866 resultados para Monitoring Systems
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The Southern Ischia canyon system has been investigated in detail through Multibeam bathymetry and Sparker seismic data and has been put in the geological framework of the deep sea depositional systems off the Campania region. The geological and geomorphological characteristics of the canyon system have been also compared with the characters of the Mediterranean submarine canyons and with the deep sea depositional systems of the Tyrrhenian sea. The Southern Ischia canyon system engraves a narrow continental shelf from Punta Imperatore to Punta San Pancrazio, being limited southwestwards from the relict volcanic edifice of the Ischia Bank. It consists of twenty-two drainage axes, whose planimetric trending has been reconstructed in a sketch morphological map realized through the geological interpretation of Multibeam bathymetry. While the eastern boundary of the canyon system is controlled by extensional tectonics, being limited by a NE-SW trending (anti-Apenninic) normal fault, its western boundary is controlled by volcanism, due to the growth of the Ischia volcanic bank. Submarine gravitational instabilities also acted in relationships to the canyon system, allowing for the individuation of large-scale creeping at the sea bottom and hummocky deposits already interpreted as debris avalanche deposits. Quaternary marine seismic sequences have been reconstructed through a densely spaced seismic grid recorded through a Sparker multitip seismic source, allowing for a detailed observation of steep erosional slopes occurring on the southern flank of the island and related deep sea depositional systems. Important implications of this study will regard the coastal monitoring and beach nourishment of the southern flank of the island, being involved by a strong erosion of marine and coastal systems.
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There is increasing evidence of a causal link between airborne particles and ill health and this study monitored the exposure to both airborne particles and the gas phase contaminants of environmental tobacco smoke (ETS) in a nightclub. The present study followed a number of pilot studies in which the human exposure to airborne particles in a nightclub was assessed and the spatio-temporal distribution of gas phase pollutants was evaluated in restaurants and pubs. The work reported here re-examined the nightclub environment and utilized concurrent and continuous monitoring using optical scattering samplers to measure particulates (PM10) together with multi-gas analysers. The analysis illustrated the highly episodic nature of both gaseous and particulate concentrations in both the dance floor and in the bar area but levels were well below the maximum recommended exposure levels. Short-term exposure to high concentrations may however be relevant when considering the possible toxic effects on biological systems. The results give an indication of the problems associated with achieving acceptable indoor air quality (IAQ) in a complex space and identified some of the problems inherent in the design and operation of ventilation systems for such spaces.
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This paper, based on the outcome of discussions at a NORMAN Network-supported workshop in Lyon (France) in November 2014 aims to provide a common position of passive sampling community experts regarding concrete actions required to foster the use of passive sampling techniques in support of contaminant risk assessment and management and for routine monitoring of contaminants in aquatic systems. The brief roadmap presented here focusses on the identification of robust passive sampling methodology, technology that requires further development or that has yet to be developed, our current knowledge of the evaluation of uncertainties when calculating a freely dissolved concentration, the relationship between data from PS and that obtained through biomonitoring. A tiered approach to identifying areas of potential environmental quality standard (EQS) exceedances is also shown. Finally, we propose a list of recommended actions to improve the acceptance of passive sampling by policy-makers. These include the drafting of guidelines, quality assurance and control procedures, developing demonstration projects where biomonitoring and passive sampling are undertaken alongside, organising proficiency testing schemes and interlaboratory comparison and, finally, establishing passive sampler-based assessment criteria in relation to existing EQS.
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The cotton industry in Australia funds biannual disease surveys conducted by plant pathologists. The objective of these surveys is to monitor the distribution and importance of key endemic pests and record the presence or absence of new or exotic diseases. Surveys have been conducted in Queensland since 2002/03, with surveillance undertaken by experienced plant pathologists. Monitoring of endemic diseases indicates the impact of farming practices on disease incidence and severity. The information collected gives direction to cotton disease research. Routine diagnostics has provided early detection of new disease problems which include 1) the identification of Nematospora coryli, a pathogenic yeast associated with seed and internal boll rot; and 2) Rotylenchulus reniformis, a plant-parasitic nematode. This finding established the need for an intensive survey of the Theodore district revealing that reniform was prevalent across the district at populations causing up to 30% yield loss. Surveys have identified an exotic defoliating strain (VCG 1A) and non-defoliating strains of Verticillium dahliae, which cause Verticillium wilt. An intensive study of the diversity of V. dahliae and the impact these strains have on cotton are underway. Results demonstrate the necessity of general multi-pest surveillance systems in broad acre agriculture in providing (1) an ongoing evaluation of current integrated disease management practices and (2) early detection for a suite of exotic pests and previously unknown pests.
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Recent developments in automation, robotics and artificial intelligence have given a push to a wider usage of these technologies in recent years, and nowadays, driverless transport systems are already state-of-the-art on certain legs of transportation. This has given a push for the maritime industry to join the advancement. The case organisation, AAWA initiative, is a joint industry-academia research consortium with the objective of developing readiness for the first commercial autonomous solutions, exploiting state-of-the-art autonomous and remote technology. The initiative develops both autonomous and remote operation technology for navigation, machinery, and all on-board operating systems. The aim of this study is to develop a model with which to estimate and forecast the operational costs, and thus enable comparisons between manned and autonomous cargo vessels. The building process of the model is also described and discussed. Furthermore, the model’s aim is to track and identify the critical success factors of the chosen ship design, and to enable monitoring and tracking of the incurred operational costs as the life cycle of the vessel progresses. The study adopts the constructive research approach, as the aim is to develop a construct to meet the needs of a case organisation. Data has been collected through discussions and meeting with consortium members and researchers, as well as through written and internal communications material. The model itself is built using activity-based life cycle costing, which enables both realistic cost estimation and forecasting, as well as the identification of critical success factors due to the process-orientation adopted from activity-based costing and the statistical nature of Monte Carlo simulation techniques. As the model was able to meet the multiple aims set for it, and the case organisation was satisfied with it, it could be argued that activity-based life cycle costing is the method with which to conduct cost estimation and forecasting in the case of autonomous cargo vessels. The model was able to perform the cost analysis and forecasting, as well as to trace the critical success factors. Later on, it also enabled, albeit hypothetically, monitoring and tracking of the incurred costs. By collecting costs this way, it was argued that the activity-based LCC model is able facilitate learning from and continuous improvement of the autonomous vessel. As with the building process of the model, an individual approach was chosen, while still using the implementation and model building steps presented in existing literature. This was due to two factors: the nature of the model and – perhaps even more importantly – the nature of the case organisation. Furthermore, the loosely organised network structure means that knowing the case organisation and its aims is of great importance when conducting a constructive research.
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Agricultural land has been identified as a potential source of greenhouse gas emissions offsets through biosequestration in vegetation and soil. In the extensive grazing land of Australia, landholders may participate in the Australian Government’s Emissions Reduction Fund and create offsets by reducing woody vegetation clearing and allowing native woody plant regrowth to grow. This study used bioeconomic modelling to evaluate the trade-offs between an existing central Queensland grazing operation, which has been using repeated tree clearing to maintain pasture growth, and an alternative carbon and grazing enterprise in which tree clearing is reduced and the additional carbon sequestered in trees is sold. The results showed that ceasing clearing in favour of producing offsets produces a higher net present value over 20 years than the existing cattle enterprise at carbon prices, which are close to current (2015) market levels (~$13 t–1 CO2-e). However, by modifying key variables, relative profitability did change. Sensitivity analysis evaluated key variables, which determine the relative profitability of carbon and cattle. In order of importance these were: the carbon price, the gross margin of cattle production, the severity of the tree–grass relationship, the area of regrowth retained, the age of regrowth at the start of the project, and to a lesser extent the cost of carbon project administration, compliance and monitoring. Based on the analysis, retaining regrowth to generate carbon income may be worthwhile for cattle producers in Australia, but careful consideration needs to be given to the opportunity cost of reduced cattle income.
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Climate change and carbon (C) sequestration are a major focus of research in the twenty-first century. Globally, soils store about 300 times the amount of C that is released per annum through the burning of fossil fuels (Schulze and Freibauer 2005). Land clearing and introduction of agricultural systems have led to rapid declines in soil C reserves. The recent introduction of conservation agricultural practices has not led to a reversing of the decline in soil C content, although it has minimized the rate of decline (Baker et al. 2007; Hulugalle and Scott 2008). Lal (2003) estimated the quantum of C pools in the atmosphere, terrestrial ecosystems, and oceans and reported a “missing C” component in the world C budget. Though not proven yet, this could be linked to C losses through runoff and soil erosion (Lal 2005) and a lack of C accounting in inland water bodies (Cole et al. 2007). Land management practices to minimize the microbial respiration and soil organic C (SOC) decline such as minimum tillage or no tillage were extensively studied in the past, and the soil erosion and runoff studies monitoring those management systems focused on other nutrients such as nitrogen (N) and phosphorus (P).
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Energy efficient policies are being applied to network protocols, devices and classical network management systems. Researchers have already studied in depth each of those fields, including for instance a long monitoring processes of various number of individual ICT equipment from where power models are constructed. With the development of smart meters and emerging protocols such as SNMP and NETCONF, currently there is an open field to couple the power models, translated to the expected behavior, with the realtime energy measurements. The goal is to derive a comparison on the power data between both of the processes in the direction of detection for possible deviations on the expected results. The logical assumption is that a fault in the usage of a particular device will not only increase its own energy usage, but also may cause additional consumption on the other devices part of the network. A platform is developed to monitor and analyze the retrieved power data of a simulated enterprise ICT infrastructure. Moreover, smart algorithms are developed which are aware of the different states that are occurring on each device during their typical use phase, as well as to detect and isolate possible anomalies. The produced results are obtained and validated with the use of Cisco switches and routers, Dell Precision stations and Raritan PDU as part of the monitored infrastructure.
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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.
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Part 14: Interoperability and Integration
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[EN] Herein we investigate the feasibility of detecting photo-induced surface stress changes using the deflection response of cantilevers. For this purpose, silicon microcantilevers have been functionalised with spiropyran photochromic molecules, using both a monolayer and a polymeric brushes approach. Uponultraviolet light irradiation, the spiropyran unit is converted to the merocyanine form due to the photo-induced cleavage of the Cspiro-O bond. The two forms of the molecule have dramatically different charge,polarity and molecular conformations. This makes spiropyrans an ideal system to study the correlation between photo-induced molecular changes and corresponding changes in surface stress. Our investigations include monitoring the changes in static cantilever deflection, and consequently, surface stress of the spiropyran functionalised cantilevers on exposure to ultraviolet light. Cantilever deflection data reveals that ultraviolet induced conformational changes in the spiropyran moiety cause a change incompressive surface stress and this varies with the type of functionalisation method implemented. The change in surface stress response from the spiropyran polymer brushes functionalised cantilevers gives an average surface stress change of 98 Nm−1(n = 24) while the spiropyran monolayer coated cantilevers have an average surface stress change of about 446 Nm−1(n = 8) upon irradiation with UV light.
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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.
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Modern power networks incorporate communications and information technology infrastructure into the electrical power system to create a smart grid in terms of control and operation. The smart grid enables real-time communication and control between consumers and utility companies allowing suppliers to optimize energy usage based on price preference and system technical issues. The smart grid design aims to provide overall power system monitoring, create protection and control strategies to maintain system performance, stability and security. This dissertation contributed to the development of a unique and novel smart grid test-bed laboratory with integrated monitoring, protection and control systems. This test-bed was used as a platform to test the smart grid operational ideas developed here. The implementation of this system in the real-time software creates an environment for studying, implementing and verifying novel control and protection schemes developed in this dissertation. Phasor measurement techniques were developed using the available Data Acquisition (DAQ) devices in order to monitor all points in the power system in real time. This provides a practical view of system parameter changes, system abnormal conditions and its stability and security information system. These developments provide valuable measurements for technical power system operators in the energy control centers. Phasor Measurement technology is an excellent solution for improving system planning, operation and energy trading in addition to enabling advanced applications in Wide Area Monitoring, Protection and Control (WAMPAC). Moreover, a virtual protection system was developed and implemented in the smart grid laboratory with integrated functionality for wide area applications. Experiments and procedures were developed in the system in order to detect the system abnormal conditions and apply proper remedies to heal the system. A design for DC microgrid was developed to integrate it to the AC system with appropriate control capability. This system represents realistic hybrid AC/DC microgrids connectivity to the AC side to study the use of such architecture in system operation to help remedy system abnormal conditions. In addition, this dissertation explored the challenges and feasibility of the implementation of real-time system analysis features in order to monitor the system security and stability measures. These indices are measured experimentally during the operation of the developed hybrid AC/DC microgrids. Furthermore, a real-time optimal power flow system was implemented to optimally manage the power sharing between AC generators and DC side resources. A study relating to real-time energy management algorithm in hybrid microgrids was performed to evaluate the effects of using energy storage resources and their use in mitigating heavy load impacts on system stability and operational security.
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This research investigates the implementation of battery-less RFID sensing platforms inside lossy media, such as, concrete and grout. Both concrete and novel grouts can be used for nuclear plant decommissioning as part of the U.S. Department of Energy’s (DOE’s) cleanup projects. Our research examines the following: (1) material characterization, (2) analytical modeling of transmission and propagation losses inside lossy media, (3) maximum operational range of RFID wireless sensors embedded inside concrete and grout, and (4) best positioning of antennas for achieving longer communication range between RFID antennas and wireless sensors. Our research uses the battery-less Wireless Identification and Sensing Platform (WISP) which can be used to monitor temperature, and humidity inside complex materials. By using a commercial Agilent open-ended coaxial probe (HP8570B), the measurements of the dielectric permittivity of concrete and grout are performed. Subsequently, the measured complex permittivity is used to formulate analytical Debye models. Also, the transmission and propagation losses of a uniform plane wave inside grout are calculated. Our results show that wireless sensors will perform better in concrete than grout. In addition, the maximum axial and radial ranges for WISP are experimentally determined. Our work illustrates the feasibility of battery-less wireless sensors that are embedded inside concrete and grout. Also, our work provides information that can be used to optimize the power management, sampling rate, and antenna design of such sensors.
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This paper presents a monitoring system devoted to small sized photovoltaic (PV) power plants. The system is characterized by: a high level of integration; a low cost, when compared to the cost of the PV system to be monitored; and an easy installation in the majority of the PV plants with installed power of some kW. The system is able to collect, store, process and display electrical and meteorological parameters that are crucial when monitoring PV facilities. The identification of failures in the PV system and the elaboration of performance analysis of such facilities are other important characteristics of the developed system. The access to the information about the monitored facilities is achieved by using a web application, which was developed with a focus on the mobile devices. In addition, there is the possibility of an integration between the developed monitoring system and the central supervision system of Martifer Solar (a company focused on the development, operation and maintenance of PV systems).