8,735 research outputs found

    Placental origins of health & disease:Therapeutic opportunities

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    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    Resilience and food security in a food systems context

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    This open access book compiles a series of chapters written by internationally recognized experts known for their in-depth but critical views on questions of resilience and food security. The book assesses rigorously and critically the contribution of the concept of resilience in advancing our understanding and ability to design and implement development interventions in relation to food security and humanitarian crises. For this, the book departs from the narrow beaten tracks of agriculture and trade, which have influenced the mainstream debate on food security for nearly 60 years, and adopts instead a wider, more holistic perspective, framed around food systems. The foundation for this new approach is the recognition that in the current post-globalization era, the food and nutritional security of the world’s population no longer depends just on the performance of agriculture and policies on trade, but rather on the capacity of the entire (food) system to produce, process, transport and distribute safe, affordable and nutritious food for all, in ways that remain environmentally sustainable. In that context, adopting a food system perspective provides a more appropriate frame as it incites to broaden the conventional thinking and to acknowledge the systemic nature of the different processes and actors involved. This book is written for a large audience, from academics to policymakers, students to practitioners

    Engineering Systems of Anti-Repressors for Next-Generation Transcriptional Programming

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    The ability to control gene expression in more precise, complex, and robust ways is becoming increasingly relevant in biotechnology and medicine. Synthetic biology has sought to accomplish such higher-order gene regulation through the engineering of synthetic gene circuits, whereby a gene’s expression can be controlled via environmental, temporal, or cellular cues. A typical approach to gene regulation is through transcriptional control, using allosteric transcription factors (TFs). TFs are regulatory proteins that interact with operator DNA elements located in proximity to gene promoters to either compromise or activate transcription. For many TFs, including the ones discussed here, this interaction is modulated by binding to a small molecule ligand for which the TF evolved natural specificity and a related metabolism. This modulation can occur with two main phenotypes: a TF shows the repressor (X+) phenotype if its binding to the ligand causes it to dissociate from the DNA, allowing transcription, while a TF shows the anti-repressor (XA) phenotype if its binding to the ligand causes it to associate to the DNA, preventing transcription. While both functional phenotypes are vital components of regulatory gene networks, anti-repressors are quite rare in nature compared to repressors and thus must be engineered. We first developed a generalized workflow for engineering systems of anti-repressors from bacterial TFs in a family of transcription factors related to the ubiquitous lactose repressor (LacI), the LacI/GalR family. Using this workflow, which is based on a re-routing of the TF’s allosteric network, we engineered anti-repressors in the fructose repressor (anti-FruR – responsive to fructose-1,6-phosphate) and ribose repressor (anti-RbsR – responsive to D-ribose) scaffolds, to complement XA TFs engineered previously in the LacI scaffold (anti-LacI – responsive to IPTG). Engineered TFs were then conferred with alternate DNA binding. To demonstrate their utility in synthetic gene circuits, systems of engineered TFs were then deployed to construct transcriptional programs, achieving all of the NOT-oriented Boolean logical operations – NOT, NOR, NAND, and XNOR – in addition to BUFFER and AND. Notably, our gene circuits built using anti-repressors are far simpler in design and, therefore, exert decreased burden on the chassis cells compared to the state-of-the-art as anti-repressors represent compressed logical operations (gates). Further, we extended this workflow to engineer ligand specificity in addition to regulatory phenotype. Performing the engineering workflow with a fourth member of the LacI/GalR family, the galactose isorepressor (GalS – naturally responsive to D-fucose), we engineered IPTG-responsive repressor and anti-repressor GalS mutants in addition to a D-fucose responsive anti-GalS TF. These engineered TFs were then used to create BANDPASS and BANDSTOP biological signal processing filters, themselves compressed compared to the state-of-the-art, and open-loop control systems. These provided facile methods for dynamic turning ‘ON’ and ‘OFF’ of genes in continuous growth in real time. This presents a general advance in gene regulation, moving beyond simple inducible promoters. We then demonstrated the capabilities of our engineered TFs to function in combinatorial logic using a layered logic approach, which currently stands as the state-of-the art. Using our anti-repressors in layered logic had the advantage of reducing cellular metabolic burden, as we were able to create the fundamental NOT/NOR operations with fewer genetic parts. Additionally, we created more TFs to use in layered logic approaches to prevent cellular cross-talk and minimize the number of TFs necessary to create these gene circuits. Here we demonstrated the successful deployment of our XA-built NOR gate system to create the BUFFER, NOT, NOR, OR, AND, and NAND gates. The work presented here describes a workflow for engineering (i) allosteric phenotype, (ii) ligand selectivity, and (iii) DNA specificity in allosteric transcription factors. The products of the workflow themselves serve as vital tools for the construction of next-generation synthetic gene circuits and genetic regulatory devices. Further, from the products of the workflow presented here, certain design heuristics can be gleaned, which should better facilitate the design of allosteric TFs in the future, moving toward a semi-rational engineering approach. Additionally, the work presented here outlines a transcriptional programming structure and metrology which can be broadly adapted and scaled for future applications and expansion. Consequently, this thesis presents a means for advanced control of gene expression, with promise to have long-reaching implications in the future.Ph.D

    Electrical and Optical Modeling of Thin-Film Photovoltaic Modules

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    Heutzutage ist durch viele wissenschaftliche Studien nachgewiesen, dass die Erde lĂ€ngst 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 ĂŒberhaupt - um diese angesprochene, grĂ¶ĂŸte Herausforderung in der Geschichte der Menschheit zu bewĂ€ltigen, ist es, den Energiehunger der Menschheit durch die Erzeugung erneuerbarer und unerschöpflicher Energie zu sĂ€ttigen. Die Photovoltaik (PV)-Technologie ist ein vielversprechender AnwĂ€rter, die leistungsstĂ€rkste erneuerbare Energiequelle zu stellen, und spielt aufgrund ihrer direkten Umwandlung des Sonnenlichtes und ihrer skalierbaren Anwendbarkeit in Form von großflĂ€chigen 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 DĂŒnnschichttechnologie haben jedoch vorteilhafte Eigenschaften wie einen sehr geringen Kohlenstoffdioxid (CO2)-Fußabdruck, eine kurze energetische Amortisierungszeit und das Potenzial fĂŒr eine kostengĂŒnstige monolithische Massenproduktion, obwohl diese derzeit noch nicht final ausgereift ist. Um die DĂŒnnschichttechnologie jedoch gezielt in Richtung einer breiten Marktreife zu entwickeln, sind numerische Simulationen eine wichtige SĂ€ule fĂŒr das wissenschaftliche VerstĂ€ndnis und die technologische Optimierung. WĂ€hrend sich traditionelle Simulationsliteratur hĂ€ufig mit materialspezifischen Herausforderungen befasst, konzentriert sich diese Arbeit auf industrieorientierte Herausforderungen auf Modulebene, ohne die zugrundeliegenden Materialparameter zu verĂ€ndern. Um ein allumfassendes, digitales Modell eines Solarmoduls zu erstellen, werden in dieser Arbeit mehrere SimulationsansĂ€tze aus verschiedenen physikalischen Bereichen kombiniert. Zur Abbildung elektrischer Effekte, einschließlich der rĂ€umlichen Spannungsvariation innerhalb des Moduls, wird eine Finite Elemente Methode (FEM) zur Lösung der rĂ€umlich quantisierten Poisson-Gleichung verwendet. Um optische Effekte zu berĂŒcksichtigen, wird eine generalisierte Transfermatrix-Methode (TMM) verwendet. Alle Simulationsmethoden sind in dieser Arbeit von Grund auf neu programmiert worden, um eine VerknĂŒpfung aller Simulationsebenen mit dem höchstmöglichen Grad an Anpassung und VerknĂŒpfung 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 LĂŒcke zwischen der Simulation auf Materialebene ĂŒber die Berechnung von Laborwirkungsgraden bis hin zur Bestimmung der von zahlreichen Umweltfaktoren beeinflusste Leistung der Module im Freifeld zu ĂŒberbrĂŒcken. Durch diese VerknĂŒpfung von Zellsimulation und Systemdesign ist es lediglich aus Laboreigenschaften möglich, das Freifeldverhalten von Solarmodulen zu prognostizieren. Sogar das ZurĂŒckrechnen 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 fĂŒr mehrere Anwendungen genutzt werden. ZunĂ€chst können durch die Kombination von elektrischen und optischen Simulationen ganzheitliche Top-Down-Verlustanalysen durchgefĂŒhrt werden. Dies ermöglicht eine wissenschaftliche Einordnung und einen quantitativen Vergleich aller Verlustleistungsmechanismen auf einen Blick, was die zukĂŒnftige Forschung und Entwicklung in Richtung von technologischen Schwachstellen von Solarmodulen lenkt. DarĂŒber hinaus ermöglicht die Kombination von Elektrik und Optik die Detektion von Verlusten, die auf dem nichtlinearen Zusammenspiel dieser beiden Ebenen beruhen und auf eine rĂ€umliche Spannungsverteilung im Solarmodul zurĂŒckzufĂŒhren sind. Diese Arbeit verwendet die entwickelten numerischen Modelle ebenfalls fĂŒr Optimierungsprobleme, die an digitalen Modellen realer Solarmodule durchgefĂŒhrt werden. HĂ€ufig auftretende Fragestellungen bei der Entwicklung von Solarmodulen sind beispielsweise die Schichtdicke des vorderen optisch transparenten, elektrisch leitfĂ€higen Oxids (TCO) oder die Breite von monolithisch verschalteten Zellen. Die Bestimmung des Optimums dieser mehrdimensionalen AbwĂ€gungen zwischen optischer Transparenz, elektrischer LeitfĂ€higkeit und geometrisch inaktiver FlĂ€che zwischen den einzelnen Zellen ist ein Hauptmerkmal der Methodik dieser Arbeit. Mittels des FEM-Ansatzes dieser Arbeit ist es möglich, alle gegenseitigen Wechselwirkungen ĂŒber verschiedene physikalische Ebenen hinweg zu berĂŒcksichtigen und ein ganzheitlich optimiertes Moduldesign zu finden. Auch topologisch komplexere Probleme, wie das Finden eines geeigneten Designs fĂŒr 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 fĂŒr monolithisch integrierte Zellen eingesetzt. DarĂŒber hinaus wurde gezeigt, dass sowohl einfache Optimierungen der TCO-Schichtdicken als auch Topologie-Optimierungen stark von den vorherrschenden BeleuchtungsverhĂ€ltnissen abhĂ€ngen. Daher ist eine Optimierung auf den Jahresertrag anstelle des Laborwirkungsgrades fĂŒr industrienahe Anwendungen wesentlich sinnvoller, da die mittleren Jahreseinstrahlungen deutlich von den Laborbedingungen abweichen. Mit Hilfe dieser Ertragsoptimierung wurde in dieser Arbeit fĂŒr die Kupfer-Indium-Gallium-Diselenid CuIn1−x_{1-x}Gax_xSe2_2 (CIGS)-Technologie ein Leistungsgewinn von ĂŒber 1 % im Ertrag fĂŒr einige geografische Standorte und gleichzeitig eine Materialeinsparung fĂŒr 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 fĂŒr CIGS-DĂŒnnschicht-Solarmodule berechnet. Unter Verwendung der Randbedingungen der derzeit verfĂŒgbaren 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

    Real-Time Hybrid Visual Servoing of a Redundant Manipulator via Deep Reinforcement Learning

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    Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control could be effective for sensitive manipulation tasks where the desired end-effector pose can be ascertained via visual cues. Visual data is complex and computationally expensive to process but deep reinforcement learning has shown promise for robotic control in vision-based manipu-lation tasks. However, these methods are rarely used in industry due to the resources and expertise required to develop application-specific systems and prohibitive train-ing costs. Training reinforcement learning models in simulated environments offers a number of benefits for the development of robust robotic control algorithms by reducing training time and costs, and providing repeatable benchmarks for which algorithms can be tested, developed and eventually deployed on real robotic control environments. In this work, we present a new simulated reinforcement learning envi-ronment for developing accurate robotic manipulation control systems in fixtureless environments. Our environment incorporates a contemporary collaborative industrial robot, the KUKA LBR iiwa, with the goal of positioning its end effector in a generic fixtureless environment based on a visual cue. Observational inputs are comprised of the robotic joint positions and velocities, as well as two cameras, whose positioning reflect hybrid visual servoing with one camera attached to the robotic end-effector, and another observing the workspace respectively. We propose a state-of-the-art deep reinforcement learning approach to solving the task environment and make prelimi-nary assessments of the efficacy of this approach to hybrid visual servoing methods for the defined problem environment. We also conduct a series of experiments ex-ploring the hyperparameter space in the proposed reinforcement learning method. Although we could not prove the efficacy of a deep reinforcement approach to solving the task environment with our initial results, we remain confident that such an ap-proach could be feasible to solving this industrial manufacturing challenge and that our contributions in this work in terms of the novel software provide a good basis for the exploration of reinforcement learning approaches to hybrid visual servoing in accurate manufacturing contexts

    Post-Growth Geographies: Spatial Relations of Diverse and Alternative Economies

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    Post-Growth Geographies examines the spatial relations of diverse and alternative economies between growth-oriented institutions and multiple socio-ecological crises. The book brings together conceptual and empirical contributions from geography and its neighbouring disciplines and offers different perspectives on the possibilities, demands and critiques of post-growth transformation. Through case studies and interviews, the contributions combine voices from activism, civil society, planning and politics with current theoretical debates on socio-ecological transformation
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