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715 Research products, page 1 of 72

  • Rural Digital Europe
  • Other research products
  • Open Access
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  • Open Access English
    Authors: 
    Hernández de Cos, Pablo;
    Country: Spain

    Eurofi 2022 Financial Forum While advances in financial technology that seek to enhance the efficiency, inclusiveness and quality of services should be welcomed, they will not replace the critical role of human judgment in banking and supervision. And they cannot substitute for the importance of ongoing cooperation among Basel Committee members with a view to safeguarding global financial stability.

  • Open Access English
    Authors: 
    Bergsma, Simon; Euverink, Gerrit Jan Willem; Charalampogiannis, Nikolaos; Poulios, Efthymios; Janssens, Thierry K.S.; Achinas, Spyridon;

    The use of chemical pesticides in agriculture goes hand in hand with some crucial problems. These problems include environmental deterioration and human health complications. To eliminate the problems accompanying chemical pesticides, biological alternatives should be considered. These developments spark interest in many environmental fields, including agriculture. In this review, antifungal compounds produced by lactic acid bacteria (LABs) are considered. It summarizes the worldwide distribution of pesticides and the effect of pesticides on human health and goes into detail about LAB species, their growth, fermentation, and their antifungal compounds. Additionally, interactions between LABs with mycotoxins and plants are discussed.

  • Other research product . Other ORP type . 2022
    Open Access English
    Authors: 
    Buhl, Mie; Larjanko, , Johanni; Tjellum Darraya, Karoline;
    Publisher: Nordisk Ministerråd
    Country: Denmark
  • Other research product . Other ORP type . 2022
    Open Access English
    Authors: 
    Puentes Garzón, David Felipe;
    Publisher: Universidad del Rosario
    Country: Colombia

    La empresa austriaca TECTA, bajo la marca Entrepreno, antes Organisazia, siendo una de las empresas líderes de la región DACH en el sector de la administración de datos, decidió buscar nichos no sólo nuevos sino también más rentables, aprovechando el rebranding que estaba preparando para despegar no sólo como un nombre más simple sino uno más potente para conquistar los mercados de Europa central y occidental poniendo más esfuerzo en el tema de la digitalización y la digitalización de los nuevos clientes potenciales. Gracias a ello, ahora nosotros, estudiantes de la Universidad de Ciencias Aplicadas de Alta Austria, pudimos poner en práctica nuestros aprendizajes dotando a Entrepreno de un nuevo abanico de clientes potenciales. El proyecto se centró en realizar un estudio completo de mercado en los nuevos clientes posibles para la empresa, explorando y encontrando nuevos nichos de mercado. Como cualquier otra empresa, los futuros clientes de Entrepreno se encuentran con ciertas dificultades a la hora de implantar la nueva tecnología CRM. A la hora de desarrollar la estrategia de implantación de cualquier tipo de tecnología para estos clientes, hay que tener en cuenta estas dificultades. La iniciativa consiste en poner de relieve dichas dificultades desde la perspectiva del cliente y de la empresa, encontrando así las mejores empresas que se adaptan para esta solución. Además, en nuestro análisis, hemos prestado atención específicamente a cinco nichos de mercado: Artesanía, Empresas de construcción, Compañías de seguros, Pequeñas agencias de transporte y Asesores tributarios. El proyecto se basó en la ejecución del plan de consecución de empresas interesadas en la organización de datos en los nichos mencionados anteriormente. Debido al progresivo aumento de los datos a lo largo de los últimos años, es necesario encontrar la manera de organizarlos y de esta misma manera utilizarlos con fines empresariales. Para ello, lo que se debe hacer es transformarlos en información. Para ello existen varios programas informáticos en el mercado y Entrepreno es uno de ellos. Se realizaron varias entrevistas a los posibles clientes y se encontró información útil en las cuales se determinó que empresas de los 5 posibles nichos denominados en primer lugar iban a ser realmente las interesados en el producto y por tanto aumentar la probabilidad de su compra. Gracias a la realización de las entrevistas, se pudieron encontrar los contactos necesarios para que la empresa expandiera su producto a estos nuevos nichos. Se entrevistó a varios altos ejecutivos de estas empresas y, por tanto, se llegó a la conclusión de cuál podría ser el mejor encaje para el software. A grandes rasgos, se recomienda tomar como primer sector empresarial a entrar, las agencias de transporte más pequeñas. Se ha llegado a la conclusión de que este nicho es el que mejor encaja con el sistema CRM debido a varios factores, entre ellos la rápida necesidad de más soluciones digitales. El segundo nicho en el que se considera que debe entrar Entrepreno es el de la artesanía, que utiliza demasiado papel y no requiere de programas muy difíciles para manejar su negocio. El sector de la construcción también sería una buena opción, pero no en el momento, debido a que durante los últimos años ya se ha digitalizado bastante, lo que trae como consecuencia la oportunidad de futuros emprendimientos en este sector.

  • Other research product . Other ORP type . 2022
    Open Access English
    Authors: 
    Lazar Luminita; Rodino Steliana; Pop Ruxandra;
    Publisher: Zenodo
    Project: EC | COASTAL (773782), EC | COASTAL (773782)

    In Scenarios For The Danube's Mouth - Black Sea Region section (part of Deliverable 19) future evolutions under each of the four scenarios developed for the Danube Delta region are described qualitatively making use of each of the variables used in the Combined Model (aquaculture, agriculture and tourism). In the excel files all quantitative data corresponding with these qualitative descriptions can be accessed easily. For the first scenario we used data in order facilitate the transition from the current situation to a "greener" vision for all the main economic activities in Danube's Delta: agriculture, tourism and aquaculture. The second scenario is similar with the current situation regarding the Multi Actor Lab 05 study case area. Regarding Scenario 3, the attention of the policymakers regarding the environmental policy is focused on local issues around the middle- and high-income areas and less for Danube's Delta environment. In the Scenario 4, the trend of industrial production of conventional agricultural products is constantly growing and the labour force in the agriculture, aquaculture and tourism sectors are developed. The model offers results for a 30 years period, starting with 2020 and ending with 2050.

  • Other research product . Other ORP type . 2022
    Open Access English
    Authors: 
    Jatavallabhul, Krishna Murthy;
    Publisher: Université de Montréal
    Country: Canada

    L'intelligence artificielle (IA) moderne a ouvert de nouvelles perspectives prometteuses pour la création de robots intelligents. En particulier, les architectures d'apprentissage basées sur le gradient (réseaux neuronaux profonds) ont considérablement amélioré la compréhension des scènes 3D en termes de perception, de raisonnement et d'action. Cependant, ces progrès ont affaibli l'attrait de nombreuses techniques ``classiques'' développées au cours des dernières décennies. Nous postulons qu'un mélange de méthodes ``classiques'' et ``apprises'' est la voie la plus prometteuse pour développer des modèles du monde flexibles, interprétables et exploitables : une nécessité pour les agents intelligents incorporés. La question centrale de cette thèse est : ``Quelle est la manière idéale de combiner les techniques classiques avec des architectures d'apprentissage basées sur le gradient pour une compréhension riche du monde 3D ?''. Cette vision ouvre la voie à une multitude d'applications qui ont un impact fondamental sur la façon dont les agents physiques perçoivent et interagissent avec leur environnement. Cette thèse, appelée ``programmes différentiables pour modèler l'environnement'', unifie les efforts de plusieurs domaines étroitement liés mais actuellement disjoints, notamment la robotique, la vision par ordinateur, l'infographie et l'IA. Ma première contribution---gradSLAM--- est un système de localisation et de cartographie simultanées (SLAM) dense et entièrement différentiable. En permettant le calcul du gradient à travers des composants autrement non différentiables tels que l'optimisation non linéaire par moindres carrés, le raycasting, l'odométrie visuelle et la cartographie dense, gradSLAM ouvre de nouvelles voies pour intégrer la reconstruction 3D classique et l'apprentissage profond. Ma deuxième contribution - taskography - propose une sparsification conditionnée par la tâche de grandes scènes 3D encodées sous forme de graphes de scènes 3D. Cela permet aux planificateurs classiques d'égaler (et de surpasser) les planificateurs de pointe basés sur l'apprentissage en concentrant le calcul sur les attributs de la scène pertinents pour la tâche. Ma troisième et dernière contribution---gradSim--- est un simulateur entièrement différentiable qui combine des moteurs physiques et graphiques différentiables pour permettre l'estimation des paramètres physiques et le contrôle visuomoteur, uniquement à partir de vidéos ou d'une image fixe. Modern artificial intelligence (AI) has created exciting new opportunities for building intelligent robots. In particular, gradient-based learning architectures (deep neural networks) have tremendously improved 3D scene understanding in terms of perception, reasoning, and action. However, these advancements have undermined many ``classical'' techniques developed over the last few decades. We postulate that a blend of ``classical'' and ``learned'' methods is the most promising path to developing flexible, interpretable, and actionable models of the world: a necessity for intelligent embodied agents. ``What is the ideal way to combine classical techniques with gradient-based learning architectures for a rich understanding of the 3D world?'' is the central question in this dissertation. This understanding enables a multitude of applications that fundamentally impact how embodied agents perceive and interact with their environment. This dissertation, dubbed ``differentiable world programs'', unifies efforts from multiple closely-related but currently-disjoint fields including robotics, computer vision, computer graphics, and AI. Our first contribution---gradSLAM---is a fully differentiable dense simultaneous localization and mapping (SLAM) system. By enabling gradient computation through otherwise non-differentiable components such as nonlinear least squares optimization, ray casting, visual odometry, and dense mapping, gradSLAM opens up new avenues for integrating classical 3D reconstruction and deep learning. Our second contribution---taskography---proposes a task-conditioned sparsification of large 3D scenes encoded as 3D scene graphs. This enables classical planners to match (and surpass) state-of-the-art learning-based planners by focusing computation on task-relevant scene attributes. Our third and final contribution---gradSim---is a fully differentiable simulator that composes differentiable physics and graphics engines to enable physical parameter estimation and visuomotor control, solely from videos or a still image.

  • Open Access English
    Authors: 
    Lange, van, Milan; Vercruysse, Sarah Maya; Janz, Nina;
    Publisher: Luxembourg Centre for Contemporary and Digital History (C2DH)
  • Open Access English
    Authors: 
    Andrea Cimmino Arriaga; Raúl García Castro;
    Publisher: Zenodo

    In the last decade the Internet of Things (IoT) has experienced a significant growth and its adoption has become ubiquitous in either business and private life. As a result, several initiatives have emerged for addressing specific challenges and provide a standard or a specification to address them; like CoRE, Web of Things (WoT), oneM2M, or OGC among others. One of these challenges revolves around the discovery procedures to find IoT devices within IoT infrastructures and whether the discovery performed is semantic or syntactic. This article focuses on the WoT initiative and reports the benefits that Semantic Web technologies bring to discovery in WoT. In particular, one of the implementations for the WoT discovery is presented, which is named WoT Hive and provides syntactic and semantic discovery capabilities. WoT Hive is the only candidate implementation that addresses at the same time the syntactic and semantic functionalities specified in the discovery described by WoT. Several experiments have been carried out to test WoT Hive, these advocates that the implementation is technically sound for CRUD operations and that its semantic discovery outperforms the syntactic one implemented. Furthermore, an experiment has been carried out to compare whether syntactic discovery is faster than semantic discovery using the Link Smart implementation for syntactic discovery and WoT Hive for semantic.

  • Open Access English
    Authors: 
    Tzagkarakis, Christos; Charalampidis, Pavlos; Roubakis, Stylianos; Fragkiadakis, Alexandros; Sotiris Ioannidis;
    Publisher: Zenodo
    Project: EC | MARVEL (957337)

    Modern Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices, with the data acquisition and processing infrastructure setting restrictions in terms of computational power and energy re- sources. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies, yielding a reduced set of observations. Nevertheless, this can hamper dramatically subsequent decision-making, such as forecasting. To address this problem, in this work we evaluate short-term forecasting in highly underdetermined cases, i.e., the number of sensor streams is much higher than the number of observations. Several statistical, machine learning and neural network-based models are thoroughly examined with respect to the resulting forecasting accuracy on five different real-world datasets. The focus is given on a unified experimental protocol especially designed for short-term prediction of multiple time series at the IoT edge. The proposed framework can be considered as an important step towards establishing a solid forecasting strategy in resource constrained IoT applications.

  • Open Access English
    Authors: 
    Ghavami, Maryam;
    Country: Canada

    The application of Internet of Things (IoT) has become an important part of our daily lives in diverse areas. IoT provides the ability to integrate and communicate between different objects using smart sensors, cameras, and actuators through an Internet connection. In recent years, a combination of IoT technologies have begun to play an important role in monitoring plant health and growth condition in agricultural systems. Monitoring plant conditions and the effect of abiotic stresses in the early stages is very crucial since it can maximize crop productivity and enable producers to provide products of superior quality. The objective of this research study was to design, develop, and deploy a Raspberry Pi-based smart multi-sensor system for real-time monitoring of plant health conditions at various soil moisture levels. The developed prototype was successfully tested by conducting a series of calibration tests at known soil moisture and temperature conditions. The results obtained from five calibration tests demonstrated that the temperature and soil moisture sensors were accurate and robust over the selected period. The Raspberry Pi-based smart imaging enabled capturing images of plants in real-time for predicting their health and growth condition. To predict the critical time for irrigation, mathematical models were developed that established a relationship between the number of green (i.e., healthy) areas of the plant and soil moisture condition for each soil moisture content (i.e., 0, 20, 40, 60, and 80%). It was observed that the value of the green area of plants decreased with a decrease in soil moisture content. These models could be applied for integrating IoT-based systems in various environmental conditions.