or dispersed hydrocarbons by a variety of optical techniques including absorption, scattering and fluorescence. However, test have shown that no single instruments entirely meets either present needs or satisfies the requirements of the future more stringent legislation which may limit total hydrocarbon content to 30 ppm or even less. Hence, in this paper, a detector is devised which can detect both dissolved and dispersed oil products, has a high immunity to scattering and can operate in-line and harsh environments with a detection sensitivity of a few ppm throughout a wide range of operations.

Responsiveness of the frontal EMG for monitoring the sedation state of critically ill patients.

Background. Excessive sedation is associated with adverse patient outcomes during critical illness, and a validated monitoring technology could improve care. We developed a novel method, the responsiveness index (RI) of the frontal EMG. We compared RI data with Ramsay clinical sedation assessments in general and cardiac intensive care unit (ICU) patients. Methods. We developed the algorithm by iterative analysis of detailed observational data in 30 medical–surgical ICU patients and described its performance in this cohort and 15 patients recovering from scheduled cardiac surgery. Continuous EMG data were collected via frontal electrodes and RI data compared with modified Ramsay sedation state assessments recorded regularly by a blinded trained observer. RI performance was compared with EntropyTM across Ramsay categories to assess validity. Results. RI correlated well with the Ramsay category, especially for the cardiac surgery cohort (general ICU patients r¼0.55; cardiac surgery patients r¼0.85, both P,0.0001). Discrimination across all Ramsay categories was reasonable in the general ICU patient cohort [PK¼0.74 (SEM 0.02)] and excellent in the cardiac surgery cohort [PK¼0.92 (0.02)]. Discrimination between ‘lighter’ vs ‘deeper’ (Ramsay 1–3 vs 4–6) was good for general ICU patients [PK¼0.80 (0.02)] and excellent for cardiac surgery patients [PK¼0.96 (0.02)]. Performance was significantly better than EntropyTM. Examination of individual cases suggested good face validity. Conclusions. RI of the frontal EMG has promise as a continuous sedation state monitor in critically ill patients. Further investigation to determine its utility in ICU decision-making is warranted.

Wind turbine rotor blade monitoring using digital image correlation: a comparison to aeroelastic simulations of a multi-megawatt wind turbine

Optical full-field measurement methods such as Digital Image Correlation (DIC) provide a new opportunity for measuring deformations and vibrations with high spatial and temporal resolution. However, application to full-scale wind turbines is not trivial. Elaborate preparation of the experiment is vital and sophisticated post processing of the DIC results essential. In the present study, a rotor blade of a 3.2 MW wind turbine is equipped with a random black-and-white dot pattern at four different radial positions. Two cameras are located in front of the wind turbine and the response of the rotor blade is monitored using DIC for different turbine operations. In addition, a Light Detection and Ranging (LiDAR) system is used in order to measure the wind conditions. Wind fields are created based on the LiDAR measurements and used to perform aeroelastic simulations of the wind turbine by means of advanced multibody codes. The results from the optical DIC system appear plausible when checked against common and expected results. In addition, the comparison of relative out-of-plane blade deflections shows good agreement between DIC results and aeroelastic simulations.

Metal measurement in aquatic environments by passive sampling methods: Lessons learning from an in situ intercomparison exercise

Passive sampling devices (PS) are widely used for pollutant monitoring in water, but estimation of measurement uncertainties by PS has seldom been undertaken. The aim of this work was to identify key parameters governing PS measurements of metals and their dispersion. We report the results of an in situ intercomparison exercise on diffusive gradient in thin films (DGT) in surface waters. Interlaboratory uncertainties of time-weighted average (TWA) concentrations were satisfactory (from 28% to 112%) given the number of participating laboratories (10) and ultra-trace metal concentrations involved. Data dispersion of TWA concentrations was mainly explained by uncertainties generated during DGT handling and analytical procedure steps. We highlight that DGT handling is critical for metals such as Cd, Cr and Zn, implying that DGT assembly/dismantling should be performed in very clean conditions. Using a unique dataset, we demonstrated that DGT markedly lowered the LOQ in comparison to spot sampling and stressed the need for accurate data calculation.

Preparation of environmental monitoring plan pattern for surface water quality in Iran (Case study: Gharehsou River)

Using water quality management programs is a necessary and inevitable way for preservation and sustainable use of water resources. One of the important issues in determining the quality of water in rivers is designing effective quality control networks, so that the measured quality variables in these stations are, as far as possible, indicative of overall changes in water quality. One of the methods to achieve this goal is increasing the number of quality monitoring stations and sampling instances. Since this will dramatically increase the annual cost of monitoring, deciding on which stations and parameters are the most important ones, along with increasing the instances of sampling, in a way that shows maximum change in the system under study can affect the future decision-making processes for optimizing the efficacy of extant monitoring network, removing or adding new stations or parameters and decreasing or increasing sampling instances. This end, the efficiency of multivariate statistical procedures was studied in this thesis. Multivariate statistical procedure, with regard to its features, can be used as a practical and useful method in recognizing and analyzing rivers’ pollution and consequently in understanding, reasoning, controlling, and correct decision-making in water quality management. This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. The results of these measurements were analyzed by multivariate procedures such as: Cluster Analysis (CA), Principal Component Analysis (PCA), Factor Analysis (FA), and Discriminant Analysis (DA). Based on the findings from cluster analysis, principal component analysis, and factor analysis the stations were divided into three groups of highly polluted (HP), moderately polluted (MP), and less polluted (LP) stations Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also shows the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources. Therefore, based on the results, Gharasou river water quality monitoring program was developed and presented.

Analysis methods for meso- and macroporous silicon etching baths

Analysis methods for electrochemical etching baths consisting of various concentrations of hydrofluoric acid (HF) and an additional organic surface wetting agent are presented. These electrolytes are used for the formation of meso- and macroporous silicon. Monitoring the etching bath composition requires at least one method each for the determination of the HF concentration and the organic content of the bath. However, it is a precondition that the analysis equipment withstands the aggressive HF. Titration and a fluoride ion-selective electrode are used for the determination of the HF and a cuvette test method for the analysis of the organic content, respectively. The most suitable analysis method is identified depending on the components in the electrolyte with the focus on capability of resistance against the aggressive HF.

Application of an online-biomass sensor in an optical multisensory platform prototype for growth monitoring of biotechnical relevant microorganism and cell lines in single-use shake flasks

In the context of this work we evaluated a multisensory, noninvasive prototype platform for shake flask cultivations by monitoring three basic parameters (pH, pO2 and biomass). The focus lies on the evaluation of the biomass sensor based on backward light scattering. The application spectrum was expanded to four new organisms in addition to E. coli K12 and S. cerevisiae [1]. It could be shown that the sensor is appropriate for a wide range of standard microorganisms, e.g., L. zeae, K. pastoris, A. niger and CHO-K1. The biomass sensor signal could successfully be correlated and calibrated with well-known measurement methods like OD600, cell dry weight (CDW) and cell concentration. Logarithmic and Bleasdale-Nelder derived functions were adequate for data fitting. Measurements at low cell concentrations proved to be critical in terms of a high signal to noise ratio, but the integration of a custom made light shade in the shake flask improved these measurements significantly. This sensor based measurement method has a high potential to initiate a new generation of online bioprocess monitoring. Metabolic studies will particularly benefit from the multisensory data acquisition. The sensor is already used in labscale experiments for shake flask cultivations.

Report on JERICO Biofouling Monitoring Program (BMP)

The main aim for this document is to report after the Biofouling Monitoring Program (BMP) aiming at identifying major organisms responsible for biofouling in different geographical areas, as an input to the development and application of a more suitable approach to any specific region. This report is strongly linked to the JERICO deliverable D4.3 “Report on Biofouling Prevention Methods”, available at http://www.jerico-fp7.eu/deliverables/d4-3-report-on-biofouling-prevention-methods.

Ambient Air Quality Monitoring in Metropolitan City of Lagos, Nigeria

Twenty one sampling locations were assessed for carbon monoxide (CO), carbondioxide (CO2), oxygen (O2), sulphur dioxide (SO2), nitrogen dioxide (NO2), nitrogen oxide (NO), suspended particulate matter (SPM) and noise level using air pollutants measurement methods approved by ASTM for each specific parameter. All equipments and meters were all properly pre-calibrated before each usage for quality assurance. Findings of the study showed that measured levels of noise (61.4 - 101.4 dBA), NO (0.0 - 3.0 ppm), NO2 (0.0 - 3.0 ppm), CO (1.0 – 42.0 ppm) and SPM (0.14 – 4.82 ppm) in all sampling areas were quite high and above regulatory limits however there was no significant difference except in SPM (at all the sampling points), and noise, NO2 and NO (only in major traffic intersection). Air quality index (AQI) indicates that the ambient air can be described as poor for SPM, varied from good to very poor for CO, while NO and NO2 are very good except at major traffic intersection where they were both poor and very poor (D-E). The results suggest that strict and appropriate vehicle emission management, industrial air pollution control coupled with close burning management of wastes should be considered in the study area to reduce the risks associated with these pollutants.

Method of numerical correction of errors occasioned by delay of records during the monitoring of environmental variables of interest for animal production

As condições de ambiente térmico e aéreo, no interior de instalações para animais, alteram-se durante o dia, devido à influência do ambiente externo. Para que análises estatísticas e geoestatísticas sejam representativas, uma grande quantidade de pontos distribuídos espacialmente na área da instalação deve ser monitorada. Este trabalho propõe que a variação no tempo das variáveis ambientais de interesse para a produção animal, monitoradas no interior de instalações para animais, pode ser modelada com precisão a partir de registros discretos no tempo. O objetivo deste trabalho foi desenvolver um método numérico para corrigir as variações temporais dessas variáveis ambientais, transformando os dados para que tais observações independam do tempo gasto durante a aferição. O método proposto aproximou os valores registrados com retardos de tempo aos esperados no exato momento de interesse, caso os dados fossem medidos simultaneamente neste momento em todos os pontos distribuídos espacialmente. O modelo de correção numérica para variáveis ambientais foi validado para o parâmetro ambiental temperatura do ar, sendo que os valores corrigidos pelo método não diferiram pelo teste Tukey, a 5% de probabilidade dos valores reais registrados por meio de dataloggers.

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.