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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: Kuusemets, Laura;

    Master`s Thesis Environmental Governance and Adaptation to Climate Change Curriculum Nitrogen (N) is a crucial nutrient for crop growth production. The input of N in the form of fertilisers increases crop yield. On the other hand, agricultural nitrogen inputs can cause N leaching and the loss of N to the atmosphere, contributing to global warming. The main objectives of the study were to evaluate whether and how different crop species and fertilisation norms affect nitrous oxide (N2O) emissions and soil microbiome using the closed chamber method and quantitative polymerase chain reaction (qPCR) analysis. The study was done in the IOSDV (International Organic Nitrogen Long-term Fertilisation Experiment) experimental field. The crop species studied were barley, sorghum, and wheat. The fertiliser treatment is constituted of mineral N fertilisation and mineral N fertilisation combined with farmyard manure amendment. Three N fertiliser treatment norms were used: 0, 80 and 160 kg ha−1 . Samples were collected over seven months, from April 2022 to October 2022. qPCR was used to measure the abundance of bacterial and archaeal specific16S rRNA, nitrification (bacterial, archaeal and COMAMMOX amoA genes), denitrification (nirK, nirS, nosZI and nosZII genes) and dissimilatory nitrate reduction to ammonium (DNRA; nrfA gene) marker genes from the soil samples. The results of this study indicate that different fertilisation rates influence N2O emissions and the highest N2O emissions are emitted from the highest N fertiliser treatment (160 kg ha−1). Crop species did not have a significant effect on N2O emissions. Sorghum plots with mineral N fertilisation combined with farmyard manure amendment had slightly higher average N2O emissions compared to sorghum plots with mineral N fertilisation without manure. The microbial analysis showed nitrification was the primary process resulting in N2O emissions. Lämmastik (N) on kriitilise tähtsusega toitaine taimekasvatuses. Lämmastiku lisamine väetisena tõstab põllukultuuride saagikust. Teisalt, lämmastiku lisamine võib kaasa tuua lämmastiku leostumise ja lämmastiku kaod atmosfääri, panustades globaalsesse kliima soojenemisse. Käesoleva uurimustöö põhieesmärk oli hinnata, kas ja kuidas erinevad põllukultuurid ja väetusnromid mõjutavad dilämmastikoksiidi (N2O) vooge ja mulla mikrobioomi kasutades staatilise pimekambri meetodit ja kvantitatiivset polümeraasi ahelreaktsiooni (qPCR). Uurimistöö tehti IOSDV (International Organic Nitrogen Long-term Fertilisation Experiment) katsepõllul. Uuritud põllukultuurid olid oder, sorgo ja nisu. Väetistöötlus koosnes väetamisest mineraalse lämmastikuga ja tahesõnnikuga kombineeritud mineraalväetamisest. Katses kasutati kolme erinevat mineraalväetise normi: 0, 80 ja 160 kg ha−1 N. Proove koguti seitsme kuu vältel aprillist oktoobrini aastal 2022. Mullaproovidest määrati bakterite ja arhede 16S rRNA, nitrifikatsiooni (bakterite, arhede ja COMAMMOX amoA geenid), denitrifikatsiooni (nirK, nirS, nosZI ja nosZII) ja dissimilatoorse nitraadi reduktsioon ammooniumiks (DNRA; nrfA geen) markergeenide arvukus reaal-aja polümeraasi ahelreaktsiooni (qPCR) meetodil. Töö tulemustest järeldus, et erinevad väetise normed mõjutasid N2O emissioonide kontsentratsiooni ja suurimad N2O emissioonid tulenesid suurima väetisenormiga lappidelt (160 kg ha−1). Põllukultuurid ei omanud statistiliselt olulist mõju N2O emissioonidele. Mineraalse lämmastiku ja sõnniku kooskasutamisel sorgo lapil olid keskmised N2O vood veidi suuremad võrreldes sorgo lappidega, kus kasutati vaid mineraalset lämmastikväetist. Mikrobioloogilised analüüsid näitasid, et suurimad N2O emissioonid tulenesid nitrifikatsiooni protsessist.

    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/ EMU DSpacearrow_drop_down
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    Master thesis . 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/ EMU DSpacearrow_drop_down
      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/
      EMU DSpace
      Master thesis . 2023
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    Authors: Stéphane C. K. Tékouabou; Jérôme Chenal; Rida Azmi; Hamza Toulni; +2 Authors

    With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes.

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    Other literature type . Article . 2022 . Peer-reviewed
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      Other literature type . Article . 2022 . Peer-reviewed
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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
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    Authors: Fahed Alkhabbas; Mohammed Alsadi; Sadi Alawadi; Feras M. Awaysheh; +2 Authors

    Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors. open access

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    Sensors
    Other literature type . Article . 2022 . Peer-reviewed
    License: CC BY
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    Sensors
    Article . 2022
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    NTNU Open
    Article . 2022
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      Other literature type . Article . 2022 . Peer-reviewed
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      Article . 2022
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    Authors: Zhichao Xia; Yue He; Helena Korpelainen; Ülo Niinemets; +1 Authors

    We assessed root phenolics and rhizosphere microbiomes of Populus cathayana females and males in the replicated 30-year-old plantations, including pure female plantations (PF), pure male plantations (PM), and mixed female and male plantations (MS) to reveal sex and neighbor effects, and associations between root phenolic metabolites and root-related microbes. The phenolic composition of females varied more between intrasexual and intersexual interactions compared to that of males. Thus, sexual dimorphism was present in the metabolic composition and biochemical plasticity. MS plantations enhanced the bacterial and fungal alpha diversity of both females and males. The composition of fungal communities of females and males in MS plantations was different from that in PF and PM, while such differences were not found in the composition of bacterial communities. Bacterial and fungal diversities were correlated with concentrations of specific phenolic metabolites and were most positively responsive to root benzoic acid and pinoresinol production, respectively. Our findings indicate that sex-specific interactions affect the system of plant sex - root phenolics - rhizosphere microbes, and they may contribute to sex-specific resource utilization patterns. Knowledge of such mechanisms would be helpful when establishing plantations of dioecious plants. Peer reviewed

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    Forest Ecology and Management
    Article . 2022 . Peer-reviewed
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    Authors: Shahbaz Siddiqui; Sufian Hameed; Syed Attique Shah; Ijaz Ahmad; +3 Authors

    Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.

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    IEEE Access
    Article . 2022
    Data sources: VIRTA
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    UPF Digital Repository
    Article . 2022
    License: CC BY
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    IEEE Access
    Article . 2022 . Peer-reviewed
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    Authors: Siim Pärt; Harri Kankaanpää; Jan-Victor Björkqvist; Jan-Victor Björkqvist; +2 Authors

    A large part of oil spills happen near busy marine fairways. Presently, oil spill detection and monitoring are mostly done with satellite remote sensing algorithms, or with remote sensors or visual surveillance from aerial vehicles or ships. These techniques have their drawbacks and limitations. We evaluated the feasibility of using fluorometric sensors in flow-through systems for real-time detection of oil spills. The sensors were capable of detecting diesel oil for at least 20 days in laboratory conditions, but the presence of CDOM, turbidity and algae-derived substances substantially affected the detection capabilities. Algae extract was observed to have the strongest effect on the fluorescence signal, enhancing the signal in all combinations of sensors and solutions. The sensors were then integrated to a FerryBox system and a moored SmartBuoy. The field tests support the results of the laboratory experiments, namely that the primary source of the measured variation was the presence of interference compounds. The 2 month experiments data did not reveal peaks indicative of oil spills. Both autonomous systems worked well, providing real-time data. The main uncertainty is how the sensors' calibration and specificity to oil, and the measurement depth, affects oil detection. We recommend exploring mathematical approaches and more advanced sensors to correct for natural interferences.

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    Frontiers in Marine Science
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    Frontiers in Marine Science
    Article . 2021 . Peer-reviewed
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      Frontiers in Marine Science
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    Authors: Janika Leoste; Tiia Õun; Krista Loogma; José San Martín López;

    Implementing an Emerging Technology (ET) is a difficult task due to people lacking ET-related knowledge and skills or having skeptical and negative attitudes towards the ET. As learners construct their understanding about an ET and develop related skills by actually passing through the ET Innovation Process (IP) stages (Awareness, Acceptance and Adoption), it could be useful to provide them with training that imitates certain IP stages. Using Artificial Intelligence Enhanced Robotics (AIER) as the example ET, we designed a two-day workshop to lead learners (n = 16) through the AIER IP Awareness stage, and a six-week training course with eight contact days to simulate the AIER IP Acceptance stage to learners (n = 10). Using online surveys and quantitative content analysis methods we confirmed that the workshop format increased the AIER-related self-confidence and general knowledge in 78% of participants, while the training course helped more than half of the participants to construct usable knowledge about a specific AIER and to see its possibilities in their specific work-place contexts. This paper is the pilot of using the Technology-Enhanced Learning Innovation Process (TELIP) model, first tested on a STEAM innovation, outside the educational context, for developing appropriate training approaches for specific ET IP stages.

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    Mathematics
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    Mathematics
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    Authors: Mihkel Are; Tanel Kaart; Are Selge; Endla Reintam;

    whereas in the subsoil (30–35 cm): potato (50.6%), pea (48.5%), red clover (47.9%), spring barley (47.7%), winter wheat (46.4%). Therefore, potato was a noticeable crop, as among the crops, it had the lowest WSA content in the topsoil, while highest in the subsoil. The results shown gave an assumption that the after-effects of some crops (foremost with pea) were noticeable in the soil properties during the following crop. In the topsoil, the differences between crops were significant among crops just for TW and TW+M treatments. In TW, potato was lower than spring barley and winter wheat, but not significantly lower than pea or red clover. In the subsoil, significant differences between crops were observed for TC and TW treatments: in TC, potato was just significantly greater than red clover (but similar to other crops), and in TW, significantly greater than winter wheat. Furthermore, in the topsoil the soil organic carbon (SOC) content was not significantly affected by crops, and the use of winter cover crops generally increased the SOC content while concurrently decreased the WSA content and the soil maximum water holding capacity. This was probably caused by the additional tillage operations which cancelled out the possible benefits for the soil aggregates. As a consequence of the constantly declining SOC content, caused by the weakened soil aggregates, the plant-available P and K contents, especially in the absence of manure applications, decreased as well, probably due to the combination of fixation and removal of plant biomass. Therefore, it is expected that by continuing this trend, the plant growing conditions decline, which in turn will have a negative effect for the aggregate formation and carbon sequestration, which are essential for plant growth. The stability of the soil aggregates is an important soil quality indicator, as it affects the soil’s overall functionality. As the soil aggregates are highly affected by agricultural practices, it is essential to know how crops interact with the aggregation process. Therefore, for obtaining more knowledge, this research was conducted in Estonia in an organic crop rotation field experiment from 2012/2013 through 2015/2016 to study the effects of crops (potato → spring barley undersown with red clover → red clover → winter wheat → pea) under different treatments (TC—control TW+M—TW with farmyard manure 40 Mg ha−1 per crop rotation). The results showed that in the topsoil (5–10 cm), the soil water-stable aggregate (WSA) content (determined by the wet sieving method) from highest to lowest was following: pea (61.7%), winter wheat (61.6%), spring barley (61.5%), red clover (59.3%), potato (57.1%) TW—winter cover crops

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    Agriculture
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    Agriculture
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      Agriculture
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    Authors: Giedrė Kvieskienė; Ilze Ivanova; Karmen Trasberg; Viktorija Stasytytė; +1 Authors

    NEET (Not in Education, Employment, or Training) youth rates in Europe are generally higher in rural regions than in urban areas and the share in rural regions is constantly increasing. During the COVID-19 pandemic, young people became even more vulnerable as they experienced social exclusion and mental health problems. The objective of this paper is to analyse NEET youthrelated statistics in Europe and distinguish positive initiatives for young people in rural areas of the Baltic countries to encourage positive emotions and willingness to learn. Statistical analysis and case study methods were employed. Data on youth unemployment, NEET youth by age and gender, and poverty and social exclusion of young people, is analysed. Social policy initiatives in Lithuania, Latvia, and Estonia, mainly from rural municipalities, are presented and discussed. This research determines the key issues related to NEET youth and proposes initiatives to overcome existing problems among young people. Such social initiatives aim to promote positive social emotions of youth, promote their inclusion in society, and foster regional sustainability. Keywords: NEET youth; unemployment; COVID-19 pandemic; emotions; mental health.

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    Social Sciences
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    Authors: Jeremy Irvin; Sharon Zhou; Gavin McNicol; Fred Lu; +89 Authors

    Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET). ISSN:0168-1923 ISSN:1873-2240

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    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/ NARCISarrow_drop_down
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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
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      Article . 2021
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203 Research products
  • 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: Kuusemets, Laura;

    Master`s Thesis Environmental Governance and Adaptation to Climate Change Curriculum Nitrogen (N) is a crucial nutrient for crop growth production. The input of N in the form of fertilisers increases crop yield. On the other hand, agricultural nitrogen inputs can cause N leaching and the loss of N to the atmosphere, contributing to global warming. The main objectives of the study were to evaluate whether and how different crop species and fertilisation norms affect nitrous oxide (N2O) emissions and soil microbiome using the closed chamber method and quantitative polymerase chain reaction (qPCR) analysis. The study was done in the IOSDV (International Organic Nitrogen Long-term Fertilisation Experiment) experimental field. The crop species studied were barley, sorghum, and wheat. The fertiliser treatment is constituted of mineral N fertilisation and mineral N fertilisation combined with farmyard manure amendment. Three N fertiliser treatment norms were used: 0, 80 and 160 kg ha−1 . Samples were collected over seven months, from April 2022 to October 2022. qPCR was used to measure the abundance of bacterial and archaeal specific16S rRNA, nitrification (bacterial, archaeal and COMAMMOX amoA genes), denitrification (nirK, nirS, nosZI and nosZII genes) and dissimilatory nitrate reduction to ammonium (DNRA; nrfA gene) marker genes from the soil samples. The results of this study indicate that different fertilisation rates influence N2O emissions and the highest N2O emissions are emitted from the highest N fertiliser treatment (160 kg ha−1). Crop species did not have a significant effect on N2O emissions. Sorghum plots with mineral N fertilisation combined with farmyard manure amendment had slightly higher average N2O emissions compared to sorghum plots with mineral N fertilisation without manure. The microbial analysis showed nitrification was the primary process resulting in N2O emissions. Lämmastik (N) on kriitilise tähtsusega toitaine taimekasvatuses. Lämmastiku lisamine väetisena tõstab põllukultuuride saagikust. Teisalt, lämmastiku lisamine võib kaasa tuua lämmastiku leostumise ja lämmastiku kaod atmosfääri, panustades globaalsesse kliima soojenemisse. Käesoleva uurimustöö põhieesmärk oli hinnata, kas ja kuidas erinevad põllukultuurid ja väetusnromid mõjutavad dilämmastikoksiidi (N2O) vooge ja mulla mikrobioomi kasutades staatilise pimekambri meetodit ja kvantitatiivset polümeraasi ahelreaktsiooni (qPCR). Uurimistöö tehti IOSDV (International Organic Nitrogen Long-term Fertilisation Experiment) katsepõllul. Uuritud põllukultuurid olid oder, sorgo ja nisu. Väetistöötlus koosnes väetamisest mineraalse lämmastikuga ja tahesõnnikuga kombineeritud mineraalväetamisest. Katses kasutati kolme erinevat mineraalväetise normi: 0, 80 ja 160 kg ha−1 N. Proove koguti seitsme kuu vältel aprillist oktoobrini aastal 2022. Mullaproovidest määrati bakterite ja arhede 16S rRNA, nitrifikatsiooni (bakterite, arhede ja COMAMMOX amoA geenid), denitrifikatsiooni (nirK, nirS, nosZI ja nosZII) ja dissimilatoorse nitraadi reduktsioon ammooniumiks (DNRA; nrfA geen) markergeenide arvukus reaal-aja polümeraasi ahelreaktsiooni (qPCR) meetodil. Töö tulemustest järeldus, et erinevad väetise normed mõjutasid N2O emissioonide kontsentratsiooni ja suurimad N2O emissioonid tulenesid suurima väetisenormiga lappidelt (160 kg ha−1). Põllukultuurid ei omanud statistiliselt olulist mõju N2O emissioonidele. Mineraalse lämmastiku ja sõnniku kooskasutamisel sorgo lapil olid keskmised N2O vood veidi suuremad võrreldes sorgo lappidega, kus kasutati vaid mineraalset lämmastikväetist. Mikrobioloogilised analüüsid näitasid, et suurimad N2O emissioonid tulenesid nitrifikatsiooni protsessist.

    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/ EMU DSpacearrow_drop_down
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    EMU DSpace
    Master thesis . 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/ EMU DSpacearrow_drop_down
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      EMU DSpace
      Master thesis . 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: Stéphane C. K. Tékouabou; Jérôme Chenal; Rida Azmi; Hamza Toulni; +2 Authors

    With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes.

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    Other literature type . Article . 2022 . Peer-reviewed
    License: CC BY
    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
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      Other literature type . Article . 2022 . Peer-reviewed
      License: CC BY
      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
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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: Fahed Alkhabbas; Mohammed Alsadi; Sadi Alawadi; Feras M. Awaysheh; +2 Authors

    Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors. open access

    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/ Sensorsarrow_drop_down
    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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    Other literature type . Article . 2022 . 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/