Road Traffic Congestion Analysis Via Connected Vehicles

La congestion routière est un état particulier de mobilité où les temps de déplacement augmentent et de plus en plus de temps est passé dans le véhicule. En plus d’être une expérience très stressante pour les conducteurs, la congestion a également un impact négatif sur l’environnement et l’économie. Dans ce contexte, des pressions sont exercées sur les autorités afin qu’elles prennent des mesures décisives pour améliorer le flot du trafic sur le réseau routier. En améliorant le flot, la congestion est réduite et la durée totale de déplacement des véhicules est réduite. D’une part, la congestion routière peut être récurrente, faisant référence à la congestion qui se produit régulièrement. La congestion non récurrente (NRC), quant à elle, dans un réseau urbain, est principalement causée par des incidents, des zones de construction, des événements spéciaux ou des conditions météorologiques défavorables. Les opérateurs d’infrastructure surveillent le trafic sur le réseau mais sont contraints à utiliser le moins de ressources possibles. Cette contrainte implique que l’état du trafic ne peut pas être mesuré partout car il n’est pas réaliste de déployer des équipements sophistiqués pour assurer la collecte précise des données de trafic et la détection en temps réel des événements partout sur le réseau routier. Alors certains emplacements où le flot de trafic doit être amélioré ne sont pas surveillés car ces emplacements varient beaucoup. D’un autre côté, de nombreuses études sur la congestion routière ont été consacrées aux autoroutes plutôt qu’aux régions urbaines, qui sont pourtant beaucoup plus susceptibles d’être surveillées par les autorités de la circulation. De plus, les systèmes actuels de collecte de données de trafic n’incluent pas la possibilité d’enregistrer des informations détaillées sur les événements qui surviennent sur la route, tels que les collisions, les conditions météorologiques défavorables, etc. Aussi, les études proposées dans la littérature ne font que détecter la congestion ; mais ce n’est pas suffisant, nous devrions être en mesure de mieux caractériser l’événement qui en est la cause. Les agences doivent comprendre quelle est la cause qui affecte la variabilité de flot sur leurs installations et dans quelle mesure elles peuvent prendre les actions appropriées pour atténuer la congestion.----------ABSTRACT: Road traffic congestion is a particular state of mobility where travel times increase and more and more time is spent in vehicles. Apart from being a quite-stressful experience for drivers, congestion also has a negative impact on the environment and the economy. In this context, there is pressure on the authorities to take decisive actions to improve the network traffic flow. By improving network flow, congestion is reduced and the total travel time of vehicles is decreased. In fact, congestion can be classified as recurrent and non-recurrent (NRC). Recurrent congestion refers to congestion that happens on a regular basis. Non-recurrent congestion in an urban network is mainly caused by incidents, workzones, special events and adverse weather. Infrastructure operators monitor traffic on the network while using the least possible resources. Thus, traffic state cannot be directly measured everywhere on the traffic road network. But the location where traffic flow needs to be improved varies highly and certainly, deploying highly sophisticated equipment to ensure the accurate estimation of traffic flows and timely detection of events everywhere on the road network is not feasible. Also, many studies have been devoted to highways rather than highly congested urban regions which are intricate, complex networks and far more likely to be monitored by the traffic authorities. Moreover, current traffic data collection systems do not incorporate the ability of registring detailed information on the altering events happening on the road, such as vehicle crashes, adverse weather, etc. Operators require external data sources to retireve this information in real time. Current methods only detect congestion but it’s not enough, we should be able to better characterize the event causing it. Agencies need to understand what is the cause affecting variability on their facilities and to what degree so that they can take the appropriate action to mitigate congestion

Study of Strategies for Genetic Variant Discrimination and Detection by Optosensing

Tesis por compendio[ES] La medicina actual se dirige hacia un enfoque más personalizado basándose en el diagnóstico molecular del paciente a través del estudio de biomarcadores específicos. Aplicando este principio molecular, el diagnóstico, pronóstico y selección de la terapia se apoyan en la identificación de variaciones específicas del genoma humano, como variaciones de un único nucleótido (SNV). Para detectar estos biomarcadores se dispone de una amplia oferta de tecnologías. Sin embargo, muchos de los métodos en uso presentan limitaciones como un elevado coste, complejidad, tiempos de análisis largos o requieren de personal y equipamiento especializado, lo que imposibilita su incorporación masiva en la mayoría de los sistemas sanitarios. Por tanto, existe la necesidad de investigar y desarrollar soluciones analíticas que aporten información sobre las variantes genéticas y que se puedan implementar en diferentes escenarios del ámbito de la salud con prestaciones competitivas y económicamente viables. El objetivo principal de esta tesis ha sido desarrollar estrategias innovadoras para resolver el reto de la detección múltiple de variantes genéticas que se encuentran en forma minoritaria en muestras biológicas de pacientes, cubriendo las demandas asociadas al entorno clínico. Las tareas de investigación se centraron en la combinación de reacciones de discriminación alélica con amplificación selectiva de DNA y el desarrollo de sistemas ópticos de detección versátiles. Con el fin de atender el amplio abanico de necesidades, en el primer capítulo, se presentan resultados que mejoran las prestaciones analíticas de la reacción en cadena de la polimerasa (PCR) mediante la incorporación de una etapa al termociclado y de un agente bloqueante amplificando selectivamente las variantes minoritarias que fueron monitorizadas mediante fluorescencia a tiempo real. En el segundo capítulo, se logró la discriminación alélica combinando la ligación de oligonucleótidos con la amplificación de la recombinasa polimerasa (RPA), que al operar a temperatura constante permitió una detección tipo point-of-care (POC). La identificación de SNV se llevó a cabo mediante hibridación en formato micromatriz, utilizando la tecnología Blu-Ray como plataforma de ensayo y detección. En el tercer capítulo, se integró la RPA con la reacción de hibridación alelo especifica en cadena (AS-HCR), en formato array para genotipar SNV a partir de DNA genómico en un chip. La lectura de los resultados se realizó mediante un smartphone. En el último capítulo, se presenta la síntesis de un nuevo reactivo bioluminiscente que se aplicó a la monitorización de biomarcadores de DNA a tiempo real y final de la RPA basada en la transferencia de energía de resonancia de bioluminiscencia (BRET), eliminando la necesidad de una fuente de excitación. Todas las estrategias permitieron un reconocimiento especifico de la variante de interés, incluso en muestras que contenían tan solo 20 copias de DNA genómico diana. Se consiguieron resultados sensibles (límite de detección 0.5% variante/total), reproducibles (desviación estándar relativa < 19%), de manera sencilla (3 etapas o menos), rápida (tiempos cortos de 30-200 min) y permitiendo el análisis simultaneo de varios genes. Como prueba de concepto, estas estrategias se aplicaron a la detección e identificación en muestras clínicas de biomarcadores asociados a cáncer colorrectal y enfermedades cardiológicas. Los resultados se validaron por comparación con los métodos de referencia NGS y PCR, comprobándose que se mejoraban los requerimientos técnicos y la relación coste-eficacia. En conclusión, las investigaciones llevadas a cabo posibilitaron desarrollar herramientas de genotipado con propiedades analíticas competitivas y versátiles, aplicables a diferentes escenarios sanitarios, desde hospitales a entornos con pocos recursos. Estos resultados son prometedores al dar respuesta a la demanda de tecnologías alternativas para el diagnóstico molecular personalizado.[CA] La medicina actual es dirigeix cap a un enfocament més personalitzat basant-se en el diagnòstic molecular del pacient a través de l'estudi de biomarcadors específics. Aplicant aquest principi molecular, el diagnòstic, pronòstic i selecció de la teràpia es recolzen en la identificació de variacions específiques del genoma humà, com variacions d'un únic nucleòtid (SNV). Per a detectar aquests biomarcadors, es disposa d'una àmplia oferta de tecnologies. No obstant això, molts dels mètodes en ús presenten limitacions com un elevat cost, complexitat, temps d'anàlisis llargues o requereixen de personal i equipament especialitzat, la qual cosa impossibilita la seua incorporació massiva en la majoria dels sistemes sanitaris. Per tant, existeix la necessitat d'investigar i desenvolupar solucions analítiques que aporten informació sobre les variants genètiques i que es puguen implementar en diferents escenaris de l'àmbit de la salut amb prestacions competitives i econòmicament viables. L'objectiu principal d'aquesta tesi ha sigut desenvolupar estratègies innovadores per a resoldre el repte de la detecció múltiple de variants genètiques que es troben en forma minoritària en mostres biològiques de pacients, cobrint les demandes associades a l'entorn clínic. Les tasques d'investigació es van centrar en la combinació de reaccions de discriminació al·lèlica amb amplificació selectiva de DNA i al desenvolupament de sistemes òptics de detecció versàtils. Amb la finalitat d'atendre l'ampli ventall de necessitats, en el primer capítol, es presenten resultats que milloren les prestacions analítiques de la reacció en cadena de la polimerasa (PCR) mitjançant la incorporació d'una etapa al termociclat i d'un agent bloquejant amplificant selectivament les variants minoritàries que van ser monitoritzades mitjançant fluorescència a temps real. En el segon capítol, es va aconseguir la discriminació al·lèlica combinant el lligament d'oligonucleòtids amb l'amplificació de la recombinasa polimerasa (RPA), que en operar a temperatura constant va permetre una detecció tipus point-of-care (POC). La identificació de SNV es va dur a terme mitjançant hibridació en format micromatriu, utilitzant la tecnologia Blu-Ray com a plataforma d'assaig i detecció. En el tercer capítol, es va integrar la RPA amb la reacció d'hibridació al·lel específica en cadena (AS-HCR), en format matriu per a genotipar SNV a partir de DNA genòmic en un xip. La lectura dels resultats es va realitzar mitjançant un telèfon intel·ligent. En l'últim capítol, es presenta la síntesi d'un nou reactiu bioluminescent que es va aplicar al monitoratge de biomarcadors de DNA a temps real i final de la RPA basada en la transferència d'energia de ressonància de bioluminescència (BRET), eliminant la necessitat d'una font d'excitació. Totes les estratègies van permetre un reconeixement específic de la variant d'interès, fins i tot en mostres que només contenien 20 còpies de DNA genòmic diana. Es van aconseguir resultats sensibles (límit de detecció 0.5% variant/total), reproduïbles (desviació estàndard relativa < 19%), de manera senzilla (3 etapes o menys), ràpida (temps curts de 30-200 min) i permetent l'anàlisi simultània de diversos gens. Com a prova de concepte, aquestes estratègies es van aplicar a la detecció i identificació en mostres clíniques de biomarcadors associats a càncer colorectal i a malalties cardiològiques. Els resultats es van validar per comparació amb els mètodes de referència NGS i PCR, comprovant-se que es milloraven els requeriments tècnics i la relació cost-eficàcia. En conclusió, les investigacions dutes a terme van possibilitar desenvolupar eines de genotipat amb propietats analítiques competitives i versàtils, aplicables a diferents escenaris sanitaris, des d'hospitals a entorns amb pocs recursos. Aquests resultats són prometedors en donar resposta a la demanda de tecnologies alternatives per al diagnòstic molecular personalitzat.[EN] Current medicine is moving towards a more personalized approach based on the patients' molecular diagnosis through the study of specific biomarkers. Diagnosis, prognosis and therapy selection, applying this molecular principle, rely on identifying specific variations in the human genome, such as single nucleotide variations (SNV). A wide range of technologies is available to detect these biomarkers. However, many of the employed methods have limitations such as high cost, complexity, long analysis times, or requiring specialized personnel and equipment, making their massive incorporation in most healthcare systems impossible. Therefore, there is a need to research and develop analytical solutions that provide information on genetic variants that can be implemented in different health scenarios with competitive and economically feasible performances. The main objective of this thesis has been to develop innovative strategies to solve the challenge of multiple detection of genetic variants that are found in a minority amount in patient samples, covering the demands associated with the clinical setting. Research tasks focused on the combination of allelic discrimination reactions with selective DNA amplification and the development of versatile optical detection systems. In order to meet the wide range of needs, in the first chapter, the analytical performances of the polymerase chain reaction (PCR) were improved by incorporating a thermocycling step and a blocking agent to amplify selectively minority variants that were monitored by real-time fluorescence. In the second chapter, allelic discrimination was achieved by combining oligonucleotide ligation with recombinase polymerase amplification (RPA), which operates at a constant temperature, allowing point-of-care (POC) detection. SNV identification was carried out by hybridization in microarray format, using Blu-Ray technology as the assay platform and detector. RPA was integrated with allele-specific hybridization chain reaction (AS-HCR), in an array format to genotype SNV from genomic DNA on a chip in the third chapter. The reading of the results was performed using a smartphone. In the last chapter, a new bioluminescent reagent was synthesized. It was applied to real-time and endpoint DNA biomarker monitoring based on bioluminescence resonance energy transfer (BRET), eliminating the need for an excitation source. All the strategies allowed specific recognition of the target variant, even in samples containing as few as 20 copies of target genomic DNA. Sensitive (limit of detection 0.5% variant/total), reproducible (relative standard deviation < 19%), simple (3 steps or less), fast (short times of 30-200 min) results were achieved, allowing simultaneous analysis of several genes. As proof of concept, these strategies were applied to detect and identify biomarkers associated with colorectal cancer and cardiological diseases in clinical samples. The results were validated by comparison with reference methods such as NGS and PCR, proving that the technical requirements and cost-effectiveness were improved. In conclusion, the developed research made it possible to develop genotyping tools with competitive analytical properties and versatile, applicable to different healthcare scenarios, from hospitals to limited-resource environments. These results are promising since they respond to the demand for alternative technologies for personalized molecular diagnostics.The authors acknowledge the financial support received from the Generalitat Valenciana PROMETEO/2020/094, GRISOLIA/2014/024 PhD Grant and GVA-FPI-2017 PhD grant, the Spanish Ministry of Economy and Competitiveness MINECO projects CTQ2016-75749-R and PID2019-110713RB-I00 and European Regional Development Fund (ERDF).Lázaro Zaragozá, A. (2022). Study of Strategies for Genetic Variant Discrimination and Detection by Optosensing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185216TESISCompendi

Machine learning techniques to estimate the dynamics of a slung load multirotor UAV system

This thesis addresses the question of designing robust and flexible controllers to enable autonomous operation of a multirotor UAV with an attached slung load for general cargo transport. This is achieved by following an experimental approach; real flight data from a slung load multirotor coupled system is used as experience, allowing for a computer software to estimate the pose of the slung in order to propose a swing-free controller that will dampen the oscillations of the slung load when the multirotor is following a desired flight trajectory. The thesis presents the reader with a methodology describing the development path from vehicle design and modelling over slung load state estimators to controller synthesis. Attaching a load via a cable to the underside of the aircraft alters the mass distribution of the combined "airborne entity" in a highly dynamic fashion. The load will be subject to inertial, gravitational and unsteady aerodynamic forces which are transmitted to the aircraft via the cable, providing another source of external force to the multirotor platform and thus altering the flight dynamic response characteristics of the vehicle. Similarly the load relies on the forces transmitted by the multirotor to alter its state, which is much more difficult to control. The principle research hypothesis of this thesis is that the dynamics of the coupled system can be identified by applying Machine Learning techniques. One of the major contributions of this thesis is the estimator that uses real flight data to train an unstructured black-box algorithm that can output the position vector of the load using the vehicle pose and pilot pseudo-controls as input. Experimental results show very accurate position estimation of the load using the machine learning estimator when comparing it with a motion tracking system (~2% offset). Another contribution lies in the avionics solution created for data collection, algorithm execution and control of multirotor UAVs, experimental results show successful autonomous flight with a range of algorithms and applications. Finally, to enable flight capabilities of a multirotor with slung load, a control system is developed that dampens the oscillations of the load; the controller uses a feedback approach to simultaneously prevent exciting swing and to actively dampen swing in the slung load. The methods and algorithms developed in this thesis are validated by flight testing

11th International Coral Reef Symposium Proceedings

A defining theme of the 11th International Coral Reef Symposium was that the news for coral reef ecosystems are far from encouraging. Climate change happens now much faster than in an ice-age transition, and coral reefs continue to suffer fever-high temperatures as well as sour ocean conditions. Corals may be falling behind, and there appears to be no special silver bullet remedy. Nevertheless, there are hopeful signs that we should not despair. Reef ecosystems respond vigorously to protective measures and alleviation of stress. For concerned scientists, managers, conservationists, stakeholders, students, and citizens, there is a great role to play in continuing to report on the extreme threat that climate change represents to earth’s natural systems. Urgent action is needed to reduce CO2 emissions. In the interim, we can and must buy time for coral reefs through increased protection from sewage, sediment, pollutants, overfishing, development, and other stressors, all of which we know can damage coral health. The time to act is now. The canary in the coral-coal mine is dead, but we still have time to save the miners. We need effective management rooted in solid interdisciplinary science and coupled with stakeholder buy in, working at local, regional, and international scales alongside global efforts to give reefs a chance.https://nsuworks.nova.edu/occ_icrs/1000/thumbnail.jp

Promoting Andean children's learning of science through cultural and digital tools

Conference Theme: To see the world and a grain of sand: Learning across levels of space, time, and scaleIn Peru, there is a large achievement gap in rural schools. In order to overcome this problem, the study aims to design environments that enhance science learning through the integration of ICT with cultural artifacts, respecting the Andean culture and empower rural children to pursue lifelong learning. This investigation employs the Cultural-Historical Activity Theory (CHAT) framework, and the Design-Based Research (DBR) methodology using an iterative process of design, implementation and evaluation of the innovative practice.published_or_final_versio

Cancer Biomarker Research and Personalized Medicine

Biomarkers are measures of a biological state. The treatment of individual patients based on particular factors, such as biomarkers, distinguishes standard, generalized treatment plans from personalized medicine. Even though personalized medicine is applicable to most branches of medicine, the field of oncology is perhaps where it is most easily employed. Cancer is a heterogeneous disease; although patients may be diagnosed histologically with the same cancer type, their tumors can comprise varying tumor microenvironments and molecular characteristics that can impact treatment response and prognosis. There has been a major drive over the past decade to try and realize personalized cancer medicine through the discovery and use of disease-specific biomarkers. This book, entitled “Cancer Biomarker Research and Personalized Medicine”, encompasses 22 publications from colleagues working on a diverse range of cancers, including prostate, breast, ovarian, head and neck, liver, gastric, bladder, colorectal, and kidney. The biomarkers assessed in these studies include genes, intracellular or secreted proteins, exosomes, DNA, RNA, miRNA, circulating tumor cells, circulating immune cells, in addition to radiomic features