2,865 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Silicon-Based Optical Sensors for Fungal Pathogen Diagnostics

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    The last years have witnessed a link between the COVID-19 pandemic with increasing numbers of vulnerable patients and globally emerging incidences of severe drug-resistant fungal infections, thus, calling for rapid, reliable, and sensitive diagnostic tools for fungal infections. However, despite strong warnings from health authorities, such as the World Health Organization, concerning the fatal consequences of the global spread of drug-resistant pathogenic fungi, progress in fungal infection diagnosis and therapy is still limited. Today, gold standard methods for revealing resistance and susceptibility in pathogenic fungi, namely antifungal susceptibility testing (AFST), require several days for completion, and thus this lengthy process can adversely affect antifungal therapy and further promote the spread of resistance. In this work, the use of photonic silicon chips consisting of micropatterned diffraction gratings as sensitive sensors for rapid AFST of clinically relevant fungal pathogens is investigated. These photonic chips provide a surface for the colonization of microbial pathogens at a liquid-solid interface and serve as the optical transducer element for label-free monitoring of fungal growth by detecting real-time changes in the white light reflectance. These sensor elements are used to track morphological changes of fungi in the presence of clinically relevant antifungals at varying concentrations to rapidly determine the minimum inhibitory concentration (MIC) values that help to classify pathogens as resistant or susceptible. We show that by careful design of the chip dimensions, this optical method can extend from bacteria, through yeasts, to filamentous fungi for accelerated AFST, which is at least three times faster than current gold standard methods and can provide same-day results. Moreover, a 3D-printed microfluidic gradient generator was designed to complement the assay and provide an integrated system, which can potentially be employed in point-of-care settings. This gradient generator produces the two-fold dilution series of clinically relevant antimicrobials in an automated manner and is interfaced with the photonic silicon chips to include a complete, on-chip, label-free, and phenotypic assay. Using the bacterial species Escherichia coli and ciprofloxacin as a model pathogen-drug combination, MIC values can be expeditiously determined within 90 minutes compared to current clinical practices, which typically require up to 24 h for bacterial species

    Differences in well-being:the biological and environmental causes, related phenotypes, and real-time assessment

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    Well-being is a complex, and multifaceted construct that includes feeling good and functioning well. There is a growing global recognition of well-being as an important research topic and public policy goal. Well-being is related to less behavioral and emotional problems, and is associated with many positive aspects of daily life, including longevity, higher educational achievement, happier marriage, and more productivity at work. People differ in their levels of well-being, i.e., some people are in general happier or more satisfied with their lives than others. These individual differences in well-being can arise from many different factors, including biological (genetic) influences and environmental influences. To enhance the development of future mental health prevention and intervention strategies to increase well-being, more knowledge about these determinants and factors underlying well-being is needed. In this dissertation, I aimed to increase the understanding of the etiology in a series of studies using different methods, including systematic reviews, meta-analyses, twin designs, and molecular genetic designs. In part I, we brought together all published studies on the neural and physiological factors underlying well-being. This overview allowed us to critically investigate the claims made about the biology involved in well-being. The number of studies on the neural and physiological factors underlying well-being is increasing and the results point towards potential correlates of well-being. However, samples are often still small, and studies focus mostly on a single biomarker. Therefore, more well-powered, data-driven, and integrative studies across biological categories are needed to better understand the neural and physiological pathways that play a role in well-being. In part II, we investigated the overlap between well-being and a range of other phenotypes to learn more about the etiology of well-being. We report a large overlap with phenotypes including optimism, resilience, and depressive symptoms. Furthermore, when removing the genetic overlap between well-being and depressive symptoms, we showed that well-being has unique genetic associations with a range of phenotypes, independently from depressive symptoms. These results can be helpful in designing more effective interventions to increase well-being, taking into account the overlap and possible causality with other phenotypes. In part III, we used the extreme environmental change during the COVID-19 pandemic to investigate individual differences in the effects of such environmental changes on well-being. On average, we found a negative effect of the pandemic on different aspects of well-being, especially further into the pandemic. Whereas most previous studies only looked at this average negative effect of the pandemic on well-being, we focused on the individual differences as well. We reported large individual differences in the effects of the pandemic on well-being in both chapters. This indicates that one-size-fits-all preventions or interventions to maintain or increase well-being during the pandemic or lockdowns will not be successful for the whole population. Further research is needed for the identification of protective factors and resilience mechanisms to prevent further inequality during extreme environmental situations. In part IV, we looked at the real-time assessment of well-being, investigating the feasibility and results of previous studies. The real-time assessment of well-being, related variables, and the environment can lead to new insights about well-being, i.e., results that we cannot capture with traditional survey research. The real-time assessment of well-being is therefore a promising area for future research to unravel the dynamic nature of well-being fluctuations and the interaction with the environment in daily life. Integrating all results in this dissertation confirmed that well-being is a complex human trait that is influenced by many interrelated and interacting factors. Future directions to understand individual differences in well-being will be a data-driven approach to investigate the complex interplay of neural, physiological, genetic, and environmental factors in well-being

    Undergraduate Catalog of Studies, 2022-2023

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    Redes de sensores para la predicción solar a corto plazo en el marco de las microgrids y smartcities

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    En los últimos años, la potencia fotovoltaica instalada global ha crecido notablemente, llegando a superar el 20\% de la demanda energética en varios países. Esto se debe en parte a la reducción de costes de esta tecnología y la política de promover el uso de energías renovables. La producción de la energía fotovoltaica depende directamente de los niveles de radiación solar incidente sobre los paneles, que se trata de un recurso externo y variable. La irradiancia solar fluctúa principalmente por dos factores, pero la mayor variabilidad está asociada a la presencia de nubes, y estas variaciones tienen una duración que va desde unos pocos segundos hasta varios minutos. Debido al funcionamiento del mercado eléctrico y a la nula inercia en la producción energética de estos sistemas, los productores fotovoltaicos necesitan de predicciones precisas en diferentes horizontes temporales con el fin de maximizar la energía ofertada en el mercado, incrementando de este modo la integración de la misma. Por otra parte, también necesitan datos en tiempo real para una gestión más óptima del sistema fotovoltaico. Las predicciones a corto plazo se emplean para el sistema de control y balance de la producción energética, y a medio plazo para la programación y venta de energía en el mercado eléctrico, sin embargo, los sistemas actuales de predicción son escasos y caros para ser contemplados en sistemas de media y pequeña escala. Numerosos estudios han intentado cubrir la necesidad de predicción a corto plazo estimando espacio-temporalmente el campo de irradiancia con cámaras de cielo completo e imágenes de satélite, sin embargo, estos métodos están limitados por la problemática de la conversión de imagen a irradiancia. Investigadores influyentes en este área creen que las redes de sensores de irradiancia pueden jugar un papel fundamental en este contexto, ofreciendo en tiempo real varias medidas espaciales y con la alta resolución temporal necesaria. La información espacio-temporal capturada por la red permitiría estimar el campo de irradiancia y analizar su evolución, capturando incluso los eventos más rápidos. Las tecnologías inalámbricas han evolucionado en el marco de las ciudades inteligentes y el internet de las cosas, apareciendo tecnologías que se adecuan a diferentes escenarios. El interés mostrado en estos sistemas ha producido un abaratamiento de los módulos de comunicaciones inalámbricas, gracias a la economía de escala. Las redes de sensores podrían beneficiarse de estas tecnologías inalámbricas, ofreciendo a su vez un ahorro en costes del despliegue respecto a su equivalente cableado y una mayor flexibilidad para integrar nuevos nodos en la red. Por ello, esta tesis se pretende estudiar el potencial de estas redes inalámbricas como fuente de información crítica para la gestión a corto plazo de sistemas fotovoltaicos, y la explotación de los datos de la misma, implementando y desarrollando algoritmos con estos datos con fines de predicción de la producción y para la operación óptima de estos sistemas.In recent years, global installed photovoltaic power has grown significantly, exceeding 20% of energy demand in several countries. This is partly due to the cost reduction of this technology and the policy of promoting the use of renewable energies. Photovoltaic energy production depends directly on the levels of solar radiation incident on the panels, which is an external and variable resource. Solar irradiance fluctuates mainly due to two factors, but the greatest variability is associated with the presence of clouds, and these variations range in duration from a few seconds to several minutes. Due to the functioning of the electricity market and the lack of inertia in the energy production of these systems, PV producers need accurate forecasts at different time horizons in order to maximize the energy offered in the market, thus increasing the integration of the same. On the other hand, they also need real-time data for more optimal PV system management. Short-term forecasts are used for the energy production control and balancing system, and medium-term forecasts are used for scheduling and selling energy in the electricity market, however, current forecasting systems are scarce and expensive to be contemplated in medium and small-scale systems. Numerous studies have attempted to address the need for short-term forecasting by estimating the spatio-temporal irradiance field with full-sky cameras and satellite imagery, however, these methods are limited by the problems of image-to-irradiance conversion. Influential researchers in this area believe that irradiance sensor networks can play a key role in this context, providing various spatial measurements in real time and with the necessary high temporal resolution. The spatio-temporal information captured by the network would allow estimating the irradiance field and analyzing its evolution, capturing even the fastest events. Wireless technologies have evolved within the framework of smart cities and the internet of things, with the emergence of technologies that are suitable for different scenarios. The interest shown in these systems has led to a reduction in the cost of wireless communications modules, thanks to economies of scale. Sensor networks could benefit from these wireless technologies, offering savings in deployment costs compared to their wired equivalent and greater flexibility to integrate new nodes in the network. Thus, this thesis aims to study the potential of these wireless networks as a source of critical information for the short-term management of photovoltaic systems, and the exploitation of the data from it, implementing and developing algorithms with this data for production prediction purposes and for the optimal operation of these systems

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Technology for Low Resolution Space Based RSO Detection and Characterisation

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    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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