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  • Rural Digital Europe
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Miguel Gutiérrez Gaitán; Luís Almeida; Pedro M. D’orey; Pedro M. Santos; +1 Authors

    This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design. To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering. Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector, closeness, betweenness, and degree. Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods. This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/04234/2020); by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Agreement, through the European Regional Development Fund (ERDF); also by FCT and the ESF (European Social Fund) through the Regional Operational Programme (ROP) Norte 2020, under PhD grant 2020.06685.BD.

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    ACM Transactions on Embedded Computing Systems
    Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hirt, Heribert; Al-Babili, Salim; Almeida-Trapp, Marilia; Antoine, Martin; +61 Authors

    Greenhouse gas (GHG) emissions have created a global climate crisis which requires immediate interventions to mitigate the negative effects on all aspects of life on this planet. As current agriculture and land use contributes up to 25% of total GHG emissions, plant scientists take center stage in finding possible solutions for a transition to sustainable agriculture and land use. In this article, the PlantACT! (Plants for climate ACTion!) initiative of plant scientists lays out a road map of how and in which areas plant scientists can contribute to finding immediate, mid-term, and long-term solutions, and what changes are necessary to implement these solutions at the personal, institutional, and funding levels. The work of H.H. was supported by baseline grant BAS/1/1062-01-01 from King Abdullah University of Science and Technology, Thuwal, KSA. A.P.M.W. acknowledges funding under Germany’s Excellence Strategy EXC-2048/1, Project ID 390686111 and the European Union H2020 project 862087-GAIN4CROPS Peer reviewed 7 Pág.

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    Research@WUR; Trends in Plant Science
    Article . 2023 . Peer-reviewed
    License: Elsevier TDM
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Research@WUR
    Article . 2023
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    HAL-CEA
    Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Catarina Jorge; Margarida Tomé; Ricardo Ruiz-Peinado; Lobna Zribi; +1 Authors

    The necessity for accurate biomass estimates is greater than ever for the sustainable management of forest resources, which is an increasingly pressing matter due to climate change. The most used method to estimate biomass for operational purposes is through allometric equations. Typically, each country develops their own models to be applied at the local scale because it is more convenient. But, for Quercus suber, a joint regional model can be more beneficial, since the species is distributed across the Mediterranean and is challenging to account for due to felling limitations and the nature of mature cork biomass itself. We found that these characteristics are reflected in the biomass datasets and compatibility was, perhaps, the largest impediment to such a model. The use of dummy variables to differentiate between countries, as well as compromises in the limits of biomass compartments, allowed us to develop two joint models to estimate aboveground biomass in Portugal, Spain and Tunisia. One model as a function of diameter and another as a function of diameter and total tree height. In addition, we developed a separate model for roots (modelling efficiency of fitting = 0.89), since it was not possible to assure additivity of the whole tree. All coefficients were estimated using Seemingly Unrelated Regressions (SUR) and model fitting assured additivity in the aboveground compartments—leaves and woody biomass (modelling efficiency of fitting = 0.89 and 0.93, respectively). This work proves that it is possible to have a biologically sound and efficient model for the three countries, despite differences in the observed allometric patterns. This research was funded by the Forest Research Centre, a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal (UIDB/00239/2020). Catarina Jorge was funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), under scholarship UI/BD/150757/2020. Peer reviewed 11Pág.

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    Forests
    Other literature type . Article . 2023 . Peer-reviewed
    License: CC BY
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    Forests
    Article . 2023
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Forestsarrow_drop_down
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      Other literature type . Article . 2023 . Peer-reviewed
      License: CC BY
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Felipe Viel; Renato Cotrim Maciel; Laio Oriel Seman; Cesar Albenes Zeferino; +2 Authors

    Hyperspectral images contain tens to hundreds of bands, implying a high spectral resolution. This high spectral resolution allows for obtaining a precise signature of structures and compounds that make up the captured scene. Among the types of processing that may be applied to Hyperspectral Images, classification using machine learning models stands out. The classification process is one of the most relevant steps for this type of image. It can extract information using spatial and spectral information and spatial-spectral fusion. Artificial Neural Network models have been gaining prominence among existing classification techniques. They can be applied to data with one, two, or three dimensions. Given the above, this work evaluates Convolutional Neural Network models with one, two, and three dimensions to identify the impact of classifying Hyperspectral Images with different types of convolution. We also expand the comparison to Recurrent Neural Network models, Attention Mechanism, and the Transformer architecture.. Furthermore, a novelty pre-processing method is proposed for the classification process to avoid generating data leaks between training, validation, and testing data. The results demonstrated that using 1 Dimension Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and Transformer architectures reduces memory consumption and sample processing time and maintain a satisfactory classification performance up to 99% accuracy on larger datasets. In addition, the Transfomer architecture can approach the 2D-CNN and 3D-CNN architectures in accuracy using only spectral information. The results also show that using two or three dimensions convolution layers improves accuracy at the cost of greater memory consumption and processing time per sample. Furthermore, the pre-processing methodology guarantees the disassociation of training and testing data.

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    Repositório Comum
    Article . 2016
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    IEEE Access
    Article . 2023
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    IEEE Access
    Article . 2023 . Peer-reviewed
    License: CC BY NC ND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Repositório Comumarrow_drop_down
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      Article . 2023
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      IEEE Access
      Article . 2023 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hasselwander, Marc; Kiko, Mwendwa; Johnson, Ted;

    Digitalization and advances in information and communications technologies (ICT) are having a major impact on urban transport and the way people move around cities. However, this hardly relates to informal transport in the global South. Despite its importance and the fact that billions of people depend on it, insufficient efforts have been made by policymakers to improve informal transport services. In this think piece, we propose that this circumstance can be addressed on two counts through digital civic engagement and open data. In the digital age, civic engagement is no longer only associated with voluntary work at community level, but it is also very effective remotely. Digital civic engagement leverages social media to create awareness for in-situ concerns and provides a platform for local volunteers to connect with international experts to create people-centered solutions (e.g., mobile apps). Open data stimulates and facilitates digital civic engagement, which, in turn, can lead to the creation of more open data. Cities and local authorities should encourage and foster this virtuous cycle, and make transport data sets openly available. This can spur innovation and promote sustainable mobility behavior – with volunteers being powerful agents driving these efforts. Indeed, the case of the Trufi Association demonstrates that open data (e.g., in the form of volunteered geographic information) and the development of mobile apps in several developing cities have helped make informal transport more visible and transparent. The insights from the informal transport field can serve as an impetus for other sectors of the informal economy – such as retail, commerce, agriculture, manufacturing, and services – to promote digital civic engagement and open data to bring about positive change.

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    Authors: Rasti, B.; Chang, Y.; Dalsasso, E.; Denis, L.; +1 Authors

    Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox. Comment: Published in IEEE Geoscience and Remote Sensing Magazine

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    ROBIS
    Article . 2022
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2021
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    IEEE Geoscience and Remote Sensing Magazine
    Article . 2022 . Peer-reviewed
    License: IEEE Copyright
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    https://doi.org/10.48550/arxiv...
    Article . 2021
    License: CC BY NC ND
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2021
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      IEEE Geoscience and Remote Sensing Magazine
      Article . 2022 . Peer-reviewed
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      https://doi.org/10.48550/arxiv...
      Article . 2021
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    Authors: Mark A. K. Gillespie; Mathilde Baude; Jacobus Biesmeijer; Nigel Boatman; +16 Authors

    Research into pollinators in managed landscapes has recently combined approaches of pollination ecology and landscape ecology, because key stressors are likely to interact across wide areas. While laboratory and field experiments are valuable for furthering understanding, studies are required to investigate the interacting drivers of pollinator health and diversity across a broader range of landscapes and a wider array of taxa. Here, we use a network of 96 study landscapes in six topographically diverse regions of Britain, to test the combined importance of honeybee density, insecticide loadings, floral resource availability and habitat diversity to pollinator communities. We also explore the interactions between these drivers and the cover and proximity of semi-natural habitat. We found that among our four drivers, only honeybee density was positively related to wild pollinator abundance and diversity, and the positive association between abundance and floral resources depended on insecticide loadings and habitat diversity. By contrast, our exploratory models including habitat composition metrics revealed a complex suite of interactive effects. These results demonstrate that improving pollinator community composition and health is unlikely to be achieved with general resource enhancements only. Rather, local land-use context should be considered in fine-tuning pollinator management and conservation. This article is part of the theme issue ‘Natural processes influencing pollinator health: from chemistry to landscapes’.

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    Europe PubMed Central
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    Authors: S. Kotthaus; J. A. Bravo-Aranda; J. A. Bravo-Aranda; J. A. Bravo-Aranda; +17 Authors

    The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth’s surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sublayer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe. Marie Sklodowska-Curie Action Cofund 2016 EU project - Athenea3i national Portuguese funds through FCT - Fundacao para a Ciencia e Tecnologia, I.P. CA18235 871115 101036245 ANR-20-CE22-0013 PID2020-120015RB-I00 PID2020.117825GB.C21 P18-RT-3820 P20-00136 A-RNM-430-UGR20 754446 UIDB/04683/2020 European Cooperation in Science and Technology (COST) PTDC/CTAMET/29678/2017 European Commission University of Granada, Programa Operativo FEDER Andalucia 2014-2020 French National Research Agency (ANR) Region Ile-de-France Earth System Excellence Units Program Spanish Government Junta de Andalucia

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    https://doi.org/10.5194/amt-20...
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      https://doi.org/10.5194/amt-20...
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    Authors: Isabel Marques; Duarte Gouveia; Jean-Charles Gaillard; Sónia Martins; +6 Authors

    Drought is a major threat to coffee, compromising the quality and quantity of its production. We have analyzed the core proteome of 18 Coffea canephora cv. Conilon Clone 153 and C. arabica cv. Icatu plants and assessed their responses to moderate (MWD) and severe (SWD) water deficits. Label-free quantitative shotgun proteomics identified 3000 proteins in both genotypes, but less than 0.8% contributed to ca. 20% of proteome biomass. Proteomic changes were dependent on the severity of drought, being stronger under SWD and with an enrolment of different proteins, functions, and pathways than under MWD. The two genotypes displayed stress-responsive proteins under SWD, but only C. arabica showed a higher abundance of proteins involved in antioxidant detoxification activities. Overall, the impact of MWD was minor in the two genotypes, contrary to previous studies. In contrast, an extensive proteomic response was found under SWD, with C. arabica having a greater potential for acclimation/resilience than C. canephora. This is likely supported by a wider antioxidative response and an ability to repair photosynthetic structures, being crucial to develop new elite genotypes that assure coffee supply under water scarcity levels. International audience

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    Authors: Hind Bril El-Haouzi; Etienne Valette; Bettina-Johanna Krings; António Moniz;

    UIDB/04647/2020 UIDP/04647/2020 Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap between humans and systems in which human was seen as an important source of errors and disruptions. Today, the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised awareness about the central role humans have to play in manufacturing systems, the design of which must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of Things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different human social dimensions associated with CPS and IoT and focuses on their conceptual evolution regarding automated production systems’ sociability, notably by bringing humans back in the loop. Hereby, this paper aims to take stock of current research trends to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. Consequently, different models of sociability as a way to integrate humans in the broad sense and/or the develop future automated production systems have been identified from the literature and analysed. publishersversion published

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    DOAJ
    Article . 2021
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    https://doi.org/10.5445/ir/100...
    Article . 2021
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    KITopen
    Article . 2021
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    https://doi.org/10.5445/ir/100...
    Article . 2021
    License: CC BY
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    Societies
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    Societies
    Article . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Miguel Gutiérrez Gaitán; Luís Almeida; Pedro M. D’orey; Pedro M. Santos; +1 Authors

    This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design. To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering. Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector, closeness, betweenness, and degree. Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods. This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/04234/2020); by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Agreement, through the European Regional Development Fund (ERDF); also by FCT and the ESF (European Social Fund) through the Regional Operational Programme (ROP) Norte 2020, under PhD grant 2020.06685.BD.

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    ACM Transactions on Embedded Computing Systems
    Article . 2024 . Peer-reviewed
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    Authors: Hirt, Heribert; Al-Babili, Salim; Almeida-Trapp, Marilia; Antoine, Martin; +61 Authors

    Greenhouse gas (GHG) emissions have created a global climate crisis which requires immediate interventions to mitigate the negative effects on all aspects of life on this planet. As current agriculture and land use contributes up to 25% of total GHG emissions, plant scientists take center stage in finding possible solutions for a transition to sustainable agriculture and land use. In this article, the PlantACT! (Plants for climate ACTion!) initiative of plant scientists lays out a road map of how and in which areas plant scientists can contribute to finding immediate, mid-term, and long-term solutions, and what changes are necessary to implement these solutions at the personal, institutional, and funding levels. The work of H.H. was supported by baseline grant BAS/1/1062-01-01 from King Abdullah University of Science and Technology, Thuwal, KSA. A.P.M.W. acknowledges funding under Germany’s Excellence Strategy EXC-2048/1, Project ID 390686111 and the European Union H2020 project 862087-GAIN4CROPS Peer reviewed 7 Pág.

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    Research@WUR; Trends in Plant Science
    Article . 2023 . Peer-reviewed
    License: Elsevier TDM
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    Research@WUR
    Article . 2023
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    HAL-CEA
    Article . 2023
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    Authors: Catarina Jorge; Margarida Tomé; Ricardo Ruiz-Peinado; Lobna Zribi; +1 Authors

    The necessity for accurate biomass estimates is greater than ever for the sustainable management of forest resources, which is an increasingly pressing matter due to climate change. The most used method to estimate biomass for operational purposes is through allometric equations. Typically, each country develops their own models to be applied at the local scale because it is more convenient. But, for Quercus suber, a joint regional model can be more beneficial, since the species is distributed across the Mediterranean and is challenging to account for due to felling limitations and the nature of mature cork biomass itself. We found that these characteristics are reflected in the biomass datasets and compatibility was, perhaps, the largest impediment to such a model. The use of dummy variables to differentiate between countries, as well as compromises in the limits of biomass compartments, allowed us to develop two joint models to estimate aboveground biomass in Portugal, Spain and Tunisia. One model as a function of diameter and another as a function of diameter and total tree height. In addition, we developed a separate model for roots (modelling efficiency of fitting = 0.89), since it was not possible to assure additivity of the whole tree. All coefficients were estimated using Seemingly Unrelated Regressions (SUR) and model fitting assured additivity in the aboveground compartments—leaves and woody biomass (modelling efficiency of fitting = 0.89 and 0.93, respectively). This work proves that it is possible to have a biologically sound and efficient model for the three countries, despite differences in the observed allometric patterns. This research was funded by the Forest Research Centre, a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal (UIDB/00239/2020). Catarina Jorge was funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), under scholarship UI/BD/150757/2020. Peer reviewed 11Pág.

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    Forests
    Other literature type . Article . 2023 . Peer-reviewed
    License: CC BY
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    Forests
    Article . 2023
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Forestsarrow_drop_down
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      Forests
      Other literature type . Article . 2023 . Peer-reviewed
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      Forests
      Article . 2023
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    Authors: Felipe Viel; Renato Cotrim Maciel; Laio Oriel Seman; Cesar Albenes Zeferino; +2 Authors

    Hyperspectral images contain tens to hundreds of bands, implying a high spectral resolution. This high spectral resolution allows for obtaining a precise signature of structures and compounds that make up the captured scene. Among the types of processing that may be applied to Hyperspectral Images, classification using machine learning models stands out. The classification process is one of the most relevant steps for this type of image. It can extract information using spatial and spectral information and spatial-spectral fusion. Artificial Neural Network models have been gaining prominence among existing classification techniques. They can be applied to data with one, two, or three dimensions. Given the above, this work evaluates Convolutional Neural Network models with one, two, and three dimensions to identify the impact of classifying Hyperspectral Images with different types of convolution. We also expand the comparison to Recurrent Neural Network models, Attention Mechanism, and the Transformer architecture.. Furthermore, a novelty pre-processing method is proposed for the classification process to avoid generating data leaks between training, validation, and testing data. The results demonstrated that using 1 Dimension Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and Transformer architectures reduces memory consumption and sample processing time and maintain a satisfactory classification performance up to 99% accuracy on larger datasets. In addition, the Transfomer architecture can approach the 2D-CNN and 3D-CNN architectures in accuracy using only spectral information. The results also show that using two or three dimensions convolution layers improves accuracy at the cost of greater memory consumption and processing time per sample. Furthermore, the pre-processing methodology guarantees the disassociation of training and testing data.

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    Repositório Comum
    Article . 2016
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    IEEE Access
    Article . 2023
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    IEEE Access
    Article . 2023 . Peer-reviewed
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      Article . 2023
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    Authors: Hasselwander, Marc; Kiko, Mwendwa; Johnson, Ted;

    Digitalization and advances in information and communications technologies (ICT) are having a major impact on urban transport and the way people move around cities. However, this hardly relates to informal transport in the global South. Despite its importance and the fact that billions of people depend on it, insufficient efforts have been made by policymakers to improve informal transport services. In this think piece, we propose that this circumstance can be addressed on two counts through digital civic engagement and open data. In the digital age, civic engagement is no longer only associated with voluntary work at community level, but it is also very effective remotely. Digital civic engagement leverages social media to create awareness for in-situ concerns and provides a platform for local volunteers to connect with international experts to create people-centered solutions (e.g., mobile apps). Open data stimulates and facilitates digital civic engagement, which, in turn, can lead to the creation of more open data. Cities and local authorities should encourage and foster this virtuous cycle, and make transport data sets openly available. This can spur innovation and promote sustainable mobility behavior – with volunteers being powerful agents driving these efforts. Indeed, the case of the Trufi Association demonstrates that open data (e.g., in the form of volunteered geographic information) and the development of mobile apps in several developing cities have helped make informal transport more visible and transparent. The insights from the informal transport field can serve as an impetus for other sectors of the informal economy – such as retail, commerce, agriculture, manufacturing, and services – to promote digital civic engagement and open data to bring about positive change.

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    Authors: Rasti, B.; Chang, Y.; Dalsasso, E.; Denis, L.; +1 Authors

    Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox. Comment: Published in IEEE Geoscience and Remote Sensing Magazine

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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2021
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    IEEE Geoscience and Remote Sensing Magazine
    Article . 2022 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
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      IEEE Geoscience and Remote Sensing Magazine
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    Authors: Mark A. K. Gillespie; Mathilde Baude; Jacobus Biesmeijer; Nigel Boatman; +16 Authors

    Research into pollinators in managed landscapes has recently combined approaches of pollination ecology and landscape ecology, because key stressors are likely to interact across wide areas. While laboratory and field experiments are valuable for furthering understanding, studies are required to investigate the interacting drivers of pollinator health and diversity across a broader range of landscapes and a wider array of taxa. Here, we use a network of 96 study landscapes in six topographically diverse regions of Britain, to test the combined importance of honeybee density, insecticide loadings, floral resource availability and habitat diversity to pollinator communities. We also explore the interactions between these drivers and the cover and proximity of semi-natural habitat. We found that among our four drivers, only honeybee density was positively related to wild pollinator abundance and diversity, and the positive association between abundance and floral resources depended on insecticide loadings and habitat diversity. By contrast, our exploratory models including habitat composition metrics revealed a complex suite of interactive effects. These results demonstrate that improving pollinator community composition and health is unlikely to be achieved with general resource enhancements only. Rather, local land-use context should be considered in fine-tuning pollinator management and conservation. This article is part of the theme issue ‘Natural processes influencing pollinator health: from chemistry to landscapes’.

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    Europe PubMed Central
    Other literature type . 2022
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    Authors: S. Kotthaus; J. A. Bravo-Aranda; J. A. Bravo-Aranda; J. A. Bravo-Aranda; +17 Authors

    The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth’s surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sublayer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe. Marie Sklodowska-Curie Action Cofund 2016 EU project - Athenea3i national Portuguese funds through FCT - Fundacao para a Ciencia e Tecnologia, I.P. CA18235 871115 101036245 ANR-20-CE22-0013 PID2020-120015RB-I00 PID2020.117825GB.C21 P18-RT-3820 P20-00136 A-RNM-430-UGR20 754446 UIDB/04683/2020 European Cooperation in Science and Technology (COST) PTDC/CTAMET/29678/2017 European Commission University of Granada, Programa Operativo FEDER Andalucia 2014-2020 French National Research Agency (ANR) Region Ile-de-France Earth System Excellence Units Program Spanish Government Junta de Andalucia

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    https://doi.org/10.5194/amt-20...
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      https://doi.org/10.5194/amt-20...
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    Authors: Isabel Marques; Duarte Gouveia; Jean-Charles Gaillard; Sónia Martins; +6 Authors

    Drought is a major threat to coffee, compromising the quality and quantity of its production. We have analyzed the core proteome of 18 Coffea canephora cv. Conilon Clone 153 and C. arabica cv. Icatu plants and assessed their responses to moderate (MWD) and severe (SWD) water deficits. Label-free quantitative shotgun proteomics identified 3000 proteins in both genotypes, but less than 0.8% contributed to ca. 20% of proteome biomass. Proteomic changes were dependent on the severity of drought, being stronger under SWD and with an enrolment of different proteins, functions, and pathways than under MWD. The two genotypes displayed stress-responsive proteins under SWD, but only C. arabica showed a higher abundance of proteins involved in antioxidant detoxification activities. Overall, the impact of MWD was minor in the two genotypes, contrary to previous studies. In contrast, an extensive proteomic response was found under SWD, with C. arabica having a greater potential for acclimation/resilience than C. canephora. This is likely supported by a wider antioxidative response and an ability to repair photosynthetic structures, being crucial to develop new elite genotypes that assure coffee supply under water scarcity levels. International audience

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    Authors: Hind Bril El-Haouzi; Etienne Valette; Bettina-Johanna Krings; António Moniz;

    UIDB/04647/2020 UIDP/04647/2020 Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap between humans and systems in which human was seen as an important source of errors and disruptions. Today, the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised awareness about the central role humans have to play in manufacturing systems, the design of which must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of Things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different human social dimensions associated with CPS and IoT and focuses on their conceptual evolution regarding automated production systems’ sociability, notably by bringing humans back in the loop. Hereby, this paper aims to take stock of current research trends to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. Consequently, different models of sociability as a way to integrate humans in the broad sense and/or the develop future automated production systems have been identified from the literature and analysed. publishersversion published

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    https://doi.org/10.5445/ir/100...
    Article . 2021
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    https://doi.org/10.5445/ir/100...
    Article . 2021
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    Article . 2021
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