Nesta dissertação nós revisamos a teoria básica que descreve a junção Josephson bosônica para uma e duas espécies partindo do modelo de Bose-Hubbard. Em seguida explicamos como é possível gerar campos de calibe artificiais em um sistema de átomos neutros, como é o caso do condensado de Bose-Einstein. Finalmente, utilizando os conhecimentos teóricos desenvolvidos anteriormente nós buscamos os estados estacionários de um sistema de pseudospin 1/2 submetido a um campo de calibre não-Abeliano artificial, que torna a dinâmica da junção muito mais complexa e rica. Nós também exploramos um novo desbalanceamento de população que surge no sistema, devido a presença do campo de calibre, com características similares as do Macroscopic Quantum Self-Trapping. / In this dissertation we review the basic theory that describes the bosonic Josephson junction for one and two species using the Bose-Hubbard model. Afterwards, we explain how it is possible to generate artificial gauge fields for neutral atoms, like a Bose-Einstein condensate. Finally, using this theoretical background we search for stationary states of a pseudospin 1/2 system subject to a non-Abelian artificial gauge field which turns the dynamic of the junction much more complex and rich. We also explore a possible new populational imbalance that appears on the system due to the presence of the gauge field, with similar features as the Macroscopic Quantum Self-Trapping.

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.

Fully autonomous vehicles are expected to revolutionize transportation, reduce the cost of ownership, contribute to a cleaner environment, and prevent the majority of traffic accidents and related fatalities. Even though promising approaches for achieving full autonomy exist, developers and manufacturers have to overcome a multitude of challenged before these systems could find widespread adoption. This multiple case study explored the strategies some IT hardware and software developers of self-driving cars use to adapt traditional vehicle design frameworks to address consumer and regulatory requirements in autonomous vehicle designs. The population consisted of autonomous driving technology software and hardware developers who are currently working on fully autonomous driving technologies from or within the United States, regardless of their specialization. The theory of dynamic capabilities was the conceptual framework used for the study. Interviews from 7 autonomous vehicle hard and software engineers, together with 15 archival documents, provided the data points for the study. A thematic analysis was used to code and group results by themes. When looking at the results through the lens of dynamic capability theory, notable themes included regulatory uncertainty, functional safety, rapid iteration, and achieving a competitive advantage. Based on the findings of the study, implications for social change include the need for better regulatory frameworks to provide certainty, consumer education to manage expectations, and universal development standards that could integrate regulatory and design needs into a single approach.

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This diploma thesis deals with automatic soccer event detection . Its goal is to introduce reader to this issue , discuss possible ways of solution of this task and then implement event detection . This work aims at event recognition using spatio - temporal data of gaming objects . Introduced way of dealing with event detection lies in its converting to sequence labeling task . Then such task is solved using LSTM recurrent neural networks . Lastly , result of sequence labeling is interpreted as detected events . Library for event detection has been created as the output of this work . This library allow user to experiment with different variants how to formulate event detection as sequence labeling task .

<p>Developing a reliable in-situ non-destructive testing method to determine the strength of in-place concrete is critical because a fast-paced construction schedule exposes concrete pavement and/or structures undergoing substantial loading conditions, even at their early ages. Conventional destructive testing methods, such as compressive and flexural tests, are very time-consuming, which may cause construction delays or cost overruns. Moreover, the curing conditions of the tested cylindrical samples and the in-place concrete pavement/structures are quite different, which may result in different strength values. An NDT method that could directly correlate the mechanical properties of cementitious materials with the sensing results, regardless of the curing conditions, mix design, and size effect is needed for the in-situ application.</p><p>The piezoelectric sensor-based electromechanical impedance (EMI) technique has shown promise in addressing this challenge as it has been used to both monitor properties and detect damages on the concrete structure. Due to the direct and inverse effects of piezoelectric, this material can act as a sensor, actuator, and transducer. This research serves as a comprehensive study to investigate the feasibility and efficiency of using piezoelectric sensor-based EMI to evaluate the strength of newly poured concrete. To understand the fundamentals of this method and enhance the durability of the sensor for in-situ monitoring, this work started with sensor fabrication. It has studied two types of polymer coating on the effect of the durability of the sensor to make it practical to be used in the field.</p><p>The mortar and concrete samples with various mix designs were prepared to ascertain whether the results of the proposed sensing technique were affected by the different mixtures. The EMI measurement and compressive strength testing methods (ASTM C39, ASTM C109) were conducted in the laboratory. The experimental results of mortar samples with different water-to-cement ratios (W/C) and two types of cement (I and III) showed that the correlation coefficient (R²) is higher than 0.93 for all mixes. In the concrete experiments, the correlation coefficient between the EMI sensing index and compressive strength of all mixes is higher than 0.90. The empirical estimation function was established through a concrete slab experiment. Moreover, several trial implementations on highway construction projects (I-70, I-74, and I-465) were conducted to monitor the real-time strength development of concrete. The data processing method and the reliable index of EMI sensing were developed to establish the regression model to correlate the sensing results with the compressive strength of concrete. It has been found that the EMI sensing method and its related statistical index can effectively reflect the compressive strength gain of in-place concrete at different ages.</p><p>To further investigate the in-situ compressive strength of concrete for large-scale structures, we conducted a series of large concrete slabs with the dimension of 8 feet × 12 feet × 8 inches in depth was conducted at outdoor experiments field to simulate real-world conditions. Different types of compressive strength samples, including cast-in-place (CIP) cylinder (4” × 6”) – (ASTM C873), field molded cylinder (4” × 8”) – (ASTM C39), and core drilled sample (4” × 8”) – (ASTM C42) were prepared to compare the compressive strength of concrete. The environmental conditions, such as ambient temperatures and relative humidity, were also recorded. The in-situ EMI monitoring of concrete strength was also conducted. The testing ages in this study were started from 6 hours after the concrete cast was put in place to investigate the early age results and continued up to 365 days (one year) later for long-term monitoring. The results indicate that the strength of the CIP sample is higher than the 4” x 8” molded cylinder , and that core drilled concrete is weaker than the two aforementioned. The EMI results obtained from the slab are close to those obtained from CIP due to similar curing conditions. The EMI results collected from 4 × 8-inch cylinder samples are lower than slab and CIP, which aligns with the mechanical testing results and indicates that EMI could capture the strength gain of concrete over time.</p><p>The consequent database collected from the large slab tests was used to build a prediction model for concrete strength. The Artificial Neuron Network (ANN) was investigated and experimented with to optimize the prediction of performances. Then, a sensitivity analysis was conducted to discuss and understand the critical parameters to predict the mechanical properties of concrete using the ML model. A framework using Generative Adversarial Network (GAN) based on algorithms was then proposed to overcome real data usage restrictions. Two types of GAN algorithms were selected for the data synthesis in the research: Tabular Generative Adversarial Networks (TGAN) and Conditional Tabular Generative Adversarial Networks (CTGAN). The testing results suggested that the CTGAN-NN model shows improved testing performances and higher computational efficiency than the TGAN model. In conclusion, the AI-guided concrete strength sensing and prediction approaches developed in this dissertation will be a steppingstone towards accomplishing the reliable and intelligent assessment of in-situ concrete structures.</p><br>

<div>Artificial Intelligence (AI) plays an increasingly pivotal role in drug discovery. In particular, artificial neural networks such as deep neural networks drive this area of research. The research presented in this thesis is considered a synergistic combination of physicochemical models of protein-ligand interactions such as molecular dynamics simulation, novel machine learning concepts and the use of big data for solving fundamental problems in Structure-Based Drug Design (SBDD). This area of research involves the use of three-dimensional (3D) structural data of biomolecules to assist lead discovery and optimization in a time- and cost-efficient manner. </div><div>The main focus of the thesis research is the development of models, algorithms and methods to facilitate binding-mode elucidation, affinity prediction for congeneric series of molecules and flexible docking. </div><div><br></div><div>For pose-prediction, we developed a Convolutional Neural Network model incorporating hydration information, named DeepWatsite, which displays accurate binding-mode prediction and the capability to highlight different roles of water molecules in protein-ligand binding. In order to train the neural network model, we created a comprehensive database for hydration information of thousands of protein systems. This was made possible through the development of an efficient GPU-accelerated version of Watsite, a program for generating hydration profiles of protein systems through molecular dynamics simulations.\newline</div><div>\indent For accurate affinity prediction for congeneric series of compounds, we developed a new methodological platform for mixed-solvent simulation based on the lambda-dynamics concept. Additionally, we developed a deep-learning model that combines molecular dynamics simulations and a distance-aware graph attention algorithm. Validation studies using this method revealed that its accuracy is competitive to resource-intensive free energy perturbation (FEP) calculations. To train the model, we generated a synthetic database of congeneric series of compounds extracted from the highest-quality medicinal chemistry articles. Molecular-dynamics simulations were used to simulate all the generated systems as method for data augmentation.\newline</div><div>\indent For flexible docking, we developed a machine-learning assisted docking strategy that relies on protein-ligand distance matrix predictions. This technique is built upon Weisfeiler-Lehman neural network concept with an attention mechanism. Comprehensive validation on docking and cross-docking datasets demonstrated the potential of this method to become a docking concept with higher accuracy and efficiency than existing state-of-the-art flexible docking techniques. </div><div><br></div><div>In summary, the thesis proved the general applicability of deep-learning to various tasks in SBDD. Furthermore, it demonstrates that treating biomolecules as dynamic entities can improve the quality of computational methods in structure-based drug design.</div>

Presented work tries to reflect the structure of human world, which is able to create an overman as an artificial intelligence through its self-destructive tendency. It investigates the possibilities of our imagination and if we are able to think about artificial intelligence as a sui generis continuation of human species. Hand in hand it tries to create a tension between particular ethico-political decisions and subsequent binding structure, which they implies.

Denna rapport presenterar en förstudie om digitalisering av Mörrumsåns avrinningsområde. Studien undersökte möjligheterna att använda digitala verktyg och tekniker för att förbättra vattenreglering och engagemang från intressenter, särskilt kommunerna i området. Förstudien visade att det fanns generellt gott intresse för att införa digitala lösningar för att förbättra samordningen av vattenreglering. Totalt identifierades 36 potentiella mätpunkter i avrinningsområdet, varav vissa betraktades som högre prioritet. En viktig del av förstudien var ett visualiseringsförslag, där geografiska informationssystem (GIS) är ett möjligt exempel till visualiseringen. Utmaningar identifierades kring sensorval och deras hållbarhet samt noggrannhet i olika miljöer.  Framtidsvisionen inkluderade användning av artificiell intelligens (AI) och maskininlärning (ML) för att förutsäga torka och översvämningar. Slutsatserna visade att digitalisering av vattenhantering kan förbättra samordningen och effektiviteten samt minska risken för torka och översvämningar, men kräver mer datainsamling, standardisering och samarbete mellan intressenter för framgångsrik implementering. Genom att använda digitala verktyg kan samordningen, effektiviteten och hållbarheten förbättras i Mörrumsåns avrinningsområde med målet att bevara ekosystem och vattenresurser för framtida generationer.

The most performant Machine Learning (ML) classifiers have been labeled black-boxes due to the complexity of their decision process. eXplainable Artificial Intelligence (XAI) methods aim to alleviate this issue by crafting an interpretable explanation for a models prediction. A drawback of most XAI methods is that they are heuristic with some drawbacks such as non determinism and locality. Formal Explanations (FE) have been proposed as a way to explain the decisions of classifiers by extracting a set of features that guarantee the prediction. In this thesis we explore these guarantees for different use cases: speeding up the inference speed of tree-based Machine Learning classifiers, curriculum learning using said classifiers and also reducing training data. We find that under the right circumstances we can achieve up to 6x speedup by partially compiling the model to a set of rules that are extracted using formal explainability methods. / De mest effektiva maskininlärningsklassificerarna har betecknats som svarta lådor på grund av komplexiteten i deras beslutsprocess. Metoder för förklarbar artificiell intelligens (XAI) syftar till att lindra detta problem genom att skapa en tolkbar förklaring för modellens prediktioner. En nackdel med de flesta XAI-metoder är att de är heuristiska och har vissa nackdelar såsom icke-determinism och lokalitet. Formella förklaringar (FE) har föreslagits som ett sätt att förklara klassificerarnas beslut genom att extrahera en uppsättning funktioner som garanterar prediktionen. I denna avhandling utforskar vi dessa garantier för olika användningsfall: att öka inferenshastigheten för maskininlärningsklassificerare baserade på träd, kurser med hjälp av dessa klassificerare och även minska träningsdata. Vi finner att under rätt omständigheter kan vi uppnå upp till 6 gånger snabbare prestanda genom att delvis kompilera modellen till en uppsättning regler som extraheras med hjälp av formella förklaringsmetoder.