The evaluation of sequential optimization and reliability analysis

Sequential Optimization and Reliability Assessment (SORA) has been used for more than one decade for reliability-based design (RBD), but comprehensive theoretical studies on its performance have not been conducted. Further investigations on its performance are still needed. The objective of this thesis is to evaluate the performance of SORA for various testing problems. The performance of SORA evaluated in this thesis includes (1) accuracy, (2) efficiency, and (3) convergence behavior or robustness with numerical testing problems. SORA is evaluated with comparison with other major RBD methodologies. The testing problems are in different scales (numbers of design variables, random variables, and reliability constraints), with different distribution types (normal or non-normal distributions), and different nonlinearity of limit-state functions. This evaluation study focuses more on efficiency, which is measured by the number of limit-state function calls. The robustness of SORA is also improved by correcting a sign problem for strength-type random variables that are log-normally distributed. Through the thorough evaluation of SORA, this research helps a better understanding of SORA and other RBD methodologies, offers a better guidance for selecting RBD methodologies, and suggests possible ways for improving RBD --Abstract, page iii

Forecasting Financial Returns: A Copula-Based Method and a Robust Test

My dissertation includes two essays studying the forecasting of financial returns. In the first essay, I study the temporal dependence structures of financial returns by using a mixture copula model. A mixture copula is a linear combination of several single copulas. It is more flexible than a single copula and can capture various dependence structures in financial data. Therefore, instead of choosing a single copula based on certain statistical criteria, I propose to use a model average approach to estimate the temporal dependence structure of a stationary Markov process in a mixture copula framework. The asymptotic properties of the model average estimator are established under some regularity conditions. Simulations show that the model average approach gives the most accurate estimation and predicting results compared to some competing methods, when the working mixture model is misspecified. Using a real data example, we demonstrate the usefulness of our proposed method. In the second essay, I suggest a robust test that is a data-dependent weighted average of the regression-based test and the covariance-based test. This new test allows for multivariate cases and yields chi-squared inference regardless of whether predictors are stationary, local-to-unity or I(1). No prior knowledge of the orders of integration or bias corrections are required. Furthermore, the new test does not force the dependent variable and predictors to share the same order of integration under the alternative hypothesis. It is very important because in practice the dependent variable usually appears to be stationary while predictors may be (near) nonstationary. This test shows good simulation results. In the empirical application section, we test for the predictability of excess stock returns using a large set of predictors

The effect of mechanical processing of a mixture 80% Al2O3/20% Fe2O3 powders on the structure and electrochemical properties of lithium current sources

Пояснювальна записка: 87 стор., 21 рис., 23 таблиці, 55 джерел. Об’єкт дослідження – структурні та електрохімічні характеристики суміші порошків 80% Al2O3/20%. Метою роботи є дослідження впливу механоактивації на структуру та електрохімічні властивості нанокомпозиту 80% Al2O3/20% Fe2O3. Методи дослідження: ультрам'яка рентгенівська емісійна спектроскопія, рентгеноструктурний аналіз, растрова електронна мікроскопія, трансмісійна електронна мікроскопія, гальваностатичний аналіз. Сучасні технології вимагають створення нових матеріалів з найкращими електрохімічними та експлуатаційними властивостями, що відповідають критеріям технологічності, екологічності, дешевизни. Тому вивчення властивостей матеріалів з унікальними властивостями є безсумнівно актуальним. У цій роботі представлено дослідження впливу механоактивації суміші Al2O3 та α-Fe2O3 із співвідношенням компонентів 80/20% на структуру та електрохімічні властивості. Отриманими в роботі результатами встановлено, що зарядна ємність ЛПС з катодом на основі механоактивованої суміші підвищується за рахунок вколювання наночастинок Al2O3 в частинки α-Fe2O3 та збільшення поверхневих дефектів нанокопмозиту.Explanatory note: 87 pages, 21 figures, 23 tables, 55 sources. The object of the study – the structural and electrochamical charcteristics of the powder mixture 80% Al2O3/20% Fe2O3. The aim of the work is to investigate the influence of mechanoactivations on the morphology and electrochemical properties of 80% Al2O3/20% Fe2O3 nanocomposite. Methods of research: Ultra-soft X-ray emission spectroscopy, X-ray diffraction analysis, raster and transmission electronic microscopy, galvanostatic analysis. Modern technology requires the creation of new materials with the best electro-chemical and operational properties that meet the criteria of manufacturability, environmental friendliness, low cost. Therefore, the study of the properties of materials with unique features is undoubtedly relevant. This master thesis presents the study of influence of mechanoactivation Al2O3 and α-Fe2O3 mixture with a component ratio of 80/20% on the structure and electrochemical characteristics. From the results of investigation established that the charging capacity of the LPS with a cathode based on the mechanoactivated mixture increases due to the injection of Al2O3 nanoparticles into α-Fe2O3 nanoparticles and the increase in surface defects of the nanocomposite

An Integrated Gas Sensing System Based on Surface-Functionalized Gallium Nitride Nanowires with Embedded Micro-Heaters

In the last few decades, significant improvements have been made in gas sensor technologies. Metal-oxide sensors have been used for low-cost detection of combustible and toxic gases. However, hurdles relating to sensitivity, stability and selectivity still remain. Recently, nanotechnology has helped tremendously through the introduction of nano-engineered materials like nanowires and nanoclusters. Nanowire sensors have much better sensitivity as compared with thin-film devices due to the larger detecting surface-to-volume ratio. But clearly, improvements are still needed. For real-world applications, selectivity between different classes of compounds, such as combustible and toxic gases, is highly desirable. An ideal chemical sensor should distinguish between the individual analytes from a single class of compounds. For example, in detection of benzene or toluene, a good sensor will not be disturbed by other aromatic compounds present in the environment. This is a huge challenge for semiconductor based metal-oxide sensors, such as TiO2, SnO2, Fe2O3 and ZnO, which have inherent non-selective surface adsorption sites. Recently, a new class of nanowire-nanocluster (NWNC) based gas sensors has gained interest. This type of sensor represents a new method of functionalizing the surface for selective adsorption and detection. The adjustable sensitivity can be achieved by tuning the density, size or composition of the nanoparticles that decorate the nanowires. These advantages make the NWNC sensors a good alternative to conventional thin-film sensors. So far, research into NWNC sensors has demonstrated the potential in sensing many important classes of compounds. However, most of these NWNC devices require elevated working temperatures. They also have long response/recovery times and must function in an inert atmosphere. All these limitation will be the obstacles in real-world usage for domestic, environmental or industrial applications. And finally, the sensors thus developed must be manufacturable. That is, they must be batch fabricated with high yield. To remedy these problems, my thesis was divided into the following tasks, 1. Develop dry etching techniques to fabricate horizontally aligned GaN nanowires (NW), combining these techniques with wet etching treatment for surface damages removal. I call this a “top-down approach” using a subtractive process that fabricates NWs from thin-films and adding sensitive nanocrystals after the initial NW definition. This is to be compared to the additive “bottom-up” nanowire growth by MBE/HVPE/Sol-gel, in which NWs are grown, harvested from the growth surface and subsequently re-attached to a new surface. The top-down approach enhances the yield and homogeneity of the NW and it is mass-production oriented. 2. Study the metal-oxide nanoclusters (NCs) deposition method by physical vapor deposition (PVD) and rapid thermal annealing (RTA) for TiO2, SnO2, WO3, Fe2O3, etc. Develop the metal nanoparticle deposition method by PVD for Au, Ag, Pt, Pd, etc. 3. Study the crystalline phases and gas adsorption sites formed by the method and establish a database connecting metal-oxide bonding sites with different target chemicals. 4. Utilize Si doped n-type and unintentionally doped GaN nanowires functionalized with different metal-oxide and metal-oxide/metal composite nanoclusters to create a series of highly selective and sensitive gas sensing nanostructure devices. 5. Develop a low-cost micro-heater (MH) for local high temperature generation with low power consumption. This allows the rapid chemical desorption cycles as we anticipate frequently re-use or reset of the sensor. It also enables the use of these NWs in high temperature sensor applications. 6. Integrate the NW, NCs and MH into one working sensor, and integrate multiple types of gas sensors on a single chip. The chip can simultaneously sense many types of gases without interference. In this study, the potential of multicomponent NWNC based sensors for developing the next-generation of ultra-sensitive and highly selective chemical sensors was explored. We have achieved uA and nA levels of baseline detector current and we have shown that low UV illumination enhances sensitivity for some cases. These sensors have low power consumption making them suitable for portable devices

ASYMMETRIC ORGANOCATALYTIC HENRY REACTION OF β–SUBSTITUTED α-KETO ESTERS VIA DYNAMIC KINETIC RESOLUTION

Master'sMASTER OF SCIENC

Diverse CRISPR-Cas responses and dramatic cellular DNA changes and cell death in pKEF9-conjugated <i>Sulfolobus</i> species

The Sulfolobales host a unique family of crenarchaeal conjugative plasmids some of which undergo complex rearrangements intracellularly. Here we examined the conjugation cycle of pKEF9 in the recipient strain Sulfolobus islandicus REY15A. The plasmid conjugated and replicated rapidly generating high average copy numbers which led to strong growth retardation that was coincident with activation of CRISPR-Cas adaptation. Simultaneously, intracellular DNA was extensively degraded and this also occurred in a conjugated Δcas6 mutant lacking a CRISPR-Cas immune response. Furthermore, the integrated forms of pKEF9 in the donor Sulfolobus solfataricus P1 and recipient host were specifically corrupted by transposable orfB elements, indicative of a dual mechanism for inactivating free and integrated forms of the plasmid. In addition, the CRISPR locus of pKEF9 was progressively deleted when conjugated into the recipient strain. Factors influencing activation of CRISPR-Cas adaptation in the recipient strain are considered, including the first evidence for a possible priming effect in Sulfolobus. The 3-Mbp genome sequence of the donor P1 strain is presented

Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data

The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing research, we take a "signal" view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook "Like" is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes