5,630 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

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    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the author’s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    Sputter deposition on composites : interplay between film and substrate properties

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    Electrical and Optical Modeling of Thin-Film Photovoltaic Modules

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    Heutzutage ist durch viele wissenschaftliche Studien nachgewiesen, dass die Erde längst dem Klimawandel unterworfen ist. Daher muss die gesamte Menschheit vereint handeln, um die schlimmsten Katastrophenszenarien zu verhindern. Ein vielversprechender Ansatz - wenn nicht sogar der vielversprechendste überhaupt - um diese angesprochene, größte Herausforderung in der Geschichte der Menschheit zu bewältigen, ist es, den Energiehunger der Menschheit durch die Erzeugung erneuerbarer und unerschöpflicher Energie zu sättigen. Die Photovoltaik (PV)-Technologie ist ein vielversprechender Anwärter, die leistungsstärkste erneuerbare Energiequelle zu stellen, und spielt aufgrund ihrer direkten Umwandlung des Sonnenlichtes und ihrer skalierbaren Anwendbarkeit in Form von großflächigen Solarmodulen bereits jetzt eine große Rolle bei der Erzeugung erneuerbarer Energie. Im PV-Sektor sind Solarmodule aus Siliziumwafern die derzeit vorherrschende Technologie. Neu aufkommende PV-Technologien wie die Dünnschichttechnologie haben jedoch vorteilhafte Eigenschaften wie einen sehr geringen Kohlenstoffdioxid (CO2)-Fußabdruck, eine kurze energetische Amortisierungszeit und das Potenzial für eine kostengünstige monolithische Massenproduktion, obwohl diese derzeit noch nicht final ausgereift ist. Um die Dünnschichttechnologie jedoch gezielt in Richtung einer breiten Marktreife zu entwickeln, sind numerische Simulationen eine wichtige Säule für das wissenschaftliche Verständnis und die technologische Optimierung. Während sich traditionelle Simulationsliteratur häufig mit materialspezifischen Herausforderungen befasst, konzentriert sich diese Arbeit auf industrieorientierte Herausforderungen auf Modulebene, ohne die zugrundeliegenden Materialparameter zu verändern. Um ein allumfassendes, digitales Modell eines Solarmoduls zu erstellen, werden in dieser Arbeit mehrere Simulationsansätze aus verschiedenen physikalischen Bereichen kombiniert. Zur Abbildung elektrischer Effekte, einschließlich der räumlichen Spannungsvariation innerhalb des Moduls, wird eine Finite Elemente Methode (FEM) zur Lösung der räumlich quantisierten Poisson-Gleichung verwendet. Um optische Effekte zu berücksichtigen, wird eine generalisierte Transfermatrix-Methode (TMM) verwendet. Alle Simulationsmethoden sind in dieser Arbeit von Grund auf neu programmiert worden, um eine Verknüpfung aller Simulationsebenen mit dem höchstmöglichen Grad an Anpassung und Verknüpfung zu ermöglichen. Die Simulation und die Korrektheit der Parameter wird durch externe Quanteneffizienz (EQE)-Messungen, experimentelle Reflexionsdaten und gemessene Strom-Spannungs (I-U)-Kennlinien verifiziert. Der Kernpunkt der Vorgehensweise dieser Arbeit ist eine ganzheitliche Simulationsmethodik auf Modulebene. Dies ermöglicht es, die Lücke zwischen der Simulation auf Materialebene über die Berechnung von Laborwirkungsgraden bis hin zur Bestimmung der von zahlreichen Umweltfaktoren beeinflusste Leistung der Module im Freifeld zu überbrücken. Durch diese Verknüpfung von Zellsimulation und Systemdesign ist es lediglich aus Laboreigenschaften möglich, das Freifeldverhalten von Solarmodulen zu prognostizieren. Sogar das Zurückrechnen von experimentellen Messungen zu Materialparameter ist mittels des in dieser Arbeit entwickelten Verfahrens des Reverse Engineering Fittings (REF) möglich. Das in dieser Arbeit entwickelte numerische Verfahren kann für mehrere Anwendungen genutzt werden. Zunächst können durch die Kombination von elektrischen und optischen Simulationen ganzheitliche Top-Down-Verlustanalysen durchgeführt werden. Dies ermöglicht eine wissenschaftliche Einordnung und einen quantitativen Vergleich aller Verlustleistungsmechanismen auf einen Blick, was die zukünftige Forschung und Entwicklung in Richtung von technologischen Schwachstellen von Solarmodulen lenkt. Darüber hinaus ermöglicht die Kombination von Elektrik und Optik die Detektion von Verlusten, die auf dem nichtlinearen Zusammenspiel dieser beiden Ebenen beruhen und auf eine räumliche Spannungsverteilung im Solarmodul zurückzuführen sind. Diese Arbeit verwendet die entwickelten numerischen Modelle ebenfalls für Optimierungsprobleme, die an digitalen Modellen realer Solarmodule durchgeführt werden. Häufig auftretende Fragestellungen bei der Entwicklung von Solarmodulen sind beispielsweise die Schichtdicke des vorderen optisch transparenten, elektrisch leitfähigen Oxids (TCO) oder die Breite von monolithisch verschalteten Zellen. Die Bestimmung des Optimums dieser mehrdimensionalen Abwägungen zwischen optischer Transparenz, elektrischer Leitfähigkeit und geometrisch inaktiver Fläche zwischen den einzelnen Zellen ist ein Hauptmerkmal der Methodik dieser Arbeit. Mittels des FEM-Ansatzes dieser Arbeit ist es möglich, alle gegenseitigen Wechselwirkungen über verschiedene physikalische Ebenen hinweg zu berücksichtigen und ein ganzheitlich optimiertes Moduldesign zu finden. Auch topologisch komplexere Probleme, wie das Finden eines geeigneten Designs für das Metallisierungsgitter, können auf Grundlage der Simulation mittels der Methode der Topologie-Optimierung (TO) gelöst werden. In dieser Arbeit wurde das TO-Verfahren zum ersten Mal für monolithisch integrierte Zellen eingesetzt. Darüber hinaus wurde gezeigt, dass sowohl einfache Optimierungen der TCO-Schichtdicken als auch Topologie-Optimierungen stark von den vorherrschenden Beleuchtungsverhältnissen abhängen. Daher ist eine Optimierung auf den Jahresertrag anstelle des Laborwirkungsgrades für industrienahe Anwendungen wesentlich sinnvoller, da die mittleren Jahreseinstrahlungen deutlich von den Laborbedingungen abweichen. Mit Hilfe dieser Ertragsoptimierung wurde in dieser Arbeit für die Kupfer-Indium-Gallium-Diselenid CuIn1x_{1-x}Gax_xSe2_2 (CIGS)-Technologie ein Leistungsgewinn von über 1 % im Ertrag für einige geografische Standorte und gleichzeitig eine Materialeinsparung für die Metallisierungs- und TCO-Schicht von bis zu 50 % errechnet. Mit Hilfe der numerischen Simulationen dieser Arbeit können alle denkbaren technologischen Verbesserungen auf Modulebene in das Modell eingebracht werden. Auf diese Weise wurde das aktuelle technologische Limit für CIGS-Dünnschicht-Solarmodule berechnet. Unter Verwendung der Randbedingungen der derzeit verfügbaren Materialien, Technologie- und Fertigungstoleranzen und des derzeit besten in der Literatur veröffentlichten CIGS-Materials ergibt sich ein theoretisches Wirkungsgradmaximum von 24 % auf Modulebene. Das derzeit beste veröffentlichte Modul mit den gegebenen Restriktionen weist einen Wirkungsgrad von 19,2 % auf [1]. Verbessert sich der CIGS-Absorber vergleichbar mit jenem von Galliumarsenid (GaAs) im Hinblick auf dessen Rekombinationsrate, ergibt sich ein erhöhtes Wirkungsgradlimit von etwa 28 %. Im Falle eines idealen CIGS-Absorbers ohne intrinsische Rekombinationsverluste wird in dieser Arbeit eine maximale Effizienzobergrenze von 29 % berechnet

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Deep learning-powered vision-based energy management system for next-gen built environment

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    Heating, ventilation and air-conditioning (HVAC) systems provide thermally comfortable spaces for occupants, and their consumption is strongly related to how occupants utilise the building. The over- or under-utilisation of spaces and the increased adoption of flexible working hours lead to unnecessary energy usage in buildings with HVAC systems operated using static or fixed schedules during unoccupied periods. Demand-driven methods can enable HVAC systems to adapt and make timely responses to dynamic changes in occupancy. Approaches central to the implementation of a demand-driven approach are accurate in providing real-time information on occupancy, including the count, localisation and activity levels. While conventional occupancy sensors exist and can provide information on the number and location of occupants, their ability to detect and recognise occupancy activities is limited. This includes the operation of windows and appliances, which can impact the building’s performance. Artificial intelligence (AI) has recently become a critical tool in enhancing the energy performance of buildings and occupant satisfaction and health. Recent studies have shown the capabilities of AI methods, such as computer vision and deep learning in detecting and recognising human activities. The recent emergence of deep learning algorithms has propelled computer vision applications and performance. While several studies used deep learning and computer vision to recognise human motion or activity, there is limited work on integrating these methods with building energy systems. Such methods can be used to obtain accurate and real-time information about the occupants for assisting in the operation of HVAC systems. In this research, a demand-driven deep learning framework was proposed to detect and recognise occupancy behaviour for optimising the operation of building HVAC systems. The computer vision-based deep learning algorithm, convolutional neural network (CNN), was selected to develop the vision-based detector to recognise common occupancy activities such as sitting, standing, walking and opening and closing windows. A dataset consisting of images of occupants in buildings performing different activities was formed to perform the training the model. The trained model was deployed to an AI-powered camera to perform real-time detection within selected case study building spaces, which include university tutorial rooms and offices. Two main types of detectors were developed to show the capabilities of the proposed approach; this includes the occupancy activity detector and the window opening detector. Both detectors were based on the Faster R-CNN with Inception V2 model, which was trained and tested using the same approach. In addition, the influence of different parameters on the performance, such as the training data size, labelling method, and how real-time detection was conducted in different indoor spaces was evaluated. The results have shown that a single response 'people detector’ can accurately understand the number of people within a detected space. The ‘occupancy activity detector’ could provide data towards the prediction of the internal heat emissions of buildings. Furthermore, window detectors were formed to recognise the times when windows are opened, providing insights into the potential ventilation heat losses through this type of ventilation strategy employed in buildings. The information generated by the detector is then outputted as profiles, which are called Deep Learning Influence Profiles (DLIP). Building energy simulation (BES) was used to assess the potential impact of the use of detection and recognition methods on building performance, such as ventilation heat loss and energy demands. The generated DLIPs were inputted into the BES tool. Comparisons with static or scheduled occupancy profiles, currently used in conventional HVAC systems and building energy modelling were made. The results showed that the over- or under-estimation of the occupancy heat gains could lead to inaccurate heating and cooling energy predictions. The deep learning detection method showed that the occupancy heat gains could be represented more accurately compared to static office occupancy profiles. A difference of up to 55% was observed between occupancy DLIP and static heat gain profile. Similarly, the window detection method enabled accurate recognition of the opening and closing of windows and the prediction of ventilation heat losses. BES was conducted for various scenario-based cases that represented typical and/or extreme situations that would occur within selected case study buildings. Results showed that the detection methods could be useful for modulating heating and cooling systems to minimise building energy losses while providing adequate indoor air quality and thermal conditions. Based on the developed individual detectors, combined detectors were formed and also assessed during experimental tests and analysis using BES. The vision-based technique’s integration with the building control system was discussed. A heat gain prediction and optimisation strategy were proposed along with a hybrid controller that optimises energy use and thermal comfort. This should be further developed in future works and assessed in real building installations. This work also discussed the limitations and practical challenges of implementing the proposed technology. Initial results of survey-based questionnaires highlighted the importance of informing occupants about the framework approach and how DLIPs were formed. In all, preference is towards a less intrusive and effective approach that could meet the needs of optimising building energy loads for the next-gen built environment

    Quality of explosively welded steel plates using demex explosive

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    Заваривање експлозивом се често користи када конвенционалне методе заваривања не могу да обезбеде заварени спој два различита материјала, али и када треба заварити неку специфичну геометрију или велике површине металних плоча. Остваривање споја код заваривања експлозивом се заснива на динамичком дејству великог притиска створеног екплозијом. У ту сврху најчешће се користе индустријски експлозиви ниских параметара детонације, а један од њих је DEMEX, произвођача TRAYAL, из Србије. У овом истраживању DEMEX је примењен за заваривање плоча две различите врсте челика. Пре експерименталног поступка заваривања одабраних металних плоча, експлозив добијен од произвођача је подвргнут улазној контроли квалитета: мерењу његове насипне густине и брзине детонације, коришћењем оптичких сонди и фотодетектора повезаног са електронским бројачем. Експериментална поставка за заваривање била је следећа: експлозив DEMEX у прашкастом стању нанесен је у равномерном слоју преко горње челичне плоче, која је хоризонтално постављена преко доње плоче од друге врсте челика, у паралелном положају, са малим дрвеним дистанцерима ивично постављеним између њих. Активација је извршена електродетонирајућом капислом и малим бустером од пластичног експлозива. Заварени спој је испитан применом метода ултразвучне дефектоскопије, течним пенетрантима и микроструктурне анализе завареног споја. Микроструктурне анализе попречног пресека заварених плоча урађене су на стерео и оптичом микроскопу како би се анализирала зона завареног споја.Explosion welding is often used when conventional welding methods cannot provide welded joint of two dissimilar materials, but also when some specific geometry should be welded, or large surfaces of metal plates. The formation of a joint in explosive welding is based on the dynamic effect of the high pressure created by the explosion. For this purpose, most often some industrial explosives of low detonation parameters are used, and one of them is DEMEX, produced by TRAYAL, Serbia. In this research DEMEX was applied to weld plates of two different types of steel. Prior to the experimental procedure of welding, the selected metal plates, the explosive obtained from the producer was subjected to initial quality control: measurement of its bulk density and detonation velocity, using optical probes and a photodetector connected with an electronic counter. The experimental setup for welding was as follows: explosive DEMEX in powdery state was applied in a uniform layer over the upper plate, which was horizontally placed over the lower plate, in parallel position, with small wooden spacers, marginally placed between them. Activation was performed by an electro-detonating cap and a small booster of plastic explosive. The welded joint was examined using methods of ultrasonic defectoscopy, liquid penetrants testing and microstructural analysis of the welded joint. Cross-sectional microstructural analyses of the welded plates were performed using a stereo and optical microscope to analyze the weld zone

    Development of Novel Nano Platforms and Machine Learning Approaches for Raman Spectroscopy

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    In Raman spectroscopy, data analysis occupies a large amount of time and effort; thus, it is paramount to have the proper tools to extract the most meaning from the Raman analysis. This thesis explores improved ways to analyse Raman data mostly by using machine learning techniques available in Python. The substrate used throughout this thesis has been patterned through an electrohydrodynamic process that patterns micrometric pillars onto the substrate, which, after being gold coated, can generate surface-enhanced Raman scattering. An initial theoretical background was laid for the electrohydrodynamic process and additional observations regarding the fluid mechanics. Furthermore, when the structures are fabricated, and Raman measurements are taken, we show that it is possible to create an effective convolutional neural networks that systematically evaluate these patterns’ surface morphology and extracts features responsible for the surface-enhanced Raman scattering phenomenon. Being able to predict 90% of the time from optical microscope images and 99% of the time with atomic force microscopy images Additionally, a thorough machine learning analysis of the Raman literature was done. The best machine learning algorithms were put together into a script combined with a graphical user Interface that can run multiple commands such as principal component analysis and self-organizing maps, all in a centralised way. This way, we managed to consistently extract information from Raman and surface-enhanced Raman scattering spectra to open possibilities for precise peak analysis methods using a multi-Lorentzian fit algorithm

    MethOds and tools for comprehensive impact Assessment of the CCAM solutions for passengers and goods. D1.1: CCAM solutions review and gaps

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    Review of the state-of-the-art on Cooperative, Connected and Automated mobility use cases, scenarios, business models, Key Performance Indicators, impact evaluation methods, technologies, and user needs (for organisations & citizens)
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