2,893 research outputs found

    Middle Pleistocene paleoenvironmental reconstruction through phytolith analysis at the Manyara Beds, northern Tanzania

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    This project is aimed at developing a detailed habitat reconstruction for hominins living at the Manyara Beds of Northern Tanzania during the early Middle Pleistocene using phytolith remains. The dissertation comprises three interlinked, but independent studies. The first study examines the phytolith assemblages from modern surface soils and plants to create a referential baseline for studying phytoliths from the Acacia-Commiphora ecosystem surrounding the Manyara Beds, the same plant regions in which our ancestors reside. Phytoliths from 21 species of plants, including 11 unstudied taxa from this ecosystem, were characterized. Twenty-five composite surface soil samples from five sites were also analyzed. Using Stromberg's 2003 classification and interpretive scheme, this study has demonstrated that the dominant phytoliths for Commiphora are polyhedral epidermal cells, and Acacia is a rare producer of blocky-faceted rectangular plate morphotypes. The second study examines phytolith assemblages from archaeological and non-archaeological sites within the six-meter zone of the uppermost part of the lower Manyara Beds. In general, phytolith assemblage from archaeological and non-archaeological sites confirms the persistence of C4 grasslands. However, varied habitats were available for the Acheulean tool-making hominins at archaeological site MK 4, which featured palms, woody dicots, sedge, and grasslands taxa, including high proportions of warm arid and moist loving C3 and C4 PACMADs and dry adapted C4 chloridoids. There is also a small presence of wet-loving panicoids. The palms, sedges, Commelinaceae, and other aquatic monocots indicate that Manyara Beds were well-watered, at least with the occurrence of freshwater springs or rivers near the Lake shores. Therefore, inferences from phytolith assemblages from the Manyara Beds are consistent with the common predictions of many Plio-Pleistocene sites near the lake shores, pointing to hominin's dependence on water and food resources such as plants and game. The third study presents the analysis of 106 stone tool residue samples from the MK4 site to understand the function of the small flake assemblage found there. Ten tools yielded phytoliths, including two flaked and eight core tools. Phytoliths revealed the exploitation of plant resources, including grasses, palms, sedges, woody dicots, and other unknown taxa

    The Craft Hub Journey:Project Catalogue

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    Introducing the Craft Hub project and the International Exhibition ‘Investigating Craft Practices across Europe’, including its journey across Europe, the artistic curation and set-up methodology for a replicable, accessible and sustainable design, adapting to seven unique exhibition spaces and content. The recurring themes, Heritage, Sustainability, Experimentation, Technological Innovation, Empowerment and Social Inclusion create common threads running through the activities and research carried out by each Craft Hub partner

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Holocene Vegetation, Drought, and Fire Variability in the Northern Great Basin, Oregon

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    The Northern Great Basin of Oregon is an area of diverse ecologies organized along elevational gradients and variable water sources. At the lowest elevations are the remnants of Pleistocene pluvial lakes, now deflated alkaline playas. Sagebrush (Artemisia tridentata) steppe dominates the region, and anywhere there is water at or very near the surface, marshes are present. At higher elevations open dry-forest systems begin appearing, composed primarily of Pinus ponderosa, but also including Pinus contorta and Juniperus occidentalis. Populus tremuloides is also found in greater abundance at these mid-elevation areas. High-elevation sites often host mixed conifer forests, subalpine forests, and some alpine conditions, with white-bark pine (Pinus albicaulis) found at some of the highest peaks. During the Pleistocene atmospheric conditions were cooler than present day, and evapotranspiration was much lower, resulting in the formation of large lakes. There were also glaciers present in some places, as well as locations too cold and dry to form glaciers.Climatic conditions began changing rapidly beginning ca. 12,000 years ago (Mehringer 1987; Wigand 1987). Maximum insolation continued warming the planet and peaked by 11,000 years ago most of the continental ice sheets were rapidly retreating while montane glaciers in the NGB had already retreated (Osborn and Beavis, 2001). By 9000 years ago maximum air temperatures and increased aridity resulted in the Northern Great Basin pluvial lakes desiccating and many vegetation communities shifting upward in elevation. Such climate changes would also cause fire event frequency to also change during this time, resulting in conditions and disturbance timings very different than the current day. Towards the end of the early Holocene the catastrophic eruption of Mount Mazama in ca. 7640 cal yr BP (Egan, 2015) would again alter vegetation communities and fire events to varying degrees depending on locations relative to the main eruption blast zone. Despite periodic droughts climatic conditions in the NGB have generally cooled through the late Holocene, with vegetation communities again responding. (Benson et al., 1997; Minckley et al., 2007; Marsicek et al., 2018). With an emphasis on the Holocene, questions behind this dissertation were driven by asking 1) by how much did vegetation communities change in the Northern Great Basin responding to changes in climate and fire, 2) which taxa changed the least, which the most, and was it climate or fire that drove those changes, 3) by how much is it possible to observe regional or local drought severity, and 4) by how much and when did climatic timing in the Northern Great Basin differ from Central and Southern Great Basin regions, if at all? To address these broad questions three locations were identified as good study sites. These three locations are within 40 km of each other but at different elevations. Differing elevations were sought for the purpose of attempting to determine what the rate of ecological change was for each location. There are few records showing continuous ecological and fire records at different elevations in the NGB from the early Holocene through to today, but of the records that do remain a rich history of variable timing for fire histories and ecological community structures are sharply delineated and preserved (Gruell, 1995; Minckley et al., 2007). Environmental conditions were reconstructed using traditional and novel methods for three sites identified as ideal for contributing ecological perspectives that would overlap in time. To reconstruct ecological settings, pollen was the primary data source for all three sites. Pollen assemblages provide a view of the climatic conditions at a given point in time, but in some cases may not reflect the full context of conditions as other variables such as tephra or charcoal may alter interpretations. Iin the case of coprolite pollen, a false sense of what vegetation is present on the landscape and in what abundances can occur. When available, carbon and nitrogen concentrations show how climate affected lake productivity, and charcoal provides insights on the fire-adapted landscape and how vegetation responded to changing arid conditions and fire events over time. Chapter 2 examines the late Pleistocene through early Holocene environmental conditions at a low-elevation site by contrasting the regional pollen signal preserved in the sediments of Paisley Caves to the more focused and hyper-local pollen found in chronologically contemporaneous coprolites produced by medium to large-sized mammals as they moved across their ancient landscapes. The results show several consistent differences in pollen assemblage composition in the coprolites compared to the sediments, consistent with the coprolite producers favoring certain environments prior to depositing coprolites in a cave. Chapter 3 examines the history of a rare mid-elevation freshwater lake in the NGB. Dog Lake is a landslide-formed lake whose lake level fluctuates annually, but remained very low during the early Holocene, followed by a period of low lake productivity and lower vegetation cover between 8700 and 8200 cal yr BP, then deepened to a point it resembled depths seen today. Using pollen, C and N concentrations, plant macrofossils, and charcoal, we found when lake productivity was low resulting from increased aridity in the early Holocene, there was also fewer fire episodes than expected from climate, likely due to low fuel availability and probably fewer ignition events. Fire frequencies increased with cooling temperatures and increased effective moisture during the middle Holocene. Chapter 4 describes the fire and hydrological history of White Pine Marsh (WPMA), a high-elevation site located in a small cirque valley at the northern terminus of the Warner Mountains. The site is in a mesic, mixed conifer forest with the perennial marsh having formed after the Mazama eruption and subsequent deposition of tephra in the basin. Sediments also show the fire history was also altered by the tephra. Charcoal showed fires were more frequent and increased in intensity during the early Holocene, abruptly changing to lower intensity and longer fire intervals post-Mazama. Pollen showed mixed conifer conditions since 9500 cal yr BP with Pinus ponderosa always dominant with variable presence of Abies. This dissertation includes published and unpublished co-authored material. At the time of writing Chapter 2 is in press at Quaternary Review. Co-authors include Daniel Gavin, Erin Herring, and Dennis Jenkins. Herring processed the coprolites and provided analysis descriptions. Jenkins provided site expertise to this paper. Gavin and Saban conceptualized the study and devised the methodology of analysis. Both Gavin and Saban analyzed the sedimentary lithological components. Saban wrote the original manuscript with Gavin’s help in the analysis and visualization of the data. Gavin also reviewed, edited, and contributed to the final manuscript. In chapter 3 Saban and Gavin conceptualized the study and analyzed sediments. Saban analyzed the pollen and charcoal. Analysis and data visualizations were significantly aided by Gavin while Saban wrote the original manuscript. Gavin further reviewed, edited, and contributed to the final manuscript. In chapter 4 Saban and Gavin conceptualized the study and devised the research methodology as well as analyzed the sediments, while Saban processed and analyzed pollen and charcoal. Saban wrote the original paper, and Gavin reviewed, edited, and contributed to the final manuscript

    Remote Sensing Approach for Monitoring Coastal Wetland in the Mekong Delta, Vietnam: Change Trends and Their Driving Forces

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    Coastal wetlands in the Mekong Delta (MD), Vietnam, provide various vital ecosystem services for the region. These wetlands have experienced critical changes due to the increase in regional anthropogenic activities, global climate change, and the associated sea level rise (SLR). However, documented information and research on the dynamics and drivers of these important wetland areas remain limited for the region. The present study aims to determine the long-term dynamics of wetlands in the south-west coast of the MD using remote sensing approaches, and analyse the potential factors driving these dynamics. Wetland maps from the years 1995, 2002, 2013, and 2020 at a 15 m spatial resolution were derived from Landsat images with the aid of a hybrid classification approach. The accuracy of the wetland maps was relatively high, with overall accuracies ranging from 86–93%. The findings showed that the critical changes over the period 1995/2020 included the expansion of marine water into coastal lands, showing 129% shoreline erosion" a remarkable increase of 345% in aquaculture ponds" and a reduction of forested wetlands and rice fields/other crops by 32% and 73%, respectively. Although mangrove forests slightly increased for the period 2013/2020, the overall trend was also a reduction of 5%. Our findings show that the substantial increase in aquaculture ponds is at the expense of mangroves, forested wetlands, and rice fields/other crops, while shoreline erosion significantly affected coastal lands, especially mangrove forests. The interaction of a set of environmental and socioeconomic factors were responsible for the dynamics. In particular, SLR was identified as one of the main underlying drivers" however, the rapid changes were directly driven by policies on land-use for economic development in the region. The trends of wetland changes and SLR implicate their significant effects on environment, natural resources, food security, and likelihood of communities in the region sustaining for the long-term. These findings can assist in developing and planning appropriate management strategies and policies for wetland protection and conservation, and for sustainable development in the region

    The development, feasibility, and acceptability of a breakfast group intervention for stroke rehabilitation

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    Background: There are 1.2 million stroke survivors in the UK and the number is projected to increase significantly over the next decade. Research suggests that between 50% and 80% of hospitalised stroke survivors experience difficulties with eating and drinking. Presently, rehabilitation approaches to address these difficulties involve individual rehabilitation sessions led by uni-professionals. Recent national stroke guidance recommends that stroke survivors receive three hours of daily rehabilitation and emphasises the importance of addressing the psychosocial aspects of recovery. Implementing these recommendations presents a challenge to healthcare professionals, who must explore innovative methods to provide the necessary rehabilitation intensity. This study aimed to address these challenges by codesigning a multi-disciplinary breakfast group intervention and implementation toolkit to improve psychosocial outcomes. Methods: The Hawkins 3-step framework for intervention design was used to develop a multidisciplinary breakfast group intervention and to understand if it was acceptable and feasible for patients and healthcare professionals in an acute stroke ward. The Hawkins 3- steps were 1) evidence review and consultations 2) coproduction 3) prototyping. In collaboration with fifteen stakeholders, a prototype breakfast group intervention and implementation toolkit were codesigned over four months. Experience-based Codesign was used to engage stakeholders. Results: The literature review is the first to investigate the psychosocial impact of eating and drinking difficulties post stroke. The key finding was the presence of psychological and social impacts which included, the experience of loss, fear, embarrassment shame and humiliation as well as social isolation. Stroke survivors were striving to get back to normality and this included the desire to socially dine with others. Two prototype iterations of the intervention were tested with 16 stroke survivors across three hospital sites. The multidisciplinary breakfast group intervention was designed to offer intensive rehabilitation in a social group context. The codesigned implementation toolkit guided a personalised and tailored approach. A perceived benefit of the intervention was the opportunity to address the psychosocial aspects of eating and drinking rehabilitation as well as providing physical rehabilitation. Stroke survivors highly value the opportunity to socialise and receive support from their peers. The intervention was acceptable to both patients and healthcare professionals, and the workforce model proved practical and feasible to deliver using a collaborative approach in the context of resource-limited healthcare. Conclusions: The breakfast group interventions, developed through codesign, were positively received by patients and staff and feasible to deliver. They introduce an innovative and novel approach to stroke rehabilitation, personalised to each individual's needs, and offer a comprehensive intervention which addresses both physical and psychosocial aspects which target challenges related to eating and drinking. Unique contributions of this study include a theoretical model for breakfast group interventions, a programme theory and practical tool kit for clinicians to support the translation of research findings and implement breakfast groups in clinical practice

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

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    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies

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    Detecting subtle defects in window frames, including dents and scratches, is vital for upholding product integrity and sustaining a positive brand perception. Conventional machine vision systems often struggle to identify these defects in challenging environments like construction sites. In contrast, modern vision systems leveraging machine and deep learning (DL) are emerging as potent tools, particularly for cosmetic inspections. However, the promise of DL is yet to be fully realized. A few manufacturers have established a clear strategy for AI integration in quality inspection, hindered mainly by issues like scarce clean datasets and environmental changes that compromise model accuracy. Addressing these challenges, our study presents an innovative approach that amplifies defect detection in DL models, even with constrained data resources. The paper proposes a new defect detection pipeline called InspectNet (IPT-enhanced UNET) that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset and a Unet model tuned for window frame defect detection and segmentation. Experiments were carried out using a Spot Robot doing window frame inspections . 16 variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that, on average, across all proposed evaluation measures, Unet outperformed all other algorithms when IPT-enhanced augmentations were applied. In particular, when using the best dataset, the average Intersection over Union (IoU) values achieved were IPT-enhanced Unet, reaching 0.91 of mIoU
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