1,367 research outputs found

    From ecosystems to people: examining the variability in the provision of ecosystem services by eelgrass meadows in Atlantic Canada

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    Seagrass meadows provide functions that support other species and ecosystem services that directly and indirectly benefit human wellbeing. However, growing in estuarine environments, seagrass meadows are exposed to interacting pressures from terrestrial and marine systems, resulting in their degradation worldwide. Efforts to conserve these social-ecological systems have met challenges, including insufficient maps to assess seagrass status and value, a limited understanding of seagrass meadow ecosystem traits underpinning the provision of ecosystem services, and a lack of public awareness necessary to support management decisions. This thesis presents multidisciplinary studies of eelgrass (Zostera marina) meadows in Placentia Bay, Newfoundland and Labrador, Canada, that contribute toward addressing these challenges. In the first study, I evaluated the reproducibility of using remotely piloted aircraft systems (RPAS) to collect seasonal maps of submerged eelgrass meadows in a temperate environment. I show that higher altitude surveys are beneficial when surveying in rapidly changing environments; however, RPAS surveys using three-colour band imagery alone may be insufficient to discriminate seasonal changes. In the second and third studies, I identified meadow structural and environmental traits underpinning eelgrass service as fish habitat and function as a coastal filter. In the second study, I show that shallower and more saline eelgrass meadows enhance diversity in fish life history traits. In the third study, I show that carbon and nitrogen content in the surface sediment was negatively related to sediment density, where isotopic ratios indicated that the carbon was predominantly derived from marine allochthonous (non-eelgrass) sources. Lastly, in the fourth study, using an online survey, I show strong awareness of eelgrass by Canadian coastal Atlantic community members, and support for conservation efforts. Participants identified fish habitat, coastal protection, and water quality maintenance as the three most important ecosystem services provided by eelgrass in Atlantic Canada. Together, the components of this thesis characterise three Newfoundland and Labrador eelgrass meadows, the services they provide, and synthesises the perception of eelgrass by Canadian coastal Atlantic community members. These findings are relevant to local management decision-making and eelgrass monitoring, while also contributing to the growing global characterization of the variability in eelgrass meadow function driving ecosystem services

    EVALUATION OF EFFECTS OF NEW ANTIFOULING SYSTEMS, ALTERNATIVE TO ORGANOTIN COMPOUNDS, ON BENTHIC MARINE INVERTEBRATES AT ECOSYSTEM, ORGANISMAL AND CELLULAR LEVEL

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    Marine biofouling on anthropic submerged substrata is associated with major ecological and socioeconomic impacts worldwide. The most widely used antifouling systems are chemical ones represented by paints with a biocide, to which booster substances can be added. The latter are highly toxic chemical substances from agriculture (herbicides, fungicides, acaricides, wood preservatives) and pharmaceutical industry (bactericides, fungicides), these cause various ecological problems due to disruptive effects provoked on non-target organisms and depletion of coastal biocoenoses. From 2001, the paints including organotin compounds (TBT and TPT), which had the best performance and were used worldwide for decades, were banned by International Maritime Organization (IMO) after the discovery of their severe impact on the oyster farms. As a consequence of the restrictions on the use of organotin-based paints, finding new antifouling systems has become a primary necessity. Therefore, the research was devoted to new eco-friendly formulations. Regarding Physical antifouling systems have been recently introduced in relation to the development of a more environmentally friendly approach rather than the chemical systems. My scholarship has been entirely financed by RESIMIX s.r.l of Brendola, Vicenza (Italy). The university-enterprise collaboration aimed to develop a new eco-friendly paint. More in general, the research program of my PhD thesis focused on the implementation of new antifouling systems with low effects on benthic marine invertebrates. My PhD activity it was been divided in 2 tasks, i.e., chemical antifouling systems and physical antifouling systems. To determine and compare the effects of these new antifouling systems on both target species (ascidians and mussels) and non-target species (clams) the tasks have been developed at three study levels, i.e., ecosystem, individuals, and cells. As regards of chemical antifouling systems I have been investigated the significant differences in the ecological succession of hard-substratum community, by means of a series of biodiversity indexes, during at least one-year exposure to various RESIMIX paints and trade copper-based paints. In addition, a comparative monitoring with trade antifouling paints has been considered together with the effects on settlement and metamorphosis of ascidian larvae and finally, the observation of the mechanisms of action in in vitro immunotoxicity assays on dominant bioindicators in benthic biocoenoses like tunicates, clams and mussels. From these preliminary but significant results about chemical antifouling systems, crucial questions have arisen regarding the continuous indiscriminate introduction of such biocides into the environment. As regards physical antifouling systems I have been considered geotextiles (for protection from coastal erosion), and ultrasound (to prevent biofilm and disturb the larval settlement) reaching interesting results in both the field and the lab, which revealed the till now hidden downside of these systems.Marine biofouling on anthropic submerged substrata is associated with major ecological and socioeconomic impacts worldwide. The most widely used antifouling systems are chemical ones represented by paints with a biocide, to which booster substances can be added. The latter are highly toxic chemical substances from agriculture (herbicides, fungicides, acaricides, wood preservatives) and pharmaceutical industry (bactericides, fungicides), these cause various ecological problems due to disruptive effects provoked on non-target organisms and depletion of coastal biocoenoses. From 2001, the paints including organotin compounds (TBT and TPT), which had the best performance and were used worldwide for decades, were banned by International Maritime Organization (IMO) after the discovery of their severe impact on the oyster farms. As a consequence of the restrictions on the use of organotin-based paints, finding new antifouling systems has become a primary necessity. Therefore, the research was devoted to new eco-friendly formulations. Regarding Physical antifouling systems have been recently introduced in relation to the development of a more environmentally friendly approach rather than the chemical systems. My scholarship has been entirely financed by RESIMIX s.r.l of Brendola, Vicenza (Italy). The university-enterprise collaboration aimed to develop a new eco-friendly paint. More in general, the research program of my PhD thesis focused on the implementation of new antifouling systems with low effects on benthic marine invertebrates. My PhD activity it was been divided in 2 tasks, i.e., chemical antifouling systems and physical antifouling systems. To determine and compare the effects of these new antifouling systems on both target species (ascidians and mussels) and non-target species (clams) the tasks have been developed at three study levels, i.e., ecosystem, individuals, and cells. As regards of chemical antifouling systems I have been investigated the significant differences in the ecological succession of hard-substratum community, by means of a series of biodiversity indexes, during at least one-year exposure to various RESIMIX paints and trade copper-based paints. In addition, a comparative monitoring with trade antifouling paints has been considered together with the effects on settlement and metamorphosis of ascidian larvae and finally, the observation of the mechanisms of action in in vitro immunotoxicity assays on dominant bioindicators in benthic biocoenoses like tunicates, clams and mussels. From these preliminary but significant results about chemical antifouling systems, crucial questions have arisen regarding the continuous indiscriminate introduction of such biocides into the environment. As regards physical antifouling systems I have been considered geotextiles (for protection from coastal erosion), and ultrasound (to prevent biofilm and disturb the larval settlement) reaching interesting results in both the field and the lab, which revealed the till now hidden downside of these systems

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    The material culture of English rural households c.1250-1600

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    Drawing on archaeological and historical evidence, which comprises objects recovered from archaeological excavations, Escheators’ records from the 14th and 15th centuries and Coroners’ records from the 16th century, this is the first comprehensive analysis of the possessions of non-elite households in medieval England

    Subjective Wellbeing of Undergraduate Engineering Students: A Mixed Methods Study

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    In higher education, the prevalence of mental health issues among students has raised concerns regarding their overall success and wellbeing. While existing research often focuses on identifying and addressing mental health problems, there is a lack of emphasis on understanding the positive contributors to students\u27 mental health. In this study, I expand the concept of mental health beyond the absence of negative mental health states to include the presence of positive mental health aspects through the concept of Subjective Wellbeing (SWB) (feeling that your life is going well, not badly), of engineering undergraduate participants. Both qualitative and quantitative data were collected from engineering undergraduate students within the College of Engineering at Utah State in a Concurrent Mixed Methods paradigm through an online survey. Analysis of the data provided valuable insights into SWB among undergraduate students and the factors perceived to contribute to it. Furthermore, this research offers recommendations aimed at enhancing the collegiate experiences of engineering undergraduates to positively influence their mental health and overall wellbeing. By focusing on the holistic understanding of subjective wellbeing, this study contributes to the broader dialogue on student mental health and the promotion of a thriving academic environment

    Ciguatoxins

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    Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies
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