Temperature, pressure, and adsorption dependent redox potentials: III. Processes of CO conversion to value-added compounds

Carbon monoxide (CO) is a primary air pollutant and a poisonous species for human beings, animals, and some catalytic reactions. Meanwhile, CO is also a versatile feedstock in the chemical industry to produce high‐value chemicals and clean fuels, which has stimulated extensive research interests in exploiting efficient CO conversion processes. Redox potential is a key thermodynamic quantity in these processes whereas only standard reduction potentials at 25°Cand 1 atm are currently available. Herein, it is the first time to report the effects of temperature (0–1000°C), pressure (1–100 atm), and adsorption on the redox potentials of 18 CO conversion reactions to form carbon dioxide, methane, straight‐chain alkanes (ethane, propane, and butane), light olefins(ethylene, propylene, and butylene), benzene, alcohols, aldehydes, acids, and dimethyl ether, based on theoretical calculations. It was noticed that gas‐phase, aqueous‐phase, and adsorption‐state redox potentials decrease with increasing temperature at an increased rate while they show different responses to pressure change. Namely, gas‐phase and most aqueous‐phase redox potentials increase with pressure at a gradually declined rate, while adsorption‐state redox potentials are not influenced by pressure. Most importantly, the significant differences up to 2.06 V under varied conditions highlight the necessity of applying operando redox potentials for CO conversion reactions

Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX

With the advent of big data era and the development of artificial intelligence and other technologies, data security and privacy protection have become more important. Recommendation systems have many applications in our society, but the model construction of recommendation systems is often inseparable from users' data. Especially for deep learning-based recommendation systems, due to the complexity of the model and the characteristics of deep learning itself, its training process not only requires long training time and abundant computational resources but also needs to use a large amount of user data, which poses a considerable challenge in terms of data security and privacy protection. How to train a distributed recommendation system while ensuring data security has become an urgent problem to be solved. In this paper, we implement two schemes, Horizontal Federated Learning and Secure Distributed Training, based on Intel SGX(Software Guard Extensions), an implementation of a trusted execution environment, and TensorFlow framework, to achieve secure, distributed recommendation system-based learning schemes in different scenarios. We experiment on the classical Deep Learning Recommendation Model (DLRM), which is a neural network-based machine learning model designed for personalization and recommendation, and the results show that our implementation introduces approximately no loss in model performance. The training speed is within acceptable limits.Comment: 5 pages, 8 figure

Creative Commons Quiz/Lecture notes/Lecture slides(group 22)

The resource set of info2009 coursework 2 is produced by group22. it contains: 1. poster 2. internet link of a set of multiple questions 3. a pdf file of a set of multiple questions 4. reference list 5. lecture slides 6. lecture notes ps: Edward Payne ([email protected]) has not contributed to any part of the activities

Fourier-transformed gauge theory models of three-dimensional topological orders with gapped boundaries

In this paper, we apply the method of Fourier transform and basis rewriting developed in arXiv:1910.13441 for the two-dimensional quantum double model of topological orders to the three-dimensional gauge theory model (with a gauge group $G$) of three-dimensional topological orders. We find that the gapped boundary condition of the gauge theory model is characterized by a Frobenius algebra in the representation category $\mathcal Rep(G)$ of $G$, which also describes the charge splitting and condensation on the boundary. We also show that our Fourier transform maps the three-dimensional gauge theory model with input data $G$ to the Walker-Wang model with input data $\mathcal Rep(G)$ on a trivalent lattice with dangling edges, after truncating the Hilbert space by projecting all dangling edges to the trivial representation of $G$. This Fourier transform also provides a systematic construction of the gapped boundary theory of the Walker-Wang model. This establishes a correspondence between two types of topological field theories: the extended Dijkgraaf-Witten and extended Crane-Yetter theories.Comment: 39 pages, 9 figure

Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain?

The trade tension between the U.S. and China since 2018 has caused a steady decoupling of the world's two largest economies. The pandemic outbreak in 2020 complicated this process and had numerous unanticipated repercussions. This paper investigates how U.S. importers reacted to the trade war and worldwide lockdowns due to the COVID-19 pandemic. We examine the effects of the two incidents on U.S. imports separately and collectively, with various economic scopes. Our findings uncover intricate trading dynamics among the U.S., China, and Southeast Asia, through which businesses relocated portions of their global supply chain away from China to avoid high tariffs. Our analysis indicates that increased tariffs cause the U.S. to import less from China. Meanwhile, Southeast Asian exporters have integrated more into value chains centered on Chinese suppliers by participating more in assembling and completing products. However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U.S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.Comment: 30 pages, 6 figure