Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird

Diagnosis of multiple faults in rotating machinery using ensemble learning

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining

Statistical and deep learning methods for geoscience problems

Machine learning is the new frontier for technology development in geosciences and has developed extremely fast in the past decade. With the increased compute power provided by distributed computing and Graphics Processing Units (GPUs) and their exploitation provided by machine learning (ML) frameworks such as Keras, Pytorch, and Tensorflow, ML algorithms can now solve complex scientific problems. Although powerful, ML algorithms need to be applied to suitable problems conditioned for optimal results. For this reason ML algorithms require not only a deep understanding of the problem but also of the algorithm’s ability. In this dissertation, I show that Simple statistical techniques can often outperform ML-based models if applied correctly. In this dissertation, I show the success of deep learning in addressing two difficult problems. In the first application I use deep learning to auto-detect the leaks in a carbon capture project using pressure field data acquired from the DOE Cranfield site in Mississippi. I use the history of pressure, rates, and cumulative injection volumes to detect leaks as pressure anomaly. I use a different deep learning workflow to forecast high-energy electrons in Earth’s outer radiation belt using in situ measurements of different space weather parameters such as solar wind density and pressure. I focus on predicting electron fluxes of 2 MeV and higher energy and introduce the ensemble of deep learning models to further improve the results as compared to using a single deep learning architecture. I also show an example where a carefully constructed statistical approach, guided by the human interpreter, outperforms deep learning algorithms implemented by others. Here, the goal is to correlate multiple well logs across a survey area in order to map not only the thickness, but also to characterize the behavior of stacked gamma ray parasequence sets. Using tools including maximum likelihood estimation (MLE) and dynamic time warping (DTW) provides a means of generating quantitative maps of upward fining and upward coarsening across the oil field. The ultimate goal is to link such extensive well control with the spectral attribute signature of 3D seismic data volumes to provide a detailed maps of not only the depositional history, but also insight into lateral and vertical variation of mineralogy important to the effective completion of shale resource plays

TECHNOLOGY ASSESSMENT FOR SUSTAINABILITY IN WATER USE. OPERATIONALIZATION OF A RESPONSIBLE GOVERNANCE BASED IN RESPONSIBLE RESEARCH AND INNOVATION (ANTICIPATION AND INCLUSIVENESS)

The management of sustainability in water resources has underscored the critical importance of determining appropriate decision-making processes and establishing effective governance structures. Gaining comprehensive insights into the decision-making mechanisms and actors involved is pivotal for tackling present as well as prospective issues related to water efficiently. This research evaluates the interplay among water scarcity, responsible technologies for water use, and systems of governance for sustainability amid swift technological progress. Furthermore, it delves into the congruity of said endeavors with the Sustainable Development Goals (SDGs), other sustainability water frameworks and the social and political ecosystem. In this context, the active engagement and participation of societal actors, and not only stakeholders, assume a pivotal role as it significantly impacts the decision-making processes and molds the results of sustainability initiatives. An innovative approach to the concepts of responsibility and sustainability is predicated on the quality of the relationship between the network of societal actors as a key point. This work underscores the importance of establishing strong and comprehensive relationships to address the challenges concerning water management and promote the adoption of sustainable approaches, in co-creation, not only of knowledge but the epistemic subject in the process. This work sheds light on the interrelated domains of water management, sustainability, and regulation. A novel proposal is presented via a simulation exercise and use the socio-technical framework for the purpose of fostering responsible water use. The comprehension and use of responsible technology and innovation in the realm of water u management will be enhanced through the technique of operationalizing open anticipatory governance and executing a simulated experiment. By using a digital deliberation space and establishing a systematic approach towards technology assessment and sustainability, using the relational quality of the network of actors as the key element for co-production of knowledge, science and technology, the present study has produced and materialized an innovative framework.Na sustentabilidade da gestão da água reveste-se de especial importância determinar processos de tomada de decisão adequados e estabelecer estruturas de governação eficazes. Obter uma visão abrangente sobre os mecanismos de tomada de decisão e os atores envolvidos é fundamental para abordar questões presentes e futuras relacionadas ao uso eficiente da água. Este trabalho procura conhecer a interação entre gestão de água, tecnologias responsáveis pelo uso da água e sistemas de governança para a sustentabilidade. Adicionalmente, pretende conhecer a relação com os Sustainable Development Goals (SDGs), outros programas de sustentabilidade, bem como com o ecossistema social e político. Neste contexto, o envolvimento e a participação ativa dos atores sociais, e não apenas de stakeholders, assume um papel fundamental, uma vez que, não só, impactam significativamente os processos de tomada de decisão, mas, também, moldam os resultados das iniciativas de sustentabilidade. Nesta nova aproximação ao conceito de responsabilidade e sustentabilidade encontra-se a qualidade da relação entre a rede de atores sociais como ponto-chave. Sublinha-se a importância de estabelecer uma qualidade relacional enriquecida e abrangente para enfrentar de forma mais estruturada os desafios relativos à gestão da água de forma eficiente e promover a adoção de abordagens sustentáveis. Com este trabalho, procura-se aprofundar os domínios inter-relacionados da gestão da água, sustentabilidade e regulamentação. É elaborada uma proposta de simulação, utilizando uma perspetiva sociotécnica com o objetivo de capacitar a co-constituição como sujeitos e a compreensão e utilização de tecnologia responsável e inovação no âmbito da gestão do uso da água utilizando operacionalização da governação antecipatória aberta. O presente estudo materializa seu carácter de inovação ao utilizar um espaço de deliberação digital e ao estabelecer uma abordagem sistemática para a avaliação da tecnologia e sustentabilidade, usando a qualidade relacional da rede de atores como elemento-chave para a coprodução de conhecimento, ciência e tecnologia e co-constituição do próprio sujeito no processo de deliberação

Human Factors in Automated and Robotic Space Systems: Proceedings of a symposium. Part 1

Human factors research likely to produce results applicable to the development of a NASA space station is discussed. The particular sessions covered in Part 1 include: (1) system productivity -- people and machines; (2) expert systems and their use; (3) language and displays for human-computer communication; and (4) computer aided monitoring and decision making. Papers from each subject area are reproduced and the discussions from each area are summarized

Habitat Associations and Reproduction of Fishes on the Northwestern Gulf of Mexico Shelf Edge

Several of the northwestern Gulf of Mexico (GOM) shelf-edge banks provide critical hard bottom habitat for coral and fish communities, supporting a wide diversity of ecologically and economically important species. These sites may be fish aggregation and spawning sites and provide important habitat for fish growth and reproduction. Already designated as habitat areas of particular concern, many of these banks are also under consideration for inclusion in the expansion of the Flower Garden Banks National Marine Sanctuary. This project aimed to gain a more comprehensive understanding of the communities and fish species on shelf-edge banks by way of gonad histology, baited remote underwater video, and hydroacoustics, as well as traditional statistical analyses, Bayesian estimation, and machine learning techniques. The study had several objectives: (1) estimate size at sexual transition for six GOM grouper species, (2) determine the optimal number of cameras on a baited remote underwater video system, (3) create a predictive model to provide presence of fish species based on habitat, and (4) grow a model to predict fish backscatter and density based on habitat parameters. Bayesian estimation allowed for size at sexual transition determinations for the six grouper species, outperforming the tradition frequentist models, especially for situations where tradition models failed to converge. Random forests based on video data had mixed results, but models for several species were able to predict fish presences with class and overall accuracies of greater than 80%. Boosted regression trees based on hydroacoustic data reinforced the importance of depth as a driving factor in fish distributions. The study provided greater understanding and predictive ability regarding fish on the bank habitats

Contracting outsourced services with collaborative key performance indicators

While service outsourcing may benefit from the application of performance‐based contracts (PBCs), the implementation of such contracts is usually challenging. Service performance is often not only dependent on supplier effort but also on the behavior of the buying firm. Existing research on performance‐based contracting provides very limited understanding on how this challenge may be overcome. This article describes a design science research project that develops a novel approach to buyer–supplier contracting, using collaborative key performance indicators (KPIs). Collaborative KPIs evaluate and reward not only the supplier contribution to customer performance but also the customer's behavior to enable this. In this way, performance‐based contracting can also be applied to settings where supplier and customer activities are interdependent, while traditional contracting theories suggest that output controls are not effective under such conditions. In the collaborative KPI contracting process, indicators measure both supplier and customer (buying firm) performance and promote collaboration by being defined through a collaborative process and by focusing on end‐of‐process indicators. The article discusses the original case setting of a telecommunication service provider experiencing critical problems in outsourcing IT services. The initial intervention implementing this contracting approach produced substantial improvements, both in performance and in the relationship between buyer and supplier. Subsequently, the approach was tested and evaluated in two other settings, resulting in a set of actionable propositions on the efficacy of collaborative KPI contracting. Our study demonstrates how defining, monitoring, and incentivizing the performance of specific processes at the buying firm can help alleviate the limitations of traditional performance‐based contracting when the supplier's liability for service performance is difficult to verify