Local well-posedness and small Deborah limit of a molecule-based QQ-tensor system

In this paper, we consider a hydrodynamic $Q$-tensor system for nematic liquid crystal flow, which is derived from Doi-Onsager molecular theory by the Bingham closure. We first prove the existence and uniqueness of local strong solution. Furthermore, by taking Deborah number goes to zero and using the Hilbert expansion method, we present a rigorous derivation from the molecule-based $Q$-tensor theory to the Ericksen-Leslie theory.Comment: 44 page

Atmospheric non-thermal plasma discharges for cleaning and bio-decontamination

It has been shown that non-thermal plasma has great potential for chemical oxidation and bacterial inactivation. However, the mechanism of plasma-induced oxidation and bactericidal effects is not fully understood, and optimisation of the non-thermal plasma treatment is required to improve the efficiency of this technology. This research presents an investigation into the oxidation and bio-decontamination capabilities of steady-state corona discharges and impulsive transient plasma discharges in atmospheric air. Degree of decolorisation of blue dye by plasma discharges was obtained and used for evaluation of the oxidation efficiency of these discharges. The Gram-positive and Gram-negative bacteria, Staphylococcus aureus and Escherichia coli, respectively, were used for investigation of the bio-decontamination capability of the plasma discharges. It has been shown that conditions such as air humidity, electrode topology, and voltage levels may affect the efficiency of plasma treatment.;The obtained results show that the oxidation and inactivation effects depend on the amount of charge delivered by the plasma. The charge-dependent decolorisation and inactivation rates of plasma discharge treatment, which indicate the oxidation efficiency and inactivation efficiency, were obtained and analysed. Different decolorisation and inactivation rates were achieved with various electrode topologies and energisation polarities. This study also investigated the production of reactive species by atmospheric plasma discharges. Ozone concentration was measured during the decolorisation and inactivation tests. The production of OH radicals by the plasma discharges have also been obtained in this study using terephthalic acid as the chemical probe.;The obtained results confirm that the reactive oxygen species play a major role in the plasma discharge treatment. In addition, an attempt of using TiO2 as a catalyst to enhance oxidation and bio-decontamination effects of the plasma discharge treatment has been made. TiO2 was revealed to have the potential to improve the oxidation efficiency of atmospheric plasma discharges. The results obtained and presented in this thesis will help in optimisation of non-thermal plasma systems for chemical and biological decontamination.It has been shown that non-thermal plasma has great potential for chemical oxidation and bacterial inactivation. However, the mechanism of plasma-induced oxidation and bactericidal effects is not fully understood, and optimisation of the non-thermal plasma treatment is required to improve the efficiency of this technology. This research presents an investigation into the oxidation and bio-decontamination capabilities of steady-state corona discharges and impulsive transient plasma discharges in atmospheric air. Degree of decolorisation of blue dye by plasma discharges was obtained and used for evaluation of the oxidation efficiency of these discharges. The Gram-positive and Gram-negative bacteria, Staphylococcus aureus and Escherichia coli, respectively, were used for investigation of the bio-decontamination capability of the plasma discharges. It has been shown that conditions such as air humidity, electrode topology, and voltage levels may affect the efficiency of plasma treatment.;The obtained results show that the oxidation and inactivation effects depend on the amount of charge delivered by the plasma. The charge-dependent decolorisation and inactivation rates of plasma discharge treatment, which indicate the oxidation efficiency and inactivation efficiency, were obtained and analysed. Different decolorisation and inactivation rates were achieved with various electrode topologies and energisation polarities. This study also investigated the production of reactive species by atmospheric plasma discharges. Ozone concentration was measured during the decolorisation and inactivation tests. The production of OH radicals by the plasma discharges have also been obtained in this study using terephthalic acid as the chemical probe.;The obtained results confirm that the reactive oxygen species play a major role in the plasma discharge treatment. In addition, an attempt of using TiO2 as a catalyst to enhance oxidation and bio-decontamination effects of the plasma discharge treatment has been made. TiO2 was revealed to have the potential to improve the oxidation efficiency of atmospheric plasma discharges. The results obtained and presented in this thesis will help in optimisation of non-thermal plasma systems for chemical and biological decontamination

Investigation of elastoplastic ratchetting behavior of Stainless Steel 316 under cyclic uniaxial asymmetric loading at room temperature

This work investigates, both experimentally and computationally the cyclic behavior of stainless steel 316 under uniaxial loading at room temperature. Elastoplastic investigations of SS 316 are important in the development of an understanding of the possible behavior and their contribution to the material performance under cyclic loading. Cyclic plasticity can occur in an SS 316 component or structure depending on the loading conditions. Therefore, it is vital that the cyclic behavior of SS 316 is recognized and understood. In particular SS316 cylindrical rods specimens were tested under uniaxial cyclic loading. The experimental results show that ratchetting behavior regimes exist under the conditions presented. In order to simulate the experiments, an elastoplastic material model based on the Chaboche model is utilized in the commercial finite element (FE)-software ABAQUS. The Chaboche constitutive model utilized for cyclic loading, which includes nonlinear kinematic and isotropic hardening is discussed in detail. The kinematic and isotropic hardening parameters for the Chaboche model are also identified. The kinematic hardening parameters are calibrated using experimental data from the first half-cycle of loading, and the isotropic hardening parameters are defined by using cyclic experimental data from a test with symmetric strain (up to 1%). A 2D axisymmetric model is created in ABAQUS, where the same geometry, boundary conditions and loading cases are applied as those recorded experimentally. A mesh sensitivity study is also carried out. The error between simulation and experimental results is calculated and sources for the error are discussed. One strategy to decrease the numerical error is applied and evaluated. This work provides evidence that the Chaboche model can predict the cyclic behavior of stainless steel 316. However, there remain significant questions about the accuracy of the model parameters identified as they lead to errors in the predicted plastic strain at large cycle numbers. It is concluded that an improved method for calibrating the parameters or a more complex constitutive model is needed to better predict the cyclic behavior of stainless steel 316

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Improvements in autonomy offer the potential for positive outcomes in a number of domains, yet guaranteeing their safe deployment is difficult. This work investigates how humans can intelligently supervise agents to achieve some level of safety even when performance guarantees are elusive. The motivating research question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The paper formalizes this 'scaling supervision' problem, and investigates its application to the safety-critical context of autonomous vehicles (AVs) merging into traffic. It proposes a conservative, reachability-based method to reduce the burden on the AVs' human supervisors, which allows for the establishment of high-confidence upper bounds on the supervision requirements in this setting. Order statistics and traffic simulations with deep reinforcement learning show analytically and numerically that teaming of AVs enables supervision time sublinear in AV adoption. A key takeaway is that, despite present imperfections of AVs, supervision becomes more tractable as AVs are deployed en masse. While this work focuses on AVs, the scalable supervision framework is relevant to a broader array of autonomous control challenges.Comment: 14 pages, 7 figure

Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks

The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors. Adversaries may exploit vulnerabilities in the data collection or processing of navigation services to inject false information, and to thus interfere with the drivers' route selection. Such attacks can significantly increase traffic congestions, resulting in substantial waste of time and resources, and may even disrupt essential services that rely on road networks. To assess the threat posed by such attacks, we introduce a computational framework to find worst-case data-injection attacks against transportation networks. First, we devise an adversarial model with a threat actor who can manipulate drivers by increasing the travel times that they perceive on certain roads. Then, we employ hierarchical multi-agent reinforcement learning to find an approximate optimal adversarial strategy for data manipulation. We demonstrate the applicability of our approach through simulating attacks on the Sioux Falls, ND network topology

Distributionally Robust Optimization and Robust Statistics

We review distributionally robust optimization (DRO), a principled approach for constructing statistical estimators that hedge against the impact of deviations in the expected loss between the training and deployment environments. Many well-known estimators in statistics and machine learning (e.g. AdaBoost, LASSO, ridge regression, dropout training, etc.) are distributionally robust in a precise sense. We hope that by discussing the DRO interpretation of well-known estimators, statisticians who may not be too familiar with DRO may find a way to access the DRO literature through the bridge between classical results and their DRO equivalent formulation. On the other hand, the topic of robustness in statistics has a rich tradition associated with removing the impact of contamination. Thus, another objective of this paper is to clarify the difference between DRO and classical statistical robustness. As we will see, these are two fundamentally different philosophies leading to completely different types of estimators. In DRO, the statistician hedges against an environment shift that occurs after the decision is made; thus DRO estimators tend to be pessimistic in an adversarial setting, leading to a min-max type formulation. In classical robust statistics, the statistician seeks to correct contamination that occurred before a decision is made; thus robust statistical estimators tend to be optimistic leading to a min-min type formulation

Conditional Linear Regression

Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. However, there may not exist a good, simple model for the distribution, so we seek to find a small subset where there exists such a model. We give a computationally efficient algorithm with theoretical analysis for the conditional linear regression task, which is the joint task of identifying a significant portion of the data distribution, described by a k-DNF, along with a linear predictor on that portion with a small loss. In contrast to work in robust statistics on small subsets, our loss bounds do not feature a dependence on the density of the portion we fit, and compared to previous work on conditional linear regression, our algorithm’s running time scales polynomially with the sparsity of the linear predictor. We also demonstrate empirically that our algorithm can leverage this advantage to obtain a k-DNF with a better linear predictor in practice

A hybrid algorithm for quadratically constrained quadratic optimization problems

Quadratically Constrained Quadratic Programs (QCQPs) are an important class of optimization problems with diverse real-world applications. In this work, we propose a variational quantum algorithm for general QCQPs. By encoding the variables on the amplitude of a quantum state, the requirement of the qubit number scales logarithmically with the dimension of the variables, which makes our algorithm suitable for current quantum devices. Using the primal-dual interior-point method in classical optimization, we can deal with general quadratic constraints. Our numerical experiments on typical QCQP problems, including Max-Cut and optimal power flow problems, demonstrate a better performance of our hybrid algorithm over the classical counterparts.Comment: 8 pages, 3 figure

Street Stall Economy in China in the COVID-19 Era: Dilemmas and the International Experience of Promoting the Normalization of Street Stall Economy

Compared with those major policies that need to be practiced over the years, the street stall economy is more like a special means after the epidemic, resulting in a “short and brilliant” heat. Nevertheless, the street stall economy revives is facing several dilemmas. This paper reveals the dilemma of the prosperity and development of the stall economy before and after the epidemic, followed by the international experience and enlightenment of promoting the normalization of street stall economy, ranging from street vendor’s legal status and road administrative promotion to street food safety and environmental protection. To sum up, employment is the foundation of people’s livelihood and the source of wealth, hence, stall economy plays an indispensable role to create a win-win working world and promote the formation of a sustainable economic