661 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    The European Experience: A Multi-Perspective History of Modern Europe, 1500–2000

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    The European Experience brings together the expertise of nearly a hundred historians from eight European universities to internationalise and diversify the study of modern European history, exploring a grand sweep of time from 1500 to 2000. Offering a valuable corrective to the Anglocentric narratives of previous English-language textbooks, scholars from all over Europe have pooled their knowledge on comparative themes such as identities, cultural encounters, power and citizenship, and economic development to reflect the complexity and heterogeneous nature of the European experience. Rather than another grand narrative, the international author teams offer a multifaceted and rich perspective on the history of the continent of the past 500 years. Each major theme is dissected through three chronological sub-chapters, revealing how major social, political and historical trends manifested themselves in different European settings during the early modern (1500–1800), modern (1800–1900) and contemporary period (1900–2000). This resource is of utmost relevance to today’s history students in the light of ongoing internationalisation strategies for higher education curricula, as it delivers one of the first multi-perspective and truly ‘European’ analyses of the continent’s past. Beyond the provision of historical content, this textbook equips students with the intellectual tools to interrogate prevailing accounts of European history, and enables them to seek out additional perspectives in a bid to further enrich the discipline

    Green power generation and the evolution of the Chinese electricity industry, 1880 to the present

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    Climate change urgently calls for fundamental changes in the way we generate electricity. As the world’s largest electricity generator and the biggest greenhouse gas emitter, China has pledged to decarbonise its power system. The success or failure of its efforts to rapidly accelerate the deployment of renewables will have immense implications for the global green transition. How might China meet its energy needs using green energies? This is the question this thesis takes up. This thesis uses mixed methods to address change through time as means to understand where China is now in terms of energy and where it might go next. This thesis begins by applying Hughes’s system approach to investigate the evolution of China’s power system from its origins in the 1880s to the current green transition. The findings show that the Chinese power system originated in wars, was built by the Western-educated elites, embedded with the socialist-style gained from Soviet assistance, and directed by the central state’s political and economic principles. As a late developer, the case of China indicates the importance of human capital and that political, economic and educational openness are necessary conditions for late development. The thesis then focuses on the subnational political economy of the power system’s green transition through an in-depth case study. The findings of a neo-Gramscian analysis demonstrate the dynamic processes and evolving power relations of the local electricity industry’s green transition. The results point out that the rivalling coalitions of distinct economic interests – the established coalfired power historical bloc and the young renewable energy firms – were particularly central to the process. The final themed chapter examines whether it paid to adopt renewable energies in Chinese electricity generation firms from 2005 to 2017. The quantitative results show that adopting renewable energy positively impacts corporate profitability. Profitability is more stable and increases faster in firms with a higher share of renewable energies. Qualitative investigations reveal that the state-owned generators now strive for profits rather than scale, and private generators prioritise innovation and political prestige over profitability

    A Qualitative Analysis of Corporate Responsibility for the Education of U.S. Citizens

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    Educated and trained workers represent the primary critical success factor needed for all organizations to achieve their mission. Organizations depend on a constant flow of educated applicants competing for their jobs at any given time. Traditionally, public, private, and charter schools prepared U.S. citizens for college, trade schools, military, or university, enabling them to then compete successfully for jobs of the era. Today, a myriad of problems face these schools, including disruptive change, uninvolved parents, lack for funding, teacher unions, politics, school overcrowding, COVID-19, outdated training methods, security, race issues, and more. The result is that this education model is in decline and the flow of skilled workers into companies is affecting the United States, which risks losing its ability to compete locally and globally. Consensus that transcends party politics, religious infighting, and greedy decision-making must be reached in time to analyze this big-picture problem. The United States has reached a strategic inflection point and must respond to this disruptive change by developing creative, innovative, and state-of-the-art solutions to this problem, or she may not fulfill God’s will for this country. Companies strive to reach critical mass where they are self-sustaining, but this cannot be done without a change in how people are educated in the United States, which may require business and education to collaborate to reach the same goals, combining education and opportunity. This qualitative case study examined the problem that organizational leaders in the United States face, and specifically the challenges they encounter when strategically planning initiatives that will ensure a pool of educated, skilled, and talented workers available to their organizations now and in the future. Semi-structured interviews with the working population in Eastern Tennessee provided insights to this problem facing organizations across the spectrum

    Strategic Alliances in The Sharing Economy

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    This thesis aims to explore how sharing economy firms can improve profitability while remaining asset-light. Thus, we explored the possibility of forming strategic alliances with traditional firms. Currently, in academic literature, there is a lack of research on strategic alliances in the sharing economy. This emphasizes the need for a comprehensive study. Our research identified 42 strategic alliances between sharing economy firms and traditional firms. Moreover, the research revealed that joint distribution alliances were the most common type of alliance in the sharing economy. Thus, the topic was explored through an explanatory case study of the joint distribution alliance between Uber and Hertz, formed in America in 2016. This led to the following research question: “Can sharing economy firms and traditional firms create joint value?” Transaction cost theory was deployed to analyze the impact of the alliance on the firms’ profitability. More specifically, deploying transaction cost theory to determine whether the alliance impacted Uber’s revenue, costs, and the service providers’ transaction cost. Similarly, to evaluate whether the alliance impacted Hertz‘s asset utilization, revenue, and costs. The findings revealed that Uber’s mobility segment experienced significant growth and improved profitability during the first three years of the alliance. Similarly, the American revenue and total revenue increased in this period. In addition, Uber’s total costs increased, but the rate of growth decreased in the beginning of the alliance. However, there was a reduction in marketing incentives for the mobility segment. Nevertheless, the findings revealed that the alliance contributed to a significant financial gain for Uber’s service providers due to reduced transaction costs. Moreover, the findings also revealed that Hertz experienced an increase in American vehicle utilization following the alliance. In addition, there was an increase in revenue for both the American rental car segment and Hertz Global. Nevertheless, the findings demonstrate a slight increase in the costs for the rental car segment in America. Similarly, there was an increase in total costs. However, the rate of growth of these costs decreased after the first year of the alliance. In addition, depreciation of revenue earning vehicles and lease charges significantly decreased following the alliance. The findings also illustrated that both firms should have gained significant revenue from the alliance. Given that our findings align with transaction cost theory, demonstrating reductions in transaction costs and increased revenue and asset utilization, we conclude that sharing economy firms and traditional firms can create joint value through joint distribution alliances

    The agency and geography of socio-technical transitions: the case of urban transport innovations

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    The objective of this cumulative thesis is to gain deeper insights into the interplay of agency and structure through the empirical example of emerging technologies in the context of Industry 4.0. To achieve this goal, it enriches the theoretical background from evolutionary economic geography with insights from transition studies and management studies. Empirically, the analysis focuses on novelty creation toward intelligent transport systems in an urban environment. This encompasses software solutions such as big data platforms for traffic management, the Internet of Things to create a network of various objects and subjects within the city, or the development of autonomous vehicles. This thesis formulates four overarching research purposes: (1) comprehending socio-technical transitions during Industry 4.0 from an agency-based perspective; (2) understanding how agency facilitates or hinders innovation development; (3) identifying the impact of multi-scalar and cross-sectoral relations; and (4) integrating different theoretical approaches to gain a holistic understanding of the empirical domain. The thesis adopts a qualitative research design with a philosophical grounding in critical realism, drawing on semi-structured expert interviews, literature reviews, and document and network analysis. The main contribution of this thesis rests on four distinct research papers. A systematic literature review sets the conceptual basis for the analysis, identifying future research avenues based on the existing research body. The first case study analyzes the development of an app-based solution for managing urban logistics in Barcelona from a multi-level perspective. The other two case studies investigate the evolution of advanced air mobility in Germany and the city of Hamburg

    Enhancing Financial Hedging Strategies through Modern Computational Methods

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    El "pairs trading" és una estratègia de fons de cobertura que es beneficia dels moviments relatius entre els preus de dos actius financers cointegrats, aprofitant les divergències temporals respecte a l'equilibri a llarg termini. La cointegració es manifesta quan els preus dels actius tenen tendència a moure's conjuntament a causa d'una relació econòmica subjacent, com ara dues empreses que operen dins de la mateixa cadena de valor. Identificant aquests parells, els inversors poden aprofitar les discrepàncies de preus temporals, amb l'expectativa que convergeixin finalment al seu valor d’equilibri. No obstant això, les cointegracions entre parells d'actius poden debilitar-se amb el temps, i una possible ruptura de la cointegració comporta el risc de pèrdues si no s'identifica i es gestiona de manera proactiva. Aquest treball de fi de grau es centra en una exploració quantitativa dels principis fonamentals del "pairs trading" al mercat d'accions, alhora que desenvolupa un algoritme d’inversió i de gestió de risc aplicable a casos reals. L'objectiu de l'algoritme és identificar de manera eficaç parells d'accions cointegrats, executar operacions i monitorar activament la cointegració per detectar possibles riscos de futura ruptura. Per substanciar les troballes i arribar a conclusions exhaustives, s'ha estudiat un extens dataset que inclou les dades de mercat de 117 accions des de novembre de 2020 fins a gener de 2022. Totes les troballes presentades en la memòria, juntament amb les figures i taules adjuntes, s'han obtingut mitjançant l'anàlisi quantitativa en Python d'aquest conjunt de dades. L’informe comença amb una visió general de les estratègies de fons de cobertura i aprofundeix en els fonaments del "pairs trading", posant èmfasi en la importància de la cointegració entre actius i els reptes que es presenten actualment. Un cop s'ha discutit el marc teòric, s'inicia la recerca de resultats quantitatius, on es pre-processen les dades per tal de trobar actius cointegrats, dissenyar l’estratègia quantitativa d’inversió de forma neutral a mercat i presentar resultats de "backtests" per avaluar el rendiment i la rendibilitat de l’algoritme. Més endavant, s'utilitzen tècniques avançades d'aprenentatge automàtic per monitorar la cointegració i abordar el risc de ruptura. Les conclusions d'aquest treball de fi de grau revelen resultats significatius sobre l'efectivitat de l'estratègia de "pairs trading" i l'ús de tècniques d'aprenentatge automàtic en aquest context. S'ha determinat que els parells d'actius cointegrats mostren un rendiment notablement superior als parells no cointegrats, amb un rendiment anual mitjà de l’algoritme del 57,68% durant el 2020, superant àmpliament els referents del mercat. A més, s'ha demostrat que la capacitat de predir amb precisió l'evolució de la cointegració és crucial per evitar pèrdues financeres. Mitjançant l'ús d'un model de "random forest", s'ha aconseguit identificar amb una gran precisió els parells que perden cointegració amb dues setmanes d'antelació. Aquests resultats demostren l'enorme potencial de l'aprenentatge automàtic en les estratègies financeres quantitatives per gestionar el risc i prendre decisions informadesEl "pairs trading" es una estrategia de cobertura que tiene como objetivo capitalizar los movimientos relativos de los precios de dos activos financieros cointegrados, aprovechando las divergencias temporales respecto al equilibrio a largo plazo. La cointegración se observa cuando los precios de los activos tienden a moverse juntos debido a una relación económica subyacente, como dos empresas que operan en la misma cadena de valor. Identificando estos pares, los inversores pueden aprovechar las discrepancias de precios, con la expectativa de que converjan finalmente a su valor de equilibrio. Sin embargo, las cointegraciones entre pares de activos pueden debilitarse con el tiempo, y una posible ruptura de la cointegración conlleva el riesgo de pérdidas si no se identifica y gestiona de manera proactiva. Este trabajo de fin de grado se centra en una exploración cuantitativa de los principios fundamentales del "pairs trading" en el mercado de acciones, al tiempo que desarrolla un algoritmo de inversión y gestión de riesgos aplicable a casos reales. El objetivo del algoritmo es identificar de manera eficaz pares de acciones cointegrados, ejecutar operaciones y monitorear activamente la cointegración para detectar posibles riesgos de futura ruptura. Para respaldar los hallazgos y llegar a conclusiones exhaustivas, se ha estudiado un extenso dataset que incluye los precios de mercado de 117 acciones desde noviembre de 2020 hasta enero de 2022. Todos los hallazgos presentados en el informe, junto con las figuras y tablas adjuntas, se han obtenido mediante el análisis cuantitativo en Python de este conjunto de datos. El informe comienza con una visión general de las estrategias de cobertura y profundiza en los fundamentos del "pairs trading", poniendo énfasis en la importancia de la cointegración entre activos y los desafíos que se presentan en la actualidad. Una vez discutido el marco teórico, se inicia la búsqueda de resultados cuantitativos, donde se preprocesan los datos para encontrar activos cointegrados, diseñar la estrategia cuantitativa de inversión de forma neutral al mercado y presentar resultados de "backtests" para evaluar el rendimiento y la rentabilidad del algoritmo. Más adelante, se utilizan técnicas avanzadas de aprendizaje automático para monitorear la cointegración y abordar el riesgo de ruptura. Las conclusiones de este trabajo de fin de grado revelan resultados significativos sobre la efectividad de la estrategia de "pairs trading" y el uso de técnicas de aprendizaje automático en este contexto. Se ha determinado que los pares de activos cointegrados muestran un rendimiento notablemente superior a los pares no cointegrados, con un rendimiento anual promedio del algoritmo del 57,68% durante 2020, superando ampliamente los puntos de referencia del mercado. Además, se ha demostrado que la capacidad de predecir con precisión la evolución de la cointegración es crucial para evitar pérdidas financieras. Mediante el uso de un modelo de "random forest", se ha logrado identificar con una gran precisión los pares que pierden cointegración con dos semanas de anticipación. Estos resultados demuestran el enorme potencial del aprendizaje automático en las estrategias financieras cuantitativas para gestionar el riesgo y tomar decisiones informadasPairs trading is a hedge fund strategy that aims to capitalize on the relative price movements of two cointegrated assets by exploiting temporary price divergences from the long-term equilibrium. Cointegration occurs when asset prices tend to move together due to an underlying economic relationship, such as two companies operating within the same value chain. By identifying these pairs, investors can profit from temporary price discrepancies, with the expectation that they will eventually converge to their equilibrium value. However, cointegrations between asset pairs can weaken over time, and a potential breakdown of cointegration poses the risk of losses if not identified and managed proactively. This bachelor’s thesis focuses on a quantitative exploration of the fundamental principles of pairs trading in the stock market while developing an investment and risk management algorithm applicable to real cases. The algorithm's objective is to effectively identify cointegrated stock pairs, execute trades, and actively monitor cointegration to detect potential breakdown risks. To substantiate the findings and reach comprehensive conclusions, an extensive dataset including market data for 117 equity assets from November 2020 to January 2022 has been studied. All the findings presented in the report, along with the accompanying figures and tables, have been obtained through the quantitative analysis in Python of this dataset. The report begins with an overview of hedge fund strategies and delves into the foundations of pairs trading, emphasizing the importance of asset cointegration and the challenges presented in the current context. Once the theoretical framework is discussed, the search for quantitative results begins, where the data is preprocessed to find cointegrated assets, the quantitative investment strategy is designed, and the backtest results are evaluated to assess the performance and profitability of the algorithm. Advanced machine learning techniques are then employed to monitor cointegration and address the risk of breakdown. The conclusions of this undergraduate thesis reveal significant results regarding the effectiveness of pairs trading strategies and the use of machine learning techniques in this context. It has been determined that cointegrated asset pairs exhibit significantly higher performance than non- cointegrated pairs, with an average annual return of 57.68% during 2020, far surpassing market benchmarks. Moreover, it has been demonstrated that accurately predicting the evolution of cointegration is crucial to avoid financial losses. By leveraging a random forest model, pairs losing cointegration have been identified two weeks in advance with a significant accuracy rate. These results showcase the tremendous potential of machine learning in quantitative financial strategies to manage risk and make informed decision

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Experimental Investigation of Silicon Dust Explosions in Pipes

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    Metallurgical silicon and its alloys are essential in several industries, such as solar energy, automotive, aerospace, and steel production. However, silicon dust, generated during silicon production, processing, and handling, poses significant health and safety hazards. Dust extraction systems control these risks by capturing, transporting, and removing dust particles from the work environment, thereby improving air quality for personnel, and reducing the accumulation of dust deposits. This study investigates the hazards associated with dust explosions in dust extraction ducts. It measures explosion pressures inside pipes and observes the length of emitted fireballs. Experiments were conducted using an explosion vessel and pipes of three different internal diameters, with expanders and reducers utilized to create pipe configurations of both diminishing and increasing diameters, simulating a small-scale dust extraction system. The dusts used in the experiments were silicon and silicon-alloy dusts, with dust layers representing nominal dust concentrations ranging from 250 to 5000 g/m³. Piezoelectric pressure transducers recorded pressure development at specific locations inside the pipes, while a high-speed camera captured the fireball length emitted from the pipe ends. Experiments involving single pipes and configurations of two connected pipes revealed a strong correlation between dust layer concentration and fireball length. However, no evidence suggested a relationship between explosion pressure and dust layer concentration. A 25-metre configuration with four connected pipes of varying diameters, with the smallest pipes near the exit, demonstrated that silicon dust, formally classified as St-1 dust, although on the limit to St-2, can result in detonation-like overpressures and shock wave speeds. Further research is needed to determine whether and under which conditions a silicon dust deflagration may transition into a self-sustained detonation.Masteroppgave i energiENERGI399I5MAMN-ENE

    Grain & Noise - Artists in Synthetic Biology Labs: Constructive Disturbances of Art in Science

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    The collaboration between scientists and artists in the form of Artist-in-Lab residencies may not only cause a productive disturbance for a day's work in the laboratory, but also reveal new ways of understanding. Research and science communication company Biofaction has brought together artists and synthetic biologists throughout Europe in a residence program that spans four truly cross-disciplinary collaborations. The contributors to this volume share their reflections of the dynamic frictions that occurred when their artistic and scientific worlds met. These stories, where chemistry labs, tobacco plants, genetically edited bacteria, and new-to-nature enzymes collide with music, photography, film, and visual arts, infuse the ongoing dialogue between art and sciences with grain, noise, and synergies
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