1,501 research outputs found

    Addiction in context

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    The dissertation provides a comprehensive exploration of the interplay between social and cultural factors in substance use, specifically focusing on alcohol use disorder (AUD) and cannabis use disorder (CUD). It begins by introducing the concept of social plasticity, which posits that adolescents' susceptibility to AUD is influenced by their heightened sensitivity to their social environment, but this sensitivity increases the potential for recovery in the transition to adulthood.A series of studies delves into how social cues impact alcohol craving and consumption. One study using functional magnetic resonance imaging (fMRI) investigated social alcohol cue reactivity and its relationship to social drinking behavior, revealing increased craving but no significant change in brain activity in response to alcohol cues. Another fMRI study compared social processes in alcohol cue reactivity between adults and adolescents, showing age-related differences in how social attunement affects drinking behavior. Shifting focus to cannabis, this dissertation discusses how cultural factors, including norms, legal policies, and attitudes, influence cannabis use and processes underlying CUD. The research presented examined various facets of cannabis use, including how cannabinoid concentrations in hair correlate with self-reported use, the effects of cannabis and cigarette co-use on brain reactivity, and cross-cultural differences in CUD between Amsterdam and Texas. Furthermore, the evidence for the relationship between cannabis use, CUD, and mood disorders is reviewed, suggesting a bidirectional relationship, with cannabis use potentially preceding the onset of bipolar disorder and contributing to the development and worse prognosis of mood disorders and mood disorders leading to more cannabis use

    Pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inženjerstvu vođenom modelima

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    In this thesis, we present an approach to the production process specification and generation based on the model-driven paradigm, with the goal to increase the flexibility of factories and respond to the challenges that emerged in the era of Industry 4.0 more efficiently. To formally specify production processes and their variations in the Industry 4.0 environment, we created a novel domain-specific modeling language, whose models are machine-readable. The created language can be used to model production processes that can be independent of any production system, enabling process models to be used in different production systems, and process models used for the specific production system. To automatically transform production process models dependent on the specific production system into instructions that are to be executed by production system resources, we created an instruction generator. Also, we created generators for different manufacturing documentation, which automatically transform production process models into manufacturing documents of different types. The proposed approach, domain-specific modeling language, and software solution contribute to introducing factories into the digital transformation process. As factories must rapidly adapt to new products and their variations in the era of Industry 4.0, production must be dynamically led and instructions must be automatically sent to factory resources, depending on products that are to be created on the shop floor. The proposed approach contributes to the creation of such a dynamic environment in contemporary factories, as it allows to automatically generate instructions from process models and send them to resources for execution. Additionally, as there are numerous different products and their variations, keeping the required manufacturing documentation up to date becomes challenging, which can be done automatically by using the proposed approach and thus significantly lower process designers' time.У овој дисертацији представљен је приступ спецификацији и генерисању производних процеса заснован на инжењерству вођеном моделима, у циљу повећања флексибилности постројења у фабрикама и ефикаснијег разрешавања изазова који се појављују у ери Индустрије 4.0. За потребе формалне спецификације производних процеса и њихових варијација у амбијенту Индустрије 4.0, креиран је нови наменски језик, чије моделе рачунар може да обради на аутоматизован начин. Креирани језик има могућност моделовања производних процеса који могу бити независни од производних система и тиме употребљени у различитим постројењима или фабрикама, али и производних процеса који су специфични за одређени систем. Како би моделе производних процеса зависних од конкретног производног система било могуће на аутоматизован начин трансформисати у инструкције које ресурси производног система извршавају, креиран је генератор инструкција. Такође су креирани и генератори техничке документације, који на аутоматизован начин трансформишу моделе производних процеса у документе различитих типова. Употребом предложеног приступа, наменског језика и софтверског решења доприноси се увођењу фабрика у процес дигиталне трансформације. Како фабрике у ери Индустрије 4.0 морају брзо да се прилагоде новим производима и њиховим варијацијама, неопходно је динамички водити производњу и на аутоматизован начин слати инструкције ресурсима у фабрици, у зависности од производа који се креирају у конкретном постројењу. Тиме што је у предложеном приступу могуће из модела процеса аутоматизовано генерисати инструкције и послати их ресурсима, доприноси се креирању једног динамичког окружења у савременим фабрикама. Додатно, услед великог броја различитих производа и њихових варијација, постаје изазовно одржавати неопходну техничку документацију, што је у предложеном приступу могуће урадити на аутоматизован начин и тиме значајно уштедети време пројектаната процеса.U ovoj disertaciji predstavljen je pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inženjerstvu vođenom modelima, u cilju povećanja fleksibilnosti postrojenja u fabrikama i efikasnijeg razrešavanja izazova koji se pojavljuju u eri Industrije 4.0. Za potrebe formalne specifikacije proizvodnih procesa i njihovih varijacija u ambijentu Industrije 4.0, kreiran je novi namenski jezik, čije modele računar može da obradi na automatizovan način. Kreirani jezik ima mogućnost modelovanja proizvodnih procesa koji mogu biti nezavisni od proizvodnih sistema i time upotrebljeni u različitim postrojenjima ili fabrikama, ali i proizvodnih procesa koji su specifični za određeni sistem. Kako bi modele proizvodnih procesa zavisnih od konkretnog proizvodnog sistema bilo moguće na automatizovan način transformisati u instrukcije koje resursi proizvodnog sistema izvršavaju, kreiran je generator instrukcija. Takođe su kreirani i generatori tehničke dokumentacije, koji na automatizovan način transformišu modele proizvodnih procesa u dokumente različitih tipova. Upotrebom predloženog pristupa, namenskog jezika i softverskog rešenja doprinosi se uvođenju fabrika u proces digitalne transformacije. Kako fabrike u eri Industrije 4.0 moraju brzo da se prilagode novim proizvodima i njihovim varijacijama, neophodno je dinamički voditi proizvodnju i na automatizovan način slati instrukcije resursima u fabrici, u zavisnosti od proizvoda koji se kreiraju u konkretnom postrojenju. Time što je u predloženom pristupu moguće iz modela procesa automatizovano generisati instrukcije i poslati ih resursima, doprinosi se kreiranju jednog dinamičkog okruženja u savremenim fabrikama. Dodatno, usled velikog broja različitih proizvoda i njihovih varijacija, postaje izazovno održavati neophodnu tehničku dokumentaciju, što je u predloženom pristupu moguće uraditi na automatizovan način i time značajno uštedeti vreme projektanata procesa

    Optimization for Energy Management in the Community Microgrids

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    This thesis focuses on improving the energy management strategies for Community Microgrids (CMGs), which are expected to play a crucial role in the future smart grid. CMGs bring many benefits, including increased use of renewable energy, improved reliability, resiliency, and energy efficiency. An Energy Management System (EMS) is a key tool that helps in monitoring, controlling, and optimizing the operations of the CMG in a cost-effective manner. The EMS can include various functionalities like day-ahead generation scheduling, real-time scheduling, uncertainty management, and demand response programs. Generation scheduling in a microgrid is a challenging optimization problem, especially due to the intermittent nature of renewable energy. The power balance constraint, which is the balance between energy demand and generation, is difficult to satisfy due to prediction errors in energy demand and generation. Real-time scheduling, which is based on a shorter prediction horizon, reduces these errors, but the impact of uncertainties cannot be completely eliminated. In regards to demand response programs, it is challenging to design an effective model that motivates customers to voluntarily participate while benefiting the system operator. Mathematical optimization techniques have been widely used to solve power system problems, but their application is limited by the need for specific mathematical properties. Metaheuristic techniques, particularly Evolutionary Algorithms (EAs), have gained popularity for their ability to solve complex and non-linear problems. However, the traditional form of EAs may require significant computational effort for complex energy management problems in the CMG. This thesis aims to enhance the existing methods of EMS in CMGs. Improved techniques are developed for day-ahead generation scheduling, multi-stage real-time scheduling, and demand response implementation. For generation scheduling, the performance of conventional EAs is improved through an efficient heuristic. A new multi-stage scheduling framework is proposed to minimize the impact of uncertainties in real-time operations. In regards to demand response, a memetic algorithm is proposed to solve an incentive-based scheme from the perspective of an aggregator, and a price-based demand response driven by dynamic price optimization is proposed to enhance the electric vehicle hosting capacity. The proposed methods are validated through extensive numerical experiments and comparison with state-of-the-art approaches. The results confirm the effectiveness of the proposed methods in improving energy management in CMGs

    Guided rewriting and constraint satisfaction for parallel GPU code generation

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    Graphics Processing Units (GPUs) are notoriously hard to optimise for manually due to their scheduling and memory hierarchies. What is needed are good automatic code generators and optimisers for such parallel hardware. Functional approaches such as Accelerate, Futhark and LIFT leverage a high-level algorithmic Intermediate Representation (IR) to expose parallelism and abstract the implementation details away from the user. However, producing efficient code for a given accelerator remains challenging. Existing code generators depend on the user input to choose a subset of hard-coded optimizations or automated exploration of implementation search space. The former suffers from the lack of extensibility, while the latter is too costly due to the size of the search space. A hybrid approach is needed, where a space of valid implementations is built automatically and explored with the aid of human expertise. This thesis presents a solution combining user-guided rewriting and automatically generated constraints to produce high-performance code. The first contribution is an automatic tuning technique to find a balance between performance and memory consumption. Leveraging its functional patterns, the LIFT compiler is empowered to infer tuning constraints and limit the search to valid tuning combinations only. Next, the thesis reframes parallelisation as a constraint satisfaction problem. Parallelisation constraints are extracted automatically from the input expression, and a solver is used to identify valid rewriting. The constraints truncate the search space to valid parallel mappings only by capturing the scheduling restrictions of the GPU in the context of a given program. A synchronisation barrier insertion technique is proposed to prevent data races and improve the efficiency of the generated parallel mappings. The final contribution of this thesis is the guided rewriting method, where the user encodes a design space of structural transformations using high-level IR nodes called rewrite points. These strongly typed pragmas express macro rewrites and expose design choices as explorable parameters. The thesis proposes a small set of reusable rewrite points to achieve tiling, cache locality, data reuse and memory optimisation. A comparison with the vendor-provided handwritten kernel ARM Compute Library and the TVM code generator demonstrates the effectiveness of this thesis' contributions. With convolution as a use case, LIFT-generated direct and GEMM-based convolution implementations are shown to perform on par with the state-of-the-art solutions on a mobile GPU. Overall, this thesis demonstrates that a functional IR yields well to user-guided and automatic rewriting for high-performance code generation

    Targeting organogenesis and beta cell survival: role of the LRH1/NR5A2-PTGS2/COX2 signaling axis in pancreatic islet physiology and pathophysiology

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Biotecnología, Biomedicina y Ciencias de la SaludClave Programa: DBICódigo Línea: 110Type 1 Diabetes Mellitus (T1DM) is a disease caused by the selective destruction of pancreatic islet beta cells by aberrant activation of the immune system, characterized by a subsequent chronic unresolved proinflammatory status within the pancreas. Up to date, no effective therapies have been developed to cure this autoimmune disorder, which indeed, apart from the beta cell death and subsequent lack of insulin, leads to long-term complications that substantially impact on life quality and shorten life expectancy. However, our laboratory recently reported promising outcomes from the in vivo activation of a nuclear receptor, denoted as Liver Receptor Homolog 1 (also known as (a.k.a.) Nuclear Receptor Subfamily 5 Group A Member 2, LRH1/NR5A2), using different preclinical mouse models of autoimmune diabetes, and also in vitro, by mimicking the stress/proinflammatory conditions that characterize T1DM in both, mouse and human primary islet-cell cultures. These beneficial effects derived from the treatment with a chemical agonist of LRH1/NR5A2, codename BL001, which potentially favoured a crosstalk between the immune system and islet cells, aimed at protecting the beta cell mass via increasing its survival. Understanding the molecular signaling and consequences derived from LRH1/NR5A2 expression and activation in beta cells was the following step to exploit its therapeutic value within T1DM conditions. In this Thesis, we first uncovered the essential role of LRH1/NR5A2 expression in beta cells during neonatal development. We found that the LRH1/NR5A2 constitutive ablation in the beta cell mass caused a significant reduction of this cell type, mainly characterized by blunted proliferation, along with detrimental consequences in the metabolic and physical health of mouse pups that culminated in early death. We next demonstrated that the LRH1/NR5A2 specific activation in beta cells was the responsible of the beneficial effects observed in vivo, after BL001 treatment. Using an inducible approach, LRH1/NR5A2 ablation in adult beta cells abolished the protective effect of BL001 in streptozotocin (STZ)-treated mice, correlating with an almost complete beta cell mass destruction. In order to get insight into the mode of action of this potential anti-diabetic drug in beta cells, we next explored the molecular branches of the BL001-LRH1/NR5A2 axis, focusing on the inducible Prostaglandin Endoperoxidase Synthase-2 gene (a.k.a. Cyclooxygenase-2, Ptgs2/Cox2), previously shown to be upregulated by BL001, and which plays a role in immunomodulation. Ptgs2/Cox2 downstream signaling involves the secretion of Prostaglandin E2 (PGE2) and activation of one or several Prostaglandin G-protein coupled receptors (a.k.a. E-Prostanoid receptors, PTGERs/EPs). We found that mouse islets treated in vitro with BL001 upon a proinflammatory cytokine (CTK) challenge produced PGE2 massively. Importantly, both silencing of Ptgs2/Cox2 gene or downstream blockade of the anti-apoptotic PTGER1/EP1 receptor negated BL001-mediated increased islet-cell survival upon the CTK treatment, establishing the molecular survival signaling axis in mouse beta cells as follows: BL001-LRH1/NR5A2-Ptgs2/Cox2-PGE2-PTGER1/EP1. In parallel, we uncovered the deleterious role of the pro-apoptotic PTGER3/EP3 in an in vivo context, using the RIP-B7.1 mouse model of autoimmune diabetes. We found that PTGER3/EP3 antagonism reduced insulitis and protected the beta cell mass in these animals. Finally, as a future therapy for T1DM, it was mandatory to translate our survival cascade to a human setting. As such, we successfully recapitulated part of this pathway in human induced-Pluripotent Stem Cells (hiPSCs) derived islet-like organoids. This research work provides a complete molecular characterization of LRH1/NR5A2 activation specifically in the beta cell mass, which could be further fine-tuned to finally develop a successful therapy for T1DM.Universidad Pablo de Olavide de Sevilla. Departamento de Biología Molecular e Ingeniería Bioquímic

    Building Energy Modeling and Studies of Electric Power Distribution Systems with Distributed Energy Resources

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    There is significant opportunity for savings in energy and investment from improved performance of electric Power Distribution Systems (PDSs) through optimal planning and operation of conventional voltage-controlling devices. Novel multi-step model conversion and optimal capacitor planning (OCP) procedures are proposed for large-scale utility PDSs and are exemplified with an existing utility circuit of approximately 4,000 buses. Simulated optimal control and operation is achieved with a cluster-based approach that utilizes load-forecasting to minimize equipment degradation by intelligently dispersing device setting adjustments over time such that they remain most applicable. Improved performance may also be achieved through smart building technologies and Virtual Power Plant (VPP) control of increasingly more prevalent Distributed Energy Resources (DERs). The established simulation test bed for PDSs incorporates DERs to evaluate VPP implementations and an optimization process for control timing is proposed that minimizes targeted peak power and possible resulting increase in total daily energy. The advanced VPP controls incorporate the Consumer Technology Association (CTA) 2045 standard and EnergyStar performance characterizations to leverage HVAC systems as Generalized Energy Storage (GES) for load manipulation and to support the integration of demand-side generating DERs, such as local solar Photo-Voltaic (PV) systems

    Taylor University Catalog 2023-2024

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    The 2023-2024 academic catalog of Taylor University in Upland, Indiana.https://pillars.taylor.edu/catalogs/1128/thumbnail.jp

    DQN-based intelligent controller for multiple edge domains

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    Advanced technologies like network function virtualization (NFV) and multi-access edge computing (MEC) have been used to build flexible, highly programmable, and autonomously manageable infrastructures close to the end-users, at the edge of the network. In this vein, the use of single-board computers (SBCs) in commodity clusters has gained attention to deploy virtual network functions (VNFs) due to their low cost, low energy consumption, and easy programmability. This paper deals with the problem of deploying VNFs in a multi-cluster system formed by this kind of node which is characterized by limited computational and battery capacities. Additionally, existing platforms to orchestrate and manage VNFs do not consider energy levels during their placement decisions, and therefore, they are not optimized for energy-constrained environments. In this regard, this study proposes an intelligent controller as a global allocation mechanism based on deep reinforcement learning (DRL), specifically on deep Q-network (DQN). The conceived mechanism optimizes energy consumption in SBCs by selecting the most suitable nodes across several clusters to deploy event requests in terms of nodes’ resources and events’ demands. A comparison with available allocation algorithms revealed that our solution required 28% fewer resource costs and reduced 35% the energy consumption in the clusters’ computing nodes while maintaining high levels of acceptance ratio.This work has been supported in part (50%) by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under projects PID2019-108713RB-C51 & PID2019-108713RB-C52 MCIN/ AEI/10.13039/501100011033; and in part (50%) by AI@EDGE H2020-ICT-52-2020 under grant agreement No. 10101592
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