8,590 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions

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    Despite research history spanning more than a century, settlement patterns still hold a promise to contribute to the theories of large-scale processes in human history. Mostly they have been presented as passive imprints of past human activities and spatial interactions they shape have not been studied as the driving force of historical processes. While archaeological knowledge has been used to construct geographical theories of evolution of settlement there still exist gaps in this knowledge. Currently no theoretical framework has been adopted to explore them as spatial systems emerging from micro-choices of small population units. The goal of this thesis is to propose a conceptual model of adaptive settlement systems based on complex adaptive systems framework. The model frames settlement system formation processes as an adaptive system containing spatial features, information flows, decision making population units (agents) and forming cross scale feedback loops between location choices of individuals and space modified by their aggregated choices. The goal of the model is to find new ways of interpretation of archaeological locational data as well as closer theoretical integration of micro-level choices and meso-level settlement structures. The thesis is divided into five chapters, the first chapter is dedicated to conceptualisation of the general model based on existing literature and shows that settlement systems are inherently complex adaptive systems and therefore require tools of complexity science for causal explanations. The following chapters explore both empirical and theoretical simulated settlement patterns based dedicated to studying selected information flows and feedbacks in the context of the whole system. Second and third chapters explore the case study of the Stone Age settlement in Estonia comparing residential location choice principles of different periods. In chapter 2 the relation between environmental conditions and residential choice is explored statistically. The results confirm that the relation is significant but varies between different archaeological phenomena. In the third chapter hunter-fisher-gatherer and early agrarian Corded Ware settlement systems were compared spatially using inductive models. The results indicated a large difference in their perception of landscape regarding suitability for habitation. It led to conclusions that early agrarian land use significantly extended land use potential and provided a competitive spatial benefit. In addition to spatial differences, model performance was compared and the difference was discussed in the context of proposed adaptive settlement system model. Last two chapters present theoretical agent-based simulation experiments intended to study effects discussed in relation to environmental model performance and environmental determinism in general. In the fourth chapter the central place foragingmodel was embedded in the proposed model and resource depletion, as an environmental modification mechanism, was explored. The study excluded the possibility that mobility itself would lead to modelling effects discussed in the previous chapter. The purpose of the last chapter is the disentanglement of the complex relations between social versus human-environment interactions. The study exposed non-linear spatial effects expected population density can have on the system and the general robustness of environmental inductive models in archaeology to randomness and social effect. The model indicates that social interactions between individuals lead to formation of a group agency which is determined by the environment even if individual cognitions consider the environment insignificant. It also indicates that spatial configuration of the environment has a certain influence towards population clustering therefore providing a potential pathway to population aggregation. Those empirical and theoretical results showed the new insights provided by the complex adaptive systems framework. Some of the results, including the explanation of empirical results, required the conceptual model to provide a framework of interpretation

    Communicating a Pandemic

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    This edited volume compares experiences of how the Covid-19 pandemic was communicated in the Nordic countries – Denmark, Finland, Iceland, Norway, and Sweden. The Nordic countries are often discussed in terms of similarities concerning an extensive welfare system, economic policies, media systems, and high levels of trust in societal actors. However, in the wake of a global pandemic, the countries’ coping strategies varied, creating certain question marks on the existence of a “Nordic model”. The chapters give a broad overview of crisis communication in the Nordic countries during the first year of the Covid-19 pandemic by combining organisational and societal theoretical perspectives and encompassing crisis response from governments, public health authorities, lobbyists, corporations, news media, and citizens. The results show several similarities, such as political and governmental responses highlighting solidarity and the need for exceptional measures, as expressed in press conferences, social media posts, information campaigns, and speeches. The media coverage relied on experts and was mainly informative, with few critical investigations during the initial phases. Moreover, surveys and interviews show the importance of news media for citizens’ coping strategies, but also that citizens mostly trusted both politicians and health authorities during the crisis. This book is of interest to all who are looking to understand societal crisis management on a comprehensive level. The volume contains chapters from leading experts from all the Nordic countries and is edited by a team with complementary expertise on crisis communication, political communication, and journalism, consisting of Bengt Johansson, Øyvind Ihlen, Jenny Lindholm, and Mark Blach-Ørsten. Publishe

    Rethinking Readiness and Resilience: An Exploration of the Spiritual Domain on the Other Comprehensive Airman Fitness Framework Domains for Active-Duty Airmen Serving on Air National Guard Installations – A Qualitative Case Study

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    The purpose of this qualitative descriptive case study was to fill critical gaps in literature by understanding an Airman’s view of spiritual fitness, how spiritual fitness contributes to overall fitness, resilience and readiness, and what steps could be taken to improve Air Force spiritual fitness programming. To ensure equitability, Airmen were split into two different tenure-based groups: Active-duty Low Tenure (ADlt) and Active-duty high tenure (ADht). ADlt had an average age of 27.9 years old (SD=3.2), were 90% male, 60% White/Caucasian, and 30% Catholic/30% Christian. ADht had an average age of 34.7 years old (SD=2.0), were 100% male, 80% White/Caucasian, and 50% Christian. Spiritual fitness was reported by the following percentages of Airmen as contributing to the other three CAF domains: Physical domain – 70%, Mental domain – 100%, and Social domain – 95%. Readiness (95%) and resilience (90%) were also outlined as heavily impacted by an Airman’s spiritual fitness. Spirituality encouraged Airmen’s physical fitness through the idea that a healthy body is part of a spiritual discipline, the idea that it is part of Air Force requirements, and the idea of treating the body as a temple. Spirituality contributes to mental fitness through trusting in God\u27s promises, providing perspective to reassess situations, and providing a focal point to maintain positivity. Spirituality contributes to social fitness by serving as a source of people who provide a team mentality, support and accountability. Spirituality contributes to readiness by providing strength/excellence in Christ, inner purpose to motivate to action, and living by the Word. Spirituality contributes to resilience by individual perspective and finding strength in God’s word. Airmen suggested four areas to strengthen core values, which were truly embracing and inhabiting the Air Force core values, holding a stronger faith in God, and reinforcing the need to live up to personal core values. Airmen noted that cognitive reframing and prioritization, and doing good for others as methods to aid in strengthen a healthy perspective. In strengthening perseverance, Airmen outline four key areas, which were intentional self-reflective moments, greater religious accommodation/more chaplain interaction, greater devotion to developing spiritual fitness, and inspiring a stronger reliance on others/accountability. Recommendations to strengthen purpose were loving other people and creating shared values and goals, bringing a unique perspective to everyday situations, and continually working on one’s spirituality. Lastly, four key areas were uncovered in strengthening spiritual fitness, which will also impact an Airman’s ability to meet the demand of their assigned missions. Those areas were: Listening to and understanding other Airmen and their plights, educating the force more on spiritual fitness and the spiritual domain of CAF, spending more time in working on self-development in spiritual fitness, and authenticity. This study directly contributes to the understanding of spiritual fitness within the United States Air Force. Contributions to the field of Industrial and Organizational Psychology can be seen in this study through real-world experiences of Airmen and how spiritual fitness guides their readiness, resilience, physical fitness, mental fitness, and social fitness. This study also contributes to faith-based interventions and spiritual applications for non-military organizations

    Teaching the actuality of revolution: Aesthetics, unlearning, and the sensations of struggle

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    Exploring the nexus between aesthetics, pedagogy, and politics illustrates the central role education plays in reproducing injustice and inhibiting confidence in revolutionary struggle. Demonstrating how capitalism and its attendant forms of oppression are not merely cognitive but perceptual, Derek R. Ford proposes that revolutionary education demands the production of aesthetic experiences through which we sense the possibility and actuality of alternative worlds. To create such encounters, Ford develops a praxis of teaching and a pedagogy of unlearning that, in our current conjuncture, creates conditions for encountering what Jennifer Ponce de León calls “an other aesthetics.” Mapping contemporary capital as a perceptual ecology of structures, social relations, beliefs, and feelings, Teaching the Actuality of Revolution: Aesthetics, Unlearning, and the Sensations of Struggle provides an extensive new set of concepts, practices, and readings for revolutionaries to better plan, enact, reflect on, and refine our organizing efforts

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution

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    Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners

    The opinions of science and mathematics teachers about beliefs, practices, and implementation of meaningful learning in Israel. A case study of Arab middle school(s)

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    Wydział Studiów EdukacyjnychWiele badań pokazuje, że przekonania nauczycieli dotyczące nauczania i uczenia się silnie oddziałują na ich praktykę zawodową. Celem tej pracy było zbadanie przekonań i praktyk nauczycieli przedmiotów ścisłych i matematyki w arabskich szkołach średnich w Izraelu w obliczy wdrażania nowej reformy edukacyjnej w tym kraju, silnie osadzonej na koncepcji meaningful learning. Zgodnie z tą koncepcją, uczniowie powinni być aktywni i zaangażowani w proces rozwiązywania problemów, którego rdzeniem jest szeroko ujmowany dialog pomiędzy uczestnikami procesu uczenia się. W badaniach wykorzystano strategię badań jakościowych. Prowadzono obserwacje w klasie, częściowo ustrukturyzowane wywiady oraz analizy dokumentów (m.in. planów lekcji, testów, arkuszy roboczych) i notatek terenowych. Uczestnikami badania było dwudziestu nauczycieli z trzech szkół średnich w społeczeństwie arabskim. Uzyskane dane pozwoliły zarysować obraz przekonań tych nauczycieli na temat meaningful learning oraz zidentyfikować sytuacje, które nauczyciele postrzegają jako realizację tej koncepcji. Praca kończy się rekomendacjami dotyczącymi dalszych etapów wdrażania reformy edukacji w Izraelu.The introduction of a new reform potentially challenges teachers’ beliefs and practices about teaching. This case study explores these challenges in the context of a new reform in Israel, where major educational reform has been undertaken. A considerable body of research, alternatively, advocates that teachers’ beliefs about teaching and learning affect their teaching practices and many aspects of their professional work. These beliefs and practices influence many factors on the contextual and teacher levels. Thus, this study aimed to investigate and understand Arab middle school science and mathematics teachers’ beliefs, practices, and implementation of meaningful learning in Israel. The resulting data served to construct a background picture regarding teachers’ beliefs on meaningful learning, classroom practices, and identifying situations that teachers perceived as the implementation of meaningful learning. The study found also that curricular demands, teacher perceptions of their students, pressures of time, assessment, crowded classrooms, lack of resources, workload, and inadequate teacher understanding of the components of meaningful learning inhibited student- centered instruction. Thus, along with the reformation of teachers, there should also be a reformation in the context of the learning atmosphere and infrastructures in tune with the new reform’s intentions

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP
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