Enantioselective Synthesis and Stereospecific Transformation of Alkylboronates:

Thesis advisor: James P. MorkenThesis advisor: Marc L. SnapperThis dissertation will present three projects focusing on the enantioselective synthesis and stereospecific transformation of alkylboronates. The first project describes the development of a nickel-catalyzed enantioselective dicarbofunctionalization of alkenylboronates, which provides a modular route to secondary alkylboronic esters. Intramolecular reaction leads to enantioselective synthesis of exocyclic boronates. The second project depicts a new method for the synthesis of azetidines, pyrrolidines and piperidines via an intramolecular amination of alkylboronic esters. Regioselective amination of vicinal bis(boronates) allows the synthesis of saturated azacycles bearing boronic ester substitutions that can serve as useful synthetic handles. As the transformation is stereospecific, stereodefined cyclic amines can be synthesized from the enantioenriched boronic esters. The method is applied to the synthesis of an intermediate towards a Kras G12C inhibitor. The third project describes the development of a new chiral auxiliary on boron that can be easily synthesized from inexpensive starting materials. The auxiliary is applied to a diastereoselective radical ring-opening/closing [3+2] cycloaddition of cyclopropylanilines with alkenylboron species.Thesis (PhD) — Boston College, 2022.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Chemistry

Multi-frequency RCS Reduction Characteristics of Shape Stealth with MLFMA with Improved MMN

AbstractThree new control factors are presented for calculating the multipole mode number (MMN) efficiently and precisely. The effects of these control factors on the number of integral samples and the precision of multilevel fast multipole algorithm (MLFMA) are investigated. A new approach based on control factors which is proven to be able to improve the computational efficiency and reduce the needed memory significantly as well as ensuring the proper precision. For three aircraft models, the improved MLFMA is employed to analyze their multi-frequency scattering characteristics. It is found that aircraft shape can influence radar cross section (RCS) in different frequency zones. Both the multi-frequency RCS reduction characteristics of shape stealth aircraft and the conventional aircraft with stealth design taken into account are investigated, and the results show that shape stealth exhibits significant RCS reduction in the resonance and high-frequency zones, and with a weaker influence in the Rayleigh zone. Compared with radar absorbing material (RAM), shape stealth yields a wider multi-frequency RCS reduction. The above-mentioned results can be applied to stealth design for multiple frequencies or even for all frequencies

Multi-scale Attention Flow for Probabilistic Time Series Forecasting

The probability prediction of multivariate time series is a notoriously challenging but practical task. On the one hand, the challenge is how to effectively capture the cross-series correlations between interacting time series, to achieve accurate distribution modeling. On the other hand, we should consider how to capture the contextual information within time series more accurately to model multivariate temporal dynamics of time series. In this work, we proposed a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF), where we integrate multi-scale attention and relative position information and the multivariate data distribution is represented by the conditioned normalizing flow. Additionally, compared with autoregressive modeling methods, our model avoids the influence of cumulative error and does not increase the time complexity. Extensive experiments demonstrate that our model achieves state-of-the-art performance on many popular multivariate datasets

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation, and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from the source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.Comment: 8 pages, 4 figures, AAAI 202

Impacts of CaO solid particles in carbon dioxide absorption process from ship emission with NaOH solution

CO2 emitted from ship exhaust is one of the major sources of atmospheric pollution. In order to reduce ship CO2 emissions, this paper comes up with the idea of recovering CO2 from ship exhaust by NaOH solution and improves the absorption rate by adding CaO solid particles. The effect mechanism of CaO solid particles on CO2 absorption efficiency is analyzed in detail, and the mathematical model is deduced and the CaO enhancement factor is calculated through experiments. Experiment result demonstrates that the effect of CaO solid particles on the absorption of CO2 in alkali solution is significant. The absorption rate of pure CO2 gas, the simulated ship exhaust gas and 6135AZG marine diesel engine emission can be increased by 10%, 15.85% and 10.30%, respectively. So it can be seen that CaO solid particles play an important role in improving the absorption efficiency of ship CO2 emission