State tagging for improved Earth and environmental data quality assurance

Environmental data allows us to monitor the constantly changing environment that we live in. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term “state” here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. The users can then apply further analysis and filtering as they see fit. We illustrate our approach with two well-established long-term monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily

Advanced Data Mining and Machine Learning Algorithms for Integrated Computer-Based Analyses of Big Environmental Databases

Einsicht in die räumliche Verteilung geotechnischer und hydrologischer Untergrundeigenschaften sowie von Reservoir- und Umweltparametern sind grundlegend für geowissenschaftliche Forschungen. Entwicklungen in den Bereichen geophysikalische Erkundung sowie Fernerkundung resultieren in der Verfügbarkeit verschiedenster Verfahren für die nichtinvasive, räumlich kontinuierliche Datenerfassung im Rahmen hochauflösender Messverfahren. In dieser Arbeit habe ich verschiedene Verfahren für die Analyse erdwissenschaftlicher Datenbasen entwickelt auf der Basis von Wissenserschließungsverfahren. Eine wichtige Datenbasis stellt geophysikalische Tomographie dar, die als einziges geowissenschaftliches Erkundungsverfahren 2D und 3D Abbilder des Untergrunds liefern kann. Mittels unterschiedlicher Verfahren aus den Bereichen intelligente Datenanalyse und maschinelles Lernen (z.B. Merkmalsextraktion, künstliche neuronale Netzwerke, etc.) habe ich ein Verfahren zur Datenanalyse mittels künstlicher neuronaler Netzwerke entwickelt, das die räumlich kontinuierliche 2D oder 3D Vorhersage von lediglich an wenigen Punkten gemessenen Untergrundeigenschaften im Rahmen von Wahrscheinlichkeitsaussagen ermöglicht. Das Vorhersageverfahren basiert auf geophysikalischer Tomographie und berücksichtigt die Mehrdeutigkeit der tomographischen Bildgebung. Außerdem wird auch die Messunsicherheit bei der Erfassung der Untergrundeigenschaften an wenigen Punkten in der Vorhersage berücksichtigt. Des Weiteren habe ich untersucht, ob aus den Trainingsergebnissen künstlicher neuronaler Netzwerke bei der Vorhersage auch Aussagen über die Realitätsnähe mathematisch gleichwertiger Lösungen der geophysikalischen tomographischen Bildgebung abgeleitet werden können. Vorhersageverfahren wie das von mir vorgeschlagene, können maßgeblich zur verbesserten Lösung hydrologischer und geotechnischer Fragestellungen beitragen. Ein weiteres wichtiges Problem ist die Kartierung der Erdoberfläche, die von grundlegender Bedeutung für die Bearbeitung verschiedener ökonomischer und ökologischer Fragestellungen ist, wie z.B., die Identifizierung von Lagerstätten, den Schutz von Böden, oder Ökosystemmanagement. Kartierungsdaten resultieren entweder aus technischen (objektiven) Messungen oder visuellen (subjektiven) Untersuchungen durch erfahrene Experten. Im Rahmen dieser Arbeit zeige ich erste Entwicklungen hin zu einer automatisierten und schnellen Integration technischer und visueller (subjektiver) Daten auf der Basis unterschiedlicher intelligenter Datenanalyseverfahren (z.B., Graphenanalyse, automatische Konturerfassung, Clusteranalyse, etc.). Mit solchem Verfahren sollen hart oder weich klassifizierte Karten erstellt werden, die das Untersuchungsgebiet optimal segmentieren um höchstmögliche Konformität mit allen verfügbaren Daten zu erzielen

Listeria monocytogenes and other Listeria species in small and very small ready-to-eat meat processing plants

2010 Fall.Includes bibliographical references.Listeria monocytogenes is the causative agent of listeriosis, a severe foodborne disease associated with a high case fatality rate. To prevent product contamination with L. monocytogenes, it is crucial to understand Listeria contamination patterns in the food processing plant environment. The aim of this study was to monitor Listeria contamination patterns for two years in six small or very small ready-to-eat (RTE) meat processing plants using a routine combined cultural and molecular typing program. Each of the six plants enrolled in the study were visited on a bi-monthly basis for a two-year period where samples were collected, microbiologically analyzed for Listeria and isolates from positive samples were characterized by molecular subtyping. Year one of the project focused only on non-food contact environmental samples within each plant, and year two focused again on non-food contact environmental samples as well as food contact surfaces and finished RTE meat product samples from participating plants. Between year one and year two of sampling, we conducted an in-plant training session involving all employees at each plant. During this training session, we informed employees about general Listeria knowledge such as ecology, transmission and control strategies. Also, we informed each plant of the testing and molecular subtyping results obtained in the first year of the study. Employees also were given a pre- and post-training evaluation, which included 23 questions on Listeria, to probe knowledge gained through the training session. A common characteristic among almost all plants enrolled in our study was the persistence of a single or few predominant L. monocytogenes and/or other Listeria spp. molecular subtype(s) in the plant environment. Identification of persistent strains and their associated harborage sites in the environment of each plant highlight the continued need for adequate cleaning and sanitation practices to eliminate harborage sites and reduce the risk of transmission to the finished product. Interestingly, we not only observed a significant increase in plant employee knowledge regarding Listeria following the in-plant training sessions, but we also detected a significant decrease in Listeria contamination across all six plants when comparing testing results from year 1 and 2. With combined molecular detection and subtyping, we were able to help increase plant awareness about Listeria contamination patterns, identify harborage sites and intervention strategies to better control Listeria in the plant environment

Sensors Application in Agriculture

Novel technologies are playing an important role in the development of crop and livestock farming and have the potential to be the key drivers of sustainable intensification of agricultural systems. In particular, new sensors are now available with reduced dimensions, reduced costs, and increased performances, which can be implemented and integrated in production systems, providing more data and eventually an increase in information. It is of great importance to support the digital transformation, precision agriculture, and smart farming, and to eventually allow a revolution in the way food is produced. In order to exploit these results, authoritative studies from the research world are still needed to support the development and implementation of new solutions and best practices. This Special Issue is aimed at bringing together recent developments related to novel sensors and their proved or potential applications in agriculture

NASA Technology Applications Team: Commercial applications of aerospace technology

The Research Triangle Institute (RTI) Team has maintained its focus on helping NASA establish partnerships with U.S. industry for dual use development and technology commercialization. Our emphasis has been on outcomes, such as licenses, industry partnerships and commercialization of technologies, that are important to NASA in its mission of contributing to the improved competitive position of U.S. industry. The RTI Team has been successful in the development of NASA/industry partnerships and commercialization of NASA technologies. RTI ongoing commitment to quality and customer responsiveness has driven our staff to continuously improve our technology transfer methodologies to meet NASA's requirements. For example, RTI has emphasized the following areas: (1) Methodology For Technology Assessment and Marketing: RTI has developed and implemented effective processes for assessing the commercial potential of NASA technologies. These processes resulted from an RTI study of best practices, hands-on experience, and extensive interaction with the NASA Field Centers to adapt to their specific needs. (2) Effective Marketing Strategies: RTI surveyed industry technology managers to determine effective marketing tools and strategies. The Technology Opportunity Announcement format and content were developed as a result of this industry input. For technologies with a dynamic visual impact, RTI has developed a stand-alone demonstration diskette that was successful in developing industry interest in licensing the technology. And (3) Responsiveness to NASA Requirements: RTI listened to our customer (NASA) and designed our processes to conform with the internal procedures and resources at each NASA Field Center and the direction provided by NASA's Agenda for Change. This report covers the activities of the Research Triangle Institute Technology Applications Team for the period 1 October 1993 through 31 December 1994

NASA Technology Applications Team: Commercial applications of aerospace technology

The Research Triangle Institute (RTI) is pleased to report the results of NASA contract NASW-4367, 'Operation of a Technology Applications Team'. Through a period of significant change within NASA, the RTI Team has maintained its focus on helping NASA establish partnerships with U.S. industry for dual use development and technology commercialization. Our emphasis has been on outcomes, such as licenses, industry partnerships and commercialization of technologies that are important to NASA in its mission of contributing to the improved competitive position of U.S. industry. RTI's ongoing commitment to quality and customer responsiveness has driven our staff to continuously improve our technology transfer methodologies to meet NASA's requirements. For example, RTI has emphasized the following areas: (1) Methodology For Technology Assessment and Marketing: RTI has developed an implemented effective processes for assessing the commercial potential of NASA technologies. These processes resulted from an RTI study of best practices, hands-on experience, and extensive interaction with the NASA Field Centers to adapt to their specific needs; (2) Effective Marketing Strategies: RTI surveyed industry technology managers to determine effective marketing tools and strategies. The Technology Opportunity Announcement format and content were developed as a result of this industry input. For technologies with a dynamic visual impact, RTI has developed a stand-alone demonstration diskette that was successful in developing industry interest in licensing the technology; and (3) Responsiveness to NASA Requirements: RTI listened to our customer (NASA) and designed our processes to conform with the internal procedures and resources at each NASA Field Center and the direction provided by NASA's Agenda for Change. This report covers the activities of the Research Triangle Institute Technology Applications Team for the period 1 October 1993 through 31 December 1994

NASA technology applications team: Applications of aerospace technology

This report covers the activities of the Research Triangle Institute (RTI) Technology Applications Team for the period 1 October 1992 through 30 September 1993. The work reported herein was supported by the National Aeronautics and Space Administration (NASA), Contract No. NASW-4367. Highlights of the RTI Applications Team activities over the past year are presented in Section 1.0. The Team's progress in fulfilling the requirements of the contract is summarized in Section 2.0. In addition to our market-driven approach to applications project development, RTI has placed increased effort on activities to commercialize technologies developed at NASA Centers. These Technology Commercialization efforts are summarized in Section 3.0. New problem statements prepared by the Team in the reporting period are presented in Section 4.0. The Team's transfer activities for ongoing projects with the NASA Centers are presented in Section 5.0. Section 6.0 summarizes the status of four add-on tasks. Travel for the reporting period is described in Section 7.0. The RTI Team staff and consultants and their project responsibilities are listed in Appendix A. Appendix B includes Technology Opportunity Announcements and Spinoff! Sheets prepared by the Team while Appendix C contains a series of technology transfer articles prepared by the Team

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production