Electrochemical reduction of CO2 to methanol

An efficient method to convert CO2 to fuels using renewable energy could displace crude oil without increasing CO2 emission and provide high-density energy storage reservoirs similar to liquid fuels or batteries. Although photoelectrochemical conversion of CO2 is possible, solar-to-fuel efficiencies are lower than the combination of conventional photovoltaics (up to 40% efficiency) and electrochemical cells (up to 80% Faradaic efficiency). In the electrochemical case, electrical energy from renewable sources may be converted to hydrocarbons or alcohols using electrocatalysts. The direct reduction of CO2 to CH3OH is known to occur at several types of electrocatalysts including oxidized Cu electrodes. In this thesis, we first examine the yield behavior of an electrodeposited cuprous oxide thin film and explore relationships between surface chemistry and reaction behavior relative to air-oxidized and anodized Cu electrodes. CH3OH yields and Faradaic efficiencies observed at cuprous oxide electrodes are remarkably higher than air-oxidized or anodized Cu electrodes suggesting Cu(I) species may play a critical role in selectivity to CH3OH. Experimental results also show CH3OH yields are dynamic and the copper oxides are reduced to metallic Cu in a simultaneous process. In order to improve CH3OH activity and electrode surface’s stability, single crystal ZnO (10-10) is considered as a support since ZnO support are well known for methanol synthesis in an industrial hydrogenation reaction scale. Although experimental conditions pose challenging barriers to repeatability, Infrared Spectroscopy and yields suggest the oxide may provide stable surfaces with selectivity to CH3OH. Yield behavior is discussed in comparison with photoelectrochemical and hydrogenation reactions where the improved stability of Cu(I) species may allow relatively constant CH3OH generation

Electroweak phase transition in the economical 3-3-1 model

We consider the EWPT in the economical 3-3-1 (E331) model. Our analysis shows that the EWPT in the model is a sequence of two first-order phase transitions, $SU(3) \rightarrow SU(2)$ at the TeV scale and $SU(2) \rightarrow U(1)$ at the $100$ GeV scale. The EWPT $SU(3) \rightarrow SU(2)$ is triggered by the new bosons and the exotic quarks; its strength is about $1 - 13$ if the mass ranges of these new particles are $10^2 \,\mathrm{GeV} - 10^3 \,\mathrm{GeV}$. The EWPT $SU(2) \rightarrow U(1)$ is strengthened by only the new bosons; its strength is about $1 - 1.15$ if the mass parts of $H^0_1$, $H^\pm_2$ and $Y^\pm$ are in the ranges $10 \,\mathrm{GeV} - 10^2 \,\mathrm{GeV}$. The contributions of $H^0_1$ and $H^{\pm}_2$ to the strengths of both EWPTs may make them sufficiently strong to provide large deviations from thermal equilibrium and B violation necessary for baryogenesis.Comment: 17 pages, 9 figure

Strong efficiency improvement of SOI-LEDs through carrier confinement

Contemporary silicon light-emitting diodes in silicon-on-insulator (SOI) technology suffer from poor efficiency compared to their bulk-silicon counterparts. In this letter, we present a new device structure where the carrier injection takes place through silicon slabs of only a few nanometer thick. Its external quantum efficiency of 1.4 • 10−4 at room temperature, with a spectrum peaking at 1130 nm, is almost two orders higher than reported thus far on SOI. The structure diminishes the dominant role of nonradiative recombination at the n+ and p+contacts, by confining the injected carriers in an SOI peninsula.\ud With this approach, a compact infrared light source can be fabricated using standard semiconductor processing steps.\u

Autonomous Path Planning Under Cyber Attacks

Autonomous path planning is an important application in the field of robotics and control systems engineering, especially with the rapid development and commercialization of self-driving cars and unmanned autonomous vehicles. Some of the biggest concerns in these cyber-physical systems are safety and security. Many different attacks can be and have been conducted on either the physical or cyber layer of these systems, and these attacks can cause devastating consequences. In this project, the team investigates techniques for planning and following a trajectory in the presence of an adversary who spoofs one or more sensors on a wheeled robot platform. The robot platform that the team uses is a Turtlebot 3 Burger controlled by an LQG controller

WEBCAM-BASED LASER DOT DETECTION TECHNIQUE IN COMPUTER REMOTE CONTROL

ABSTRACTIn this paper, the authors propose a method to detect the laser dot in an interactive system using laser pointers. The method is designed for presenters who need to interact with the computer during the presentation by using the laserpointer. The detection technique is developed by using a camera to capture the presentation screen and processing every frames transferred to the ara computer. This paper focuses on the detection and tracking of laser dots, based on their characteristics to distinguish a laser dotfrom other areas on the captured frames. Experimental results showed that the proposed method could reduce the rate of misdetection by light noises of a factor of 10 and achieve an average accuracy of 82% of detection in normal presentation environments. The results point out that the better way to describe the laser dots’ features based on visual concept is to use the HSI color space instead of the normal RGB space.Keywords.  laser pointer; laser dot/spot; laser pointer interaction; control; mouse; computer screen/display

VNINDEX INDEX: A VIEW FROM QUANTITATIVE ANALYSIS

The financial strategy towards 2030 in Decision no. 368/QD-TTg enacted on 21/3/2022 by the Vietnam Prime Minister had set developmental goals for the stock market to become a channel to promote mid-and-long term capital raising for the economy, to continue changing the structure to be more appropriate for the monetary, the capital, the stock, and the bonds market. In the past two years, the stock market in Vietnam has manifested impressive growth. A prime example is the active growth of private investors. However, many challenges and loopholes still exist in the stock market. The stock market is a special market as the fluctuation of the VN-index index is affected by many factors including the expectations of the investor. When the market shows trends of increases in points then investors expect the next session to grow as well, leading to a rise in sales even for stocks with inflating costs, thus the VNindex will increase and vice versa. The structure of the econometric is utilized to assess the impacts of the VN-index of the previous month on the index of the following month on the Vietnamese stock market in the period from January 2019 to May 2022. The conclusion of the quantitative analysis shows that if the VN-index of the previous month increases by 1% then the VNindex of the following month will increase by 0.965% or vice versa. From the results of the analysis, the research team has proposed a few discussion points intending to stabilize the stock market in the upcoming time and develop the market according to the financial strategy towards 2030 in Decision no. 368/QD-TTg by the Vietnam Prime Ministe

New Frameworks for Structured Policy Learning

Sequential decision making applications are playing an increasingly important role in everyday life. Research interest in machine learning approaches to sequential decision making has surged thanks to recent empirical successes of reinforcement learning and imitation learning techniques, partly fueled by recent advances in deep learning-based function approximation. However in many real-world sequential decision making applications, relying purely on black box policy learning is often insufficient, due to practical requirements of data efficiency, interpretability, safety guarantees, etc. These challenges collectively make it difficult for many existing policy learning methods to find success in realistic applications. In this dissertation, we present recent advances in structured policy learning, which are new machine learning frameworks that integrate policy learning with principled notions of domain knowledge, which spans value-based, policy-based, and model-based structures. Our framework takes flexible reduction-style approaches that can integrate structure with reinforcement learning, imitation learning and robust control techniques. In addition to methodological advances, we demonstrate several successful applications of the new policy learning frameworks.</p

Bearing-Based Network Localization Under Gossip Protocol

This paper proposes a bearing-based network localization algorithm with a randomized gossip protocol. Each sensor node is assumed to be able to obtain the bearing vectors and communicate its position estimates with several neighboring agents. Each update involves two agents, and the update sequence follows a stochastic process. Under the assumption that the network is infinitesimally bearing rigid and contains at least two beacon nodes, we show that the proposed algorithm could successfully estimate the actual positions of the network in probability. The randomized update protocol provides a simple, distributed, and reduces the communication cost of the network. The theoretical result is then supported by a simulation of a 1089-node sensor network.Comment: preprint, 7 pages, 2 figure