Quantum Uncertainty Equalities and Inequalities for Unitary Operators

We explore the uncertainty relation for unitary operators in a new way and find two uncertainty equalities for unitary operators, which are minimized by any pure states. Additionally, we derive two sets of uncertainty inequalities that unveil hierarchical structures within the realm of unitary operator uncertainty. Furthermore, we examine and compare our method for unitary uncertainty relations to other prevailing formulations. We provide explicit examples for better understanding and clarity. Results show that the hierarchical unitary uncertainty relations establish strong bounds. Moreover, we investigate the higher-dimensional limit of the unitary uncertainty equalities.Comment: 15 pages, 4 figure

Trapezoidal Current Modulation for Bidirectional High-Step-Ratio Modular DC–DC Converters

Modular dc-dc converter (MDCC) has been proposed for high step-ratio interconnection in dc grid applications. To further optimize the performance of MDCC, this paper presents a trapezoidal current modulation with bidirectional power flow ability. By giving all the sub-module (SM) capacitors an equal duty to withstand the stack dc voltage, their voltages are balanced without additional feedback control. Moreover, based on soft-switching performance and circulating current analysis, three-level and two-level operation modes featured with high efficiency conversion and large power transmission, respectively, are introduced. The control schemes of both modes are designed to minimize the conduction losses. Besides, the SM capacitor voltage ripples with different switching patterns are compared and the option for ripple minimization is presented. A full-scale case study is provided to introduce the design process and device selection of the MDCC. The experimental tests based on a downscaled prototype are finally presented to validate the theoretical analysis

Higher-order Graph Attention Network for Stock Selection with Joint Analysis

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasetsComment: 12 pages, 6 figures

Catalytic Asymmetric Dihydroxylation of Olefins Using a Recoverable and Reusable Ligand

A free bis-cinchona alkaloid derivative ligand was prepared by a simple synthetic manipulation. With ligand/olefin mole ratio of 1%, the asymmetric dihydroxylation reactions of six olefins proceeded smoothly to give the chiral vicinal diols in high chemical yields and optical yields. The ligand itself could be recovered quantitatively by a simple operation and reused five times without loss of enantioselectivity

Displaying and delivering viral membrane antigens via WW domain–activated extracellular vesicles

Membrane proteins expressed on the surface of enveloped viruses are conformational antigens readily recognized by B cells of the immune system. An effective vaccine would require the synthesis and delivery of these native conformational antigens in lipid membranes that preserve specific epitope structures. We have created an extracellular vesicle–based technology that allows viral membrane antigens to be selectively recruited onto the surface of WW domain–activated extracellular vesicles (WAEVs). Budding of WAEVs requires secretory carrier-associated membrane protein 3, which through its proline-proline-alanine-tyrosine motif interacts with WW domains to recruit fused viral membrane antigens onto WAEVs. Immunization with influenza and HIV viral membrane proteins displayed on WAEVs elicits production of virus-specific neutralizing antibodies and, in the case of influenza antigens, protects mice from the lethal viral infection. WAEVs thus represent a versatile platform for presenting and delivering membrane antigens as vaccines against influenza, HIV, and potentially many other viral pathogens

Displaying and delivering viral membrane antigens via WW domain–activated extracellular vesicles

Membrane proteins expressed on the surface of enveloped viruses are conformational antigens readily recognized by B cells of the immune system. An effective vaccine would require the synthesis and delivery of these native conformational antigens in lipid membranes that preserve specific epitope structures. We have created an extracellular vesicle–based technology that allows viral membrane antigens to be selectively recruited onto the surface of WW domain–activated extracellular vesicles (WAEVs). Budding of WAEVs requires secretory carrier-associated membrane protein 3, which through its proline-proline-alanine-tyrosine motif interacts with WW domains to recruit fused viral membrane antigens onto WAEVs. Immunization with influenza and HIV viral membrane proteins displayed on WAEVs elicits production of virus-specific neutralizing antibodies and, in the case of influenza antigens, protects mice from the lethal viral infection. WAEVs thus represent a versatile platform for presenting and delivering membrane antigens as vaccines against influenza, HIV, and potentially many other viral pathogens