4,100 research outputs found

    Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning

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    No explorer reached a pole without a map, no chef served a meal without tasting, and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4]. By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM). Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7]. Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4). “Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8]. VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Undergraduate Catalog of Studies, 2022-2023

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    Analyzing smart city development through an evolutionary approach

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    Cities have always been places where agglomeration economies attained their highest yields, producing cultural, economic, and social benefits being the main locus of entrepreneurship and innovation. However, rapid urbanization created many problems such as inequality, pollution, diseases, insecurity and so on, that end up restraining the dynamic of value creation in 21st century. This is challenging ‘industrial cities’ to rethink and to reshape their structures to overcome these issues. In this sense, the ‘smart city’ model has gained prominence in urban development. Many cities from different countries are designing strategies and implementing them through initiatives and projects towards smart city development. It is noted that these experiences are idiosyncratic, because cities are inherently different and have different issues that must be solved in a particular way. The first question that arise is: how to make a city smarter? Despite the contrasting view of frameworks and their multitude of dimensions and approaches, the literature points out that cities must have specific elements to induce innovation processes through digital solutions and the collaboration between stakeholders in order to address local challenges and, thus, increase local competitiveness and quality of life. However, it does not an easy task and involves a set of stakeholders that may not prone to collaborate and to promote smart city development. In fact, the main difficulties of a strategy emerge during the implementation phase, because many of the challenges for cities to become or to be smart exceed the scope and capabilities of their current organizations, institutional arrangements, and governance structures. Indeed, the lack of appropriate structural and organizational formations does not foster the involvement of local stakeholders and makes it difficult to organize and coordinate the different activities needed to achieve sustainable urban development. Then, the second question that emerge is: what kind of organization can foster smart city development? In this sense, the literature sheds light on the need to discuss alternative governance models to overcome those challenges by combining political and social support with strategic planning and creative thinking in order to deal with smart city complexity. Some authors point out that it is necessary to create a dedicated organization to lead the collaboration between those stakeholders in this process of urban transformation. From that discussion, what seems clear is that the analysis of the development process of a smart city in its different dimensions and units of analysis demands a theoretical background that enables academia and industry to capture the dynamics of evolution and, therefore, understand how smart cities change over time. It is necessary to incorporate theories and concepts that consider not only the notion of space-time, but especially that delve into how the relationships between the elements of the ecosystem interact and complement each other. Then, our third question is: how to analyze this dynamic, context-dependent, long-term process of urban development so that a city becomes smarter? Some authors point out the possibility of a theoretical approximation between evolutionary approach and smart city literature affirming that due to complexity of smart city development, smart city planning is shaped by evolutionary processes too. Thus, it is necessary to incorporate the notion of evolution in the processes of urban transformation and that they occur in a certain geographical location being conditioned by local contextual factors. As aforementioned, cities are inherently different and have different issues. Thus, to measure the existing level of development is crucial to foresee the right steps to enhance urban smartness. Smartness should be seen as a continuum, in which stakeholders may implement initiatives to create, improve or alter smart city elements across those different city dimensions. The notion of smartness may help cities to understand how this process of urban transformation affects their dimensions and their performance, and, consequently, analyze what should be done to accelerate it. In this sense, it is important that cities assess their current stage of development. The assessment of smart city development may bring multiple benefits for different stakeholders. It enables the identification of city strengths and weaknesses, comparison among cities, monitoring and racking projects implementation, increasing transparency on investments, enabling to make policies based on evidences, enhancing citizen awareness, and so on. The fourth question that emerges is: how to measure the smartness of a city? In terms of smart city assessment, many scholars, organizations and companies have developed indexes, toolkits, and benchmarking to measure and rank smart cities. These assessments schemes may provide a good overview about the city’s characteristics and both its strengths and weaknesses, as well as being used to showcase its competitive position. However, most of them neglect the multiple interrelated processes related to the smart city development by adopting a summative approach. This approach presents some limitations that do not properly capture the smartness of a city. Considering that, the objectives of this study are to (1) identify the dimensions and the driving elements to make a city smarter, (2) to understand the role of smart city dedicated organization on smart city governance, (3) to propose an evolutionary framework for the analysis of smart city development and (4) to create a model to measure the smartness of a city using different methods, considering the type of data, its manipulation and analysis. To achieve these objectives, the research focused on understanding the concept of smart cities and that their development depends on a non-linear process, which should make some steps like designing strategies, implementing them through projects to solve the current urban issues. For that, the establishment of a governance structure is crucial to smart city development succeed since collaboration is needed to create complex solutions and the legitimacy of a vision. Therefore, a dedicated organization is important to articulate the stakeholders and boost the development of projects and initiatives. However, just collaborative networks will not solve the urban issues per se. It should be identified how to create, improve, change the elements from the hard and soft dimensions of a city (i.e., economy, social, environment). It is important to highlight that a smart strategy, project, or solution to be smart in fact must consider that these dimensions are integrated and then affect and are affected by each other. In addition, it is needed to incorporate in this urban planning and management discourse the notion of time and space, because past events can affect the current stage of development and the present decisions will impact future of the city. As an evolutionary process, each city will certainly follow different paths, because the dynamics of its development depends on how the (eco)system is configured and which is his level of smartness. It also should be considered the history of city and its context to define more assertive strategies and projects. Thus, for the analysis of smart city development, it is necessary to apply an evolutionary framework capable to link micro-behavior to macro- processes that occur in each territory over time. By considering smart city development as a process that changes the urban realm and the behavior of stakeholders over time, there is a need to measure how this is in fact helping (or not) the urban performance and, how cities can achieve a sustainable development in a more efficient way. In this study, it focusses on the measurement of smartness of an urban innovation ecosystem, because it provides an overview of the current stage of development and the relationship among the elements and dimensions, which could guide policymakers and the society on what invest, how to design a comprehensive strategy and when to implement it.As cidades sempre foram locais onde as economias de aglomeração atingiram seus maiores rendimentos, produzindo benefícios culturais, econômicos e sociais sendo o principal locus de empreendedorismo e inovação. No entanto, a rápida urbanização criou muitos problemas como desigualdade, poluição, doenças, insegurança e assim por diante, que acabam por restringir a dinâmica de criação de valor no século XXI. Isso está desafiando as "cidades industriais" a repensar e remodelar suas estruturas para superar esses problemas. Nesse sentido, o modelo de 'cidade inteligente' tem ganhado destaque no desenvolvimento urbano. Muitas cidades de diferentes países estão desenhando estratégias e implementando-as por meio de iniciativas e projetos para o desenvolvimento de cidades inteligentes. Nota-se que essas experiências são idiossincráticas, pois as cidades são inerentemente diferentes e possuem questões diversas que devem ser resolvidas de forma particular. A primeira questão que surge é: como tornar uma cidade mais inteligente? Apesar da visão contrastante dos frameworks e de sua multiplicidade de dimensões e abordagens, a literatura aponta que as cidades devem ter elementos específicos para induzir processos de inovação por meio de soluções digitais e da colaboração entre stakeholders para enfrentar os desafios locais e, assim, aumentar a competitividade local e qualidade de vida. No entanto, não é uma tarefa fácil e envolve um conjunto de stakeholders que podem não estar dispostos a colaborar e promover o desenvolvimento de cidades inteligentes. De fato, as principais dificuldades de uma estratégia surgem durante a fase de implementação, pois muitos dos desafios para as cidades se tornarem ou serem inteligentes excedem o escopo e as capacidades de suas atuais organizações, arranjos institucionais e estruturas de governança. De fato, as principais dificuldades de uma estratégia surgem durante a fase de implementação, pois muitos dos desafios para as cidades se tornarem ou serem inteligentes excedem o escopo e as capacidades de suas atuais organizações, arranjos institucionais e estruturas de governança. Com efeito, a falta de formações estruturais e organizativas adequadas não favorece o envolvimento dos atores locais e dificulta a organização e coordenação das diferentes atividades necessárias para alcançar um desenvolvimento urbano sustentável. Então, a segunda questão que surge é: que tipo de organização pode fomentar o desenvolvimento de cidades inteligentes? Nesse sentido, a literatura lança luz sobre a necessidade de discutir modelos alternativos de governança para superar esses desafios, combinando apoio político e social com planejamento estratégico e pensamento criativo para lidar com a complexidade da cidade inteligente. Alguns autores apontam que é necessário criar uma organização dedicada a liderar a colaboração entre as partes interessadas neste processo de transformação urbana. A partir dessa discussão, o que parece claro é que a análise do processo de desenvolvimento de uma smart city em suas diferentes dimensões e unidades de análise demanda um embasamento teórico que permita à academia e à indústria captar a dinâmica da evolução e, assim, compreender como as smart cities mudam com o tempo. É preciso incorporar teorias e conceitos que considerem não apenas a noção de espaço-tempo, mas principalmente que se aprofundem em como as relações entre os elementos do ecossistema interagem e se complementam. Então, nossa terceira pergunta é: como analisar esse processo de desenvolvimento urbano dinâmico, dependente do contexto e de longo prazo para que uma cidade se torne mais inteligente? Alguns autores apontam a possibilidade de uma aproximação teórica entre a abordagem evolutiva e a literatura de cidades inteligentes, afirmando que devido à complexidade do desenvolvimento de cidades inteligentes, o planejamento de cidades inteligentes também é moldado por processos evolutivos. Assim, é necessário incorporar a noção de evolução nos processos de transformação urbana e que eles ocorram em uma determinada localização geográfica sendo condicionados por fatores contextuais locais. Como mencionado anteriormente, as cidades são inerentemente diferentes e têm problemas diferentes. Assim, medir o nível de desenvolvimento existente é crucial para prever os passos certos para aumentar a inteligência urbana. A inteligência deve ser vista como um continuum, no qual as partes interessadas podem implementar iniciativas para criar, melhorar ou alterar os elementos da cidade inteligente nessas diferentes dimensões da cidade. A noção de smartness pode ajudar as cidades a entender como esse processo de transformação urbana afeta suas dimensões e seu desempenho e, consequentemente, analisar o que deve ser feito para acelerá- lo. Nesse sentido, é importante que as cidades avaliem seu atual estágio de desenvolvimento. A avaliação do desenvolvimento de cidades inteligentes pode trazer múltiplos benefícios para diferentes partes interessadas. Permite identificar os pontos fortes e fracos da cidade, comparar cidades, monitorar e acompanhar a implementação de projetos, aumentar a transparência nos investimentos, possibilitar a formulação de políticas com base em evidências, aumentar a conscientização do cidadão e assim por diante. A quarta questão que surge é: como medir a inteligência de uma cidade? Em termos de avaliação de cidades inteligentes, muitos acadêmicos, organizações e empresas desenvolveram índices, kits de ferramentas e benchmarking para medir e classificar cidades inteligentes. Esses esquemas de avaliação podem fornecer uma boa visão geral sobre as características da cidade e seus pontos fortes e fracos, além de serem usados para mostrar sua posição competitiva. No entanto, a maioria deles negligencia os múltiplos processos inter-relacionados relacionados ao desenvolvimento da cidade inteligente, adotando uma abordagem somativa. Essa abordagem apresenta algumas limitações que não capturam adequadamente a inteligência de uma cidade. Considerando isso, os objetivos deste estudo são (1) identificar as dimensões e os elementos impulsionadores para tornar uma cidade mais inteligente, (2) entender o papel da organização dedicada a cidades inteligentes na governança de cidades inteligentes, (3) propor uma abordagem evolutiva framework para a análise do desenvolvimento de cidades inteligentes e (4) criar um modelo para medir a inteligência de uma cidade usando diferentes métodos, considerando o tipo de dados, sua manipulação e análise. Para atingir esses objetivos, a pesquisa se concentrou em entender o conceito de cidades inteligentes e que seu desenvolvimento depende de um processo não linear, que deve seguir algumas etapas como desenhar estratégias, implementá-las por meio de projetos para resolver os problemas urbanos atuais. Para isso, o estabelecimento de uma estrutura de governança é crucial para o sucesso do desenvolvimento de cidades inteligentes, pois é necessária a colaboração para criar soluções complexas e a legitimidade de uma visão. Portanto, uma organização dedicada é importante para articular as partes interessadas e impulsionar o desenvolvimento de projetos e iniciativas. No entanto, apenas redes colaborativas não resolverão os problemas urbanos per se. Deve ser identificado como criar, melhorar, mudar os elementos das dimensões hard e soft de uma cidade (ou seja, econômica, social, ambiental). É importante destacar que uma estratégia, projeto ou solução inteligente para ser inteligente de fato deve considerar que essas dimensões estão integradas e então afetam e são afetadas umas pelas outras. Além disso, é necessário incorporar neste discurso de planejamento e gestão urbana a noção de tempo e espaço, pois eventos passados podem afetar o atual estágio de desenvolvimento e as decisões presentes impactarão o futuro da cidade. Como processo evolutivo, cada cidade certamente seguirá caminhos diferentes, pois a dinâmica de seu desenvolvimento depende de como o (eco)sistema se configura e qual é o seu nível de inteligência. Também deve ser considerada a história da cidade e seu contexto para definir estratégias e projetos mais assertivos. Assim, para a análise do desenvolvimento de cidades inteligentes, é necessário aplicar um quadro evolutivo capaz de vincular o microcomportamento aos macroprocessos que ocorrem em cada território ao longo do tempo. Ao considerar o desenvolvimento de cidades inteligentes como um processo que muda o ambiente urbana e o comportamento dos stakeholders ao longo do tempo, há a necessidade de medir como isso está de fato ajudando (ou não) o desempenho urbano e como as cidades podem alcançar um desenvolvimento sustentável em uma forma mais eficiente. Este artigo tem como foco a mensuração da inteligência de um ecossistema de inovação urbana, pois fornece uma visão geral do estágio atual de desenvolvimento e a relação entre os elementos e dimensões, o que poderá orientar os formuladores de políticas e a sociedade sobre o que investir, como projetar uma estratégia abrangente e quando implementá-la

    The Developer's Dilemma

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    This book explores this developer’s dilemma or ‘Kuznetsian tension’ between structural transformation and income inequality. Developing countries are seeking economic development—that is, structural transformation—which is inclusive in the sense that it is broad-based and raises the income of all, especially the poor. Thus, inclusive economic growth requires steady, or even falling, income inequality if it is to maximize the growth of incomes at the lower end of the distribution. Yet, this is at odds with Simon Kuznets hypothesis that economic development tends to put upward pressure on income inequality, at least initially and in the absence of countervailing policies. The book asks: what are the types or ‘varieties’ of structural transformation that have been experienced in developing countries? What inequality dynamics are associated with each variety of structural transformation? And what policies have been utilized to manage trade-offs between structural transformation, income inequality, and inclusive growth? The book answers these questions using a comparative case study approach, contrasting nine developing countries while employing a common analytical framework and a set of common datasets across the case studies. The intended intellectual contribution of the book is to provide a comparative analysis of the relationship between structural transformation, income inequality, and inclusive growth; to do so empirically at a regional and national level; and to draw conclusions from the cases on the varieties of structural transformation, their inequality dynamics, and the policies that have been employed to mediate the developer’s dilemma
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