Database Preservation Toolkit: a flexible tool to normalize and give access to databases

Digital preservation is emerging as an area of work and research that tries to provide answers that will ensure a continued and long-term access to information stored digitally. IT Platforms are constantly changing and evolving and nothing can guarantee the continuity of access to digital artifacts in their absence. This paper focuses on a specic family of digital objects: Relational Databases; they are the most frequent type of databases used by organizations worldwide. Database Preservation Toolkit enables the preservation of relational databases holding the structure and content of the the database in a preservation format in order to provide access to the database information in a long term period. If in one hand there is a need to migrate databases to newer ones that appear with technological evolution, on the other hand there is also the need to preserve the information they hold for a long time period, due to legal duties but also due to archival issues. That being said, that information must be available no matter the database management system where the information came from. In this area, solutions are still scarce. Main products for relational database preservation include CHRONOS and SIARD. The rst one is, in most of the cases, unreachable due to the associated costs. The second one is not really a product but a preservation format. The main idea behind this work was to explore the main features and limitations of the existing products in order to improve 'db-preservation-toolkit' (http://keeps.github.io/ db-preservation-toolkit/), an extracted component from the RODA project (http://www.roda-community.org). Therefore, 'db-preservation-toolkit' was improved with respect to performance and also with new features addiction in order to support more database management systems, address some missing features of the other products, support of a new preservation format (SIARD) and provide an interface where it is possible to access and search the information of the archived databases.This work is supported by the European Commission under FP7 CIP PSP grant agreement number 620998 - E-ARK

Paul Churchland e a Problemática da Semântica dos Estados Mentais

Dissertação para obtenção do Grau de Mestre em Filosofia ContemporâneaOs objectivos a cumprir nesta primeira secção serão três: i) expor em sinopse o percurso académico de Paul Churchland, evidenciando as influências de que o seu pensamento foi alvo ao longo da sua maturação; ii) introduzir os conceitos e noções necessários à subsequente exploração da sua teoria semântica; iii) apresentar genericamente o seu programa filosófico, situando-o no contexto global da história da Filosofia e explicitando a especial relevância de que a problemática da semântica dos estados mentais se reveste no mesmo (não obstante ser apenas uma parte num todo bastante mais abrangente). Naturalmente, a riqueza e extensão da obra de Churchland obrigam a que toda a tentativa de sumarizá-la não deixe de ser superficial. A exposição que se segue não será excepção, conquanto se ressalve que todas as noções centrais para a presente dissertação cujo desenvolvimento resulte insuficiente no que se segue serão devidamente aprofundadas nas secções subsequentes

Characterising the agriculture 4.0 landscape - Emerging trends, challenges and opportunities

ReviewInvestment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sectorinfo:eu-repo/semantics/publishedVersio

Current Trends, Challenges, and Future Perspectives

This work was supported in part by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through the research units UNINOVA-CTS (UIDB/00066/2020), GeoBioTec (UIDP/04035/2020), CEF (UIDB/00239/2020), and the Associate Laboratory TERRA (LA/P/0092/2020). Publisher Copyright: © 2023 by the authors.Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.publishersversionpublishe

PT-CRIS: Um miradouro sobre o universo científico nacional

Reconhecida a importância da ciência, tecnologia, inovação e do conhecimento gerado pela investigação científica, são inúmeros os sistemas de informação criados para dar resposta a necessidades de gestão e disseminação de informação em diferentes domínios. Contudo, a dispersão de informação em vários sistemas, a não adoção de normas/práticas de referência e consequentemente a replicação de informação criam dificuldades às várias entidades que gerem ou consultam informação sobre ciência e respetivos indicadores na capacidade de gestão, execução, avaliação e tomada de decisão relativa a processos de investigação. Surge assim a necessidade de criar um sistema que ofereça uma visão global do universo de ciência e tecnologia, agregando e relacionando informação de suporte à atividade científica desenvolvida em Portugal, i.e., informação sobre investigadores, organizações, programas de financiamento, projetos, resultados de investigação, instalações, equipamentos e serviços. O sistema, ao relacionar e contextualizar a informação científica atualmente dispersa em vários sistemas, permitirá transformar informação em conhecimento, aumentar a visibilidade e difusão da ciência e simplificar processos na gestão da produção científica nacional.Comunicação patrocinada pela KEEP SOLUTION

Intelligent Data-Driven Decision Support for Agricultural Systems-ID3SAS

Publisher Copyright: This work was partially supported by the SIMShore: SIMOcean Nearshore Bathymetry Based on Low Cost Approaches. This project received funding from the EEA Grants Portugal research and innovation program under Grant agreement No PT-INNOVATION-0027. © 2023 IEEE.The agricultural sector worldwide faces serious problems regarding water scarcity, which demands innovative management methods to optimise water use. In response, we propose the Intelligent Data-Driven Decision Support for Agricultural Systems (ID3SAS) methodology, which offers a scalable, flexible, and cloud-based decision support system for real-time supervision and control in agricultural environments. Aligned with the prevailing trends of Agriculture 4.0, ID3SAS integrates data acquisition, cloud-based storage, machine learning, predictive analysis, and run-time reasoning to facilitate decision-making processes, thereby assisting users in making more informed and sustainable decisions. In a case study with tomato plants, ID3SAS-irrigated plants showed 20.9% reduction in water consumption and 26.4% increase in crop production compared to traditional methods, which despite the controlled laboratory environment setting, highlights the methodology's promising potential in addressing water scarcity and enhancing agricultural productivity.publishersversionpublishe

Experiences on teaching alloy with an automated assessment platform

This paper presents Alloy4Fun, a web application that enables online editing and sharing of Alloy models and instances (including dynamic ones developed with the Electrum extension), to be used mainly in an educational context. By introducing secret paragraphs and commands in the models, Alloy4Fun allows the distribution and automated assessment of simple specification challenges, a mechanism that enables students to learn the language at their own pace. Alloy4Fun stores all versions of shared and analyzed models, as well as derivation trees that depict how they evolved over time: this wealth of information can be mined by researchers or tutors to identify, for example, learning breakdowns in the class or typical mistakes made by Alloy users. Alloy4Fun has been used in formal methods graduate courses for two years and for the latest edition we present results regarding its adoption by the students, as well as preliminary insights regarding the most common bottlenecks when learning Alloy (and Electrum).We would like to thank Daniel Jackson for the helpful comments and suggestions about the design of Alloy4Fun. This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. The third and forth authors were financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project POCI-01-0145-FEDER-016826. The second author was also supported by the FCT sabbatical grant with reference SFRH/BSAB/143106/2018

Variability of Root System Size and Distribution among Coffea canephora Genotypes

This work aimed to evaluate the variability in the distribution of the root system among genotypes of C. canephora cv. Conilon and indicate management strategies for a more efficient mineral fertilization. Root distribution was evaluated in six genotypes. The experimental design was in randomized blocks with three replications. Soil monoliths measuring about 27 cm3 were collected at six different soil depths, at three row distances and nine distances of inter-row planting. The collections were carried out in one plant of each repetition. In total, 1296 samples were evaluated. The roots were washed, digitized and processed to quantify length density, volume, surface area and diameter. The distribution of the root system was characterized using semivariograms. It was observed that the highest concentration of roots occurred in the distances close to the irrigation drippers. There was variation in the distribution of the root system among the genotypes. However, in general, the root system is concentrated at a depth of 0 to 20 cm in the soil, at distances up to 50 cm in the planting row and up to 60 cm in inter-rows. Therefore, the greatest efficiency in nutritional management can be achieved by applying fertilizers within a radius of 50 cm around the plantinfo:eu-repo/semantics/publishedVersio