Filtering and Tracking with Trinion-Valued Adaptive Algorithms

A new model for three-dimensional processes based on the trinion algebra is introduced for the first time. Compared with the pure quaternion model, the trinion model is more compact and computationally more efficient, while having similar or comparable performance in terms of adaptive linear filtering. Moreover, the trinion model can effectively represent the general relationship of state evolution in Kalman filtering, where the pure quaternion model fails. Simulations on real-world wind recordings and synthetic data sets are provided to demonstrate the potentials of this new modeling method

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

Molecular dynamics simulations of low-energy Cl atoms etching Si(100) surface

In this study, molecular dynamics simulation method is used to investigate the interactions of Cl continuously bombarding a crystalline Si (100) surface in an incident energy range of 0.3-10 eV. The surface temperature is set to be 300 K for all the incident energies. The improved Tersoff-Brenner type potential is employed. The simulation results show that a Cl-rich reaction layer is formed on the surface due to Cl continuously bombarding. The SiCl group is the predominant species in the reaction layer. The thickness of the reaction layer increases with incident energy. The etching ratio increases with incident energy increasing. The main etching product is SiCl4 when the incident energies are 0.3, 1 and 5 eV, but it is SiClx(x < 4) when the incident enery is 10 eV. With the incident energy increasing, the main etching mechanism changes from chemical etching to physical etching

Molecular dynamics simulations of energy effects on atorn F interaction with SiC(100)

In this study, molecular dynamics simulations are used to investigate atom F interacting with SiC at 300 K. Simulation results show that with the saturation of the deposition of F atoms on the surface, the compositions (SiF(x) and CF(x) groups (x < 4)) in the reaction layer reach a steady state. When incident energy is less than 6 eV, no etching is observed. With incident energy increasing, the etching yields of Si and C atoms increase. It is found that Si atoms are preferentially removed. For etching products, SiF(4) is dominant. And the main etching mechanism of Si atoms is chemical etching