Abstract In this literary, we discuss with structure and morphology improvement of GaN epilayer on c-sapphire by multi-step method in molecular-beam epitaxy. Our research is caused for the critical results of defect in GaN epilayer and rough surface morphology. In order to solve these problems we used a novel technique which we called multi-step method. In this thesis, the results of X-ray, SEM, AFM all demonstrated the achievement in our composition. However, we obtained the results of full width of half maxima (FWHM) of (0002) and (10 2) XRD rocking curves with 60~120 arcseconds and 700~ 1200 arcseconds from a series of multi-step samples respectively. Comparing with previous measurement, multi-step method is relatively superior, and the measurement of AFM roughness is under 2 nm from the series of multi-step samples. If we discuss the flat area further, we can get smoother surface which roughness is about 0.4 nm. It is obviously to recognize the flat and rough regions, but in SEM image we made sure that the flat region occupied the greater part of surface. So, in this literary we verified that the method of multi-step can improve the structure and morphology of GaN by molecular-beam epitaxy.

A model has been developed to predict the yield stress of the aluminum alloy AA6111 after multi-step heat treatments which involve combinations of ambient temperature ageing and high temperature artificial ageing. The model framework follows the internal state variable framework where the two principal state variables are i) the volume fraction of clusters which form at ambient temperature and ii) the volume fraction of metastable phases which form during high temperature ageing. The evolution of the these state variables has modeled using a set of coupled differential equations. The mechanical response (the yield stress) is then formulated in terms of the state variables through an appropriate flow stress addition law. To test the model predictions a series of experiments were conducted which examined two scenarios for multi-step heat treatments. In general, good agreement was observed between the model predictions and the experimental results. However, for the case where a short thermal excursion at 250oC was applied immediately after the solution treatment, the results were not satisfactory. This can be understood in terms of the importance of the temperature dependence for the nucleation density of metastable precipitates.

Orientador: Anna Diva Plasencia Lotufo / Resumo: A maior inserção de tecnologias da informação nas redes de distribuição de energia elétrica vem permitindo que maiores volumes de dados de consumo sejam capturados em níveis cada vez mais detalhados, menos agregados e com maiores resoluções. Com a evolução dos mercados de energia elétrica, esses tipos de dados alcançam maior importância, uma vez que a comercialização de energia também passa a considerar estes níveis de consumo. Diversas técnicas têm sido aplicadas para previsão de cargas elétricas, como modelos estatísticos, de inteligência computacional e híbridos. Na literatura especializada é possível encontrar trabalhos que aplicam a rede neural artificial ARTMAP Fuzzy para tarefas de previsão de cargas elétricas, no entanto, a técnica ainda é pouco explorada em cenários de consumo menos agregados, e com maiores níveis de detalhe. Neste trabalho a rede ARTMAP Fuzzy é aplicada em tarefas de previsão multi-step de cargas elétricas reais com distintos níveis de agregação. Considerando o impacto do ruído sobre os previsores, sobretudo na capacidade de generalização das redes neurais artificiais, a técnica singular spectrum analysis é aplicada na tarefa de remoção de ruído. Os resultados de previsão permitiram analisar desempenho da rede ARTMAP Fuzzy, que foi comparada com outros dois previsores utilizados como benchmark, a saber, seasonal autoregressive integrated moving average e a rede neural multiLayer perceptron. A remoção de ruído permitiu melhora nos níveis de generaliz... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The increased insertion of information technologies in electricity distribution networks has allowed larger volumes of consumption data to be captured at increasingly detailed, less aggregated and higher resolution levels. With the evolution of electric energy markets, these types of data become more important, since the commercialization of energy also begins to consider these levels of consumption. Several techniques have been applied to predict electrical loads, such as statistical, computational intelligence and hybrids models. In the specialized literature it is possible to find works that apply the artificial neural network ARTMAP Fuzzy for tasks of prediction of electric charges, however, the technique is still little explored in less aggregated consumption scenarios, and with greater levels of detail. In this work the ARTMAP Fuzzy network is applied in multi-step forecasting tasks of real electric loads with different levels of aggregation. Considering the impact of noise on predictors, especially in the generalization capacity of artificial neural networks, the singular spectrum analysis technique is applied in the noise removal task. The prediction results allowed to analyze the performance of the ARTMAP Fuzzy network, which was compared with other two predictors used as benchmark, namely seasonal autoregressive integrated moving average and the multiLayer perceptron neural network. The noise removal allowed an improvement in the levels of network generalization, po... (Complete abstract click electronic access below) / Doutor

Submitted by MARCOS RICARDO MÜLLER (marcos.ricardo.unesp@gmail.com) on 2018-04-23T16:13:18Z No. of bitstreams: 1 muller_mr_tese_lv.pdf: 4063915 bytes, checksum: 2bd34db37cecdea37a74a93bf0e348b5 (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-04-23T17:10:16Z (GMT) No. of bitstreams: 1 muller_mr_dr_ilha.pdf: 4063915 bytes, checksum: 2bd34db37cecdea37a74a93bf0e348b5 (MD5) / Made available in DSpace on 2018-04-23T17:10:16Z (GMT). No. of bitstreams: 1 muller_mr_dr_ilha.pdf: 4063915 bytes, checksum: 2bd34db37cecdea37a74a93bf0e348b5 (MD5) Previous issue date: 2018-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A maior inserção de tecnologias da informação nas redes de distribuição de energia elétrica vem permitindo que maiores volumes de dados de consumo sejam capturados em níveis cada vez mais detalhados, menos agregados e com maiores resoluções. Com a evolução dos mercados de energia elétrica, esses tipos de dados alcançam maior importância, uma vez que a comercialização de energia também passa a considerar estes níveis de consumo. Diversas técnicas têm sido aplicadas para previsão de cargas elétricas, como modelos estatísticos, de inteligência computacional e híbridos. Na literatura especializada é possível encontrar trabalhos que aplicam a rede neural artificial ARTMAP Fuzzy para tarefas de previsão de cargas elétricas, no entanto, a técnica ainda é pouco explorada em cenários de consumo menos agregados, e com maiores níveis de detalhe. Neste trabalho a rede ARTMAP Fuzzy é aplicada em tarefas de previsão multi-step de cargas elétricas reais com distintos níveis de agregação. Considerando o impacto do ruído sobre os previsores, sobretudo na capacidade de generalização das redes neurais artificiais, a técnica singular spectrum analysis é aplicada na tarefa de remoção de ruído. Os resultados de previsão permitiram analisar desempenho da rede ARTMAP Fuzzy, que foi comparada com outros dois previsores utilizados como benchmark, a saber, seasonal autoregressive integrated moving average e a rede neural multiLayer perceptron. A remoção de ruído permitiu melhora nos níveis de generalização da rede, impactando positivamente sua capacidade preditiva. / The increased insertion of information technologies in electricity distribution networks has allowed larger volumes of consumption data to be captured at increasingly detailed, less aggregated and higher resolution levels. With the evolution of electric energy markets, these types of data become more important, since the commercialization of energy also begins to consider these levels of consumption. Several techniques have been applied to predict electrical loads, such as statistical, computational intelligence and hybrids models. In the specialized literature it is possible to find works that apply the artificial neural network ARTMAP Fuzzy for tasks of prediction of electric charges, however, the technique is still little explored in less aggregated consumption scenarios, and with greater levels of detail. In this work the ARTMAP Fuzzy network is applied in multi-step forecasting tasks of real electric loads with different levels of aggregation. Considering the impact of noise on predictors, especially in the generalization capacity of artificial neural networks, the singular spectrum analysis technique is applied in the noise removal task. The prediction results allowed to analyze the performance of the ARTMAP Fuzzy network, which was compared with other two predictors used as benchmark, namely seasonal autoregressive integrated moving average and the multiLayer perceptron neural network. The noise removal allowed an improvement in the levels of network generalization, positively impacting its predictive capacity. / 1560734

The accurate estimation of travel time is valuable for a variety of transportation applications such as freeway performance evaluation and real-time traveler information. Given the extensive availability of traffic data collected by intelligent transportation systems, a variety of travel time estimation methods have been developed. Despite limited success under light traffic conditions, traditional corridor travel time prediction methods have suffered various drawbacks. First, most of these methods are developed based on data generated by dual-loop detectors that contain average spot speeds. However, single-loop detectors (and other devices that emulate its operation) are the most commonly used devices in traffic monitoring systems. There has not been a reliable methodology for travel time prediction based on data generated by such devices due to the lack of speed measurements. Moreover, the majority of existing studies focus on travel time estimation. Secondly, the effect of traffic progression along the freeway has not been considered in the travel time prediction process. Moreover, the impact of incidents on travel time estimates has not been effectively accounted for in existing studies.The objective of this dissertation is to develop a methodology for dynamic travel time prediction based on continuous data generated by single-loop detectors (and similar devices) and incident reports generated by the traffic monitoring system. This method involves multiple-step-ahead prediction for flow rate and occupancy in real time. A seasonal autoregressive integrated moving average (SARIMA) model is developed with an embedded adaptive predictor. This predictor adjusts the prediction error based on traffic data that becomes available every five minutes at each station. The impact of incidents is evaluated based on estimates of incident duration and the queue incurred.Tests and comparative analyses show that this method is able to capture the real-time characteristics of the traffic and provide more accurate travel time estimates particularly when incidents occur. The sensitivities of the models to the variations of the flow and occupancy data are analyzed and future research has been identified.The potential of this methodology in dealing with less than perfect data sources has been demonstrated. This provides good opportunity for the wide application of the proposed method since single-loop type detectors are most extensively installed in various intelligent transportation system deployments.

Les travaux décrits dans ce mémoire portent sur la synthèse du fragment C28-C46 de l'Hémicalide, un produit naturel extrait d'une éponge marine possédant une puissante activité antitumorale. Les étapes-clé de la synthèse sont : une addition conjuguée d'un boronate vinylique sur une lactone insaturée, une hydroxylation et une hydrogénation diastéréosélectives pour contrôler respectivement les centres C39, C40 et C42. La double liaison C34-C35 a été créée par une oléfination de Julia-Kocienski. Dans le cadre de ces travaux, une nouvelle méthode de synthèse diastéréosélective de delta-lactones alpha-hydroxylées a été mise au point mettant en jeu un réarrangement sigmatropique et une cyclisation catalysée par des complexes de métaux de transition. / The work described in this manuscript concerns the synthesis of the C28-C46 fragment of Hemicalide, a natural product extracted from a marine sponge exhibiting a highly potent antitumoral activity. The key steps of the synthesis are : a diastereoselective conjugate addition of a vinyl boroante on an unsaturated lactone, hydrogenation and hydrogenation to control the centers C39, C40 and C42, respectively. The C34-C35 double bond was created by a Julia-Kocienski olefination. During this work, a new method for the diastereoselective synthesis of delta-lactones was developed, involving a sigmatropic rearrangement and a cyclization catalyzed by transition metal complexes.

Biocatalysis is a field rapidly expanding to meet a demand for green and sustainable chemical processes. As the use of enzymes for synthetic chemistry becomes more common, the construction of multistep enzyme reactions is likely to become more prominent providing excellent cost and productivity benefits. However, the design and optimisation of multistep reactions can be challenging. An enzyme toolbox of well-characterised enzyme parts is critical for the design of novel multistep reactions. Furthermore, while whole-cell biocatalysis offers an excellent platform for multistep reactions, we are limited to the use of mesophilic host organisms such as Escherichia coli. The development of a thermophilic host organism would offer a powerful tool allowing whole-cell biocatalysis at elevated temperatures. This study aimed to investigate the construction of a multistep enzyme reaction from well-characterised enzyme parts, consisting of an esterase, a carboxylic acid reductase and an alcohol dehydrogenase. A novel thermostable esterase Af-Est2 was characterised both biochemically and structurally. The enzyme shows exceptional stability making it attractive for industrial biocatalysis, and features what is likely a structural or regulatory CoA molecule tightly bound near the active site. Five carboxylic acid reductases (CARs) taken from across the known CAR family were thoroughly characterised. Kinetic analysis of these enzymes with various substrates shows they have a broad but similar substrate specificity and that electron rich acids are favoured. The characterisation of these CARs seeks to provide specifications for their use as a biocatalyst. The use of isolated enzymes was investigated as an alternative to whole-cell biocatalysis for the multistep reaction. Additional enzymes for the regeneration of cofactors and removal of by-products were included, resulting in a seven enzyme reaction. Using characterised enzyme parts, a mechanistic mathematical model was constructed to aid in the understanding and optimisation of the reaction, demonstrating the power of this approach. Thermus thermophilus was identified as a promising candidate for use as a thermophilic host organism for whole-cell biocatalysis. Synthetic biology parts including a BioBricks vector, custom ribosome binding sites and characterised promoters were developed for this purpose. The expression of enzymes to complete the multistep enzyme reaction in T. thermophilus was successful, but native T. thermophilus enzymes prevented the biotransformation from being completed. In summary, this work makes a number of contributions to the enzyme toolbox of well-characterised enzymes, and investigates their combination into a multistep enzyme reaction both in vitro and in vivo using a novel thermophilic host organism.

The rapid growth of the world's urban population shows that people are increasingly moving to cities. In recent decades, the frequent occurrence of smog caused by increasing industrialization has brought environmental pollution to record highs. Therefore, the need to develop forecasting models about air quality occurs when the ambient air contains gasses, dust particles, smoke or odors in quantities large enough to be harmful to organic life. Accurate forecasts help people anticipate environmental conditions and act consequently to decrease dangerous pollution levels, reducing health impacts and associated costs. Rather than investigating deterministic models that attempt to simulate physical processes and develop complex mathematical simulations, this paper will focus on statistical methods, studying historical information and extracting information from data patterns. In looking for new reliable air quality forecasting methods, the goal was to develop and test an artifact based on the Transformer architecture, a novel technique initially developed for natural language processing tasks. Testing was performed against recurrent and convolutional, well-established deep-learning models successfully implemented in many applications, including time-series forecasting. Two different Transformer models were tested, one using time embeddings in the same manner as proposed in the original paper, while in the second model, the Time2Vec method has been adapted. The obtained results reveal that, even though not necessarily better than reference models, both Transformers could output accurate predictions and perform almost as well as recurrent and convolutional models.

Yes / Chemical manufacturing typically requires more than one step, involving multiple batch processes, which makes synthesis at scale laborious and wasteful. Herein, we demonstrate that several reactions can be telescoped into a single continuous process and/or be carried out as a continuous multi-component reaction (MCR), by twin screw extrusion (TSE), in the complete absence of solvent. / EPSRC (EP/L019655/1).

This thesis deals with different computational techniques related to some classes of nonlinear response regimes of engineering interest. The work is mainly divided into two parts. In the first part different numeric-analytic integration techniques for nonlinear oscillators are developed. In the second part, procedures for handling arbitrarily large rotations are addressed and a few novel developments are reported in the process. To begin the first part, we have proposed an explicit numeric-analytic technique, based on the Adomian decomposition method, for integrating strongly nonlinear oscillators. Numerical experiments suggest that this method, like most other numerical techniques, is versatile and can accurately solve strongly nonlinear and chaotic systems with relatively larger step-sizes. It is then demonstrated that the procedure may also be effectively employed for solving two-point boundary value problems with the help of a shooting algorithm. This has been followed up with the derivation and numerical exploration of variants of a recently developed numeric-analytic technique, the multi-step transversal linearization (MTrL), in the context of nonlinear oscillators of relevance in engineering dynamics. A considerable generalization and improvement over the original form of a MTrL strategy is achieved in this study. Finally, we have used the concept of MTrL method on the nonlinear variational (rate) equation corresponding to a nonlinear oscillator and thus derive another family of numeric-analytic techniques, presently referred to as the multi-step tangential linearization (MTnL). A comparison of relative errors through the MTrL and MTnL techniques consistently indicate a superior quality of approximation via the MTrL route. In the second part of the thesis, a scheme for numerical integration of rigid body rotation is proposed using only rudimentary tensor analysis. The equations of motion are rewritten in terms of rotation vectors lying in same tangent spaces, thereby facilitating vector space operations consistent with the underlying geometric structure of rotation. One of the most important findings of this part of the dissertation is that the existing constant-preserving algorithms are not necessarily accurate enough and may not be ideally applicable to cases wherein numerical accuracy is of primary importance. In contrast, the proposed rotation-algorithms, the higher order ones in particular, are significantly more accurate for conservative rotational systems for reasonably long time. Similar accuracy is expected for dissipative rotational systems as well. The operators relating rotation variables corresponding to different tangent spaces are also investigated and this should provide further insight into the understanding of rotation vector parametrization. A rotation update is next proposed in terms of rotation vectors. This update, employed along with interpolation of relative rotations, gives a strain-objective and path independent finite element implementation of a geometrically exact beam. The method has the computational advantage of requiring considerably less nodal variables due to the use of rotation vector parametrization. We have proposed a new isoparametric interpolation of nodal quaternions for computing the rotation field within an element. This should be a computationally efficient alternative to the interpolation of local rotations. It has been proved that the proposed interpolation of rotation leads to the objectivity of strain measures. Several numerical experiments are conducted to demonstrate the frame invariance, path-independence and other superior aspects of the present approach vis-`a-vis the existing methods based on the rotation vector parametrization. It is emphasized that, in order to develop an objective finite element formulation, the use of relative rotation is not mandatory and an interpolation of total rotation variables conforming with the rotation manifold should suffice.