322 research outputs found
Development of a Transferable Reactive Force Field of P/H Systems: Application to the Chemical and Mechanical Properties of Phosphorene
ReaxFF provides a method to model reactive chemical systems in large-scale
molecular dynamics simulations. Here, we developed ReaxFF parameters for
phosphorus and hydrogen to give a good description of the chemical and
mechanical properties of pristine and defected black phosphorene. ReaxFF for
P/H is transferable to a wide range of phosphorus and hydrogen containing
systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated
phosphorene, phosphorus clusters and phosphorus hydride molecules. The
potential parameters were obtained by conducting unbiased global optimization
with respect to a set of reference data generated by extensive ab initio
calculations. We extend ReaxFF by adding a 60{\deg} correction term which
significantly improves the description of phosphorus clusters. Emphasis has
been put on obtaining a good description of mechanical response of black
phosphorene with different types of defects. Compared to nonreactive SW
potential [1], ReaxFF for P/H systems provides a huge improvement in describing
the mechanical properties the pristine and defected black phosphorene and the
thermal stability of phosphorene nanotubes. A counterintuitive phenomenon is
observed that single vacancies weaken the black phosphorene more than double
vacancies with higher formation energy. Our results also show that mechanical
response of black phosphorene is more sensitive to defects for the zigzag
direction than for the armchair direction. Since ReaxFF allows straightforward
extensions to the heterogeneous systems, such as oxides, nitrides, ReaxFF
parameters for P/H systems build a solid foundation for the reactive force
field description of heterogeneous P systems, including P-containing 2D van der
Waals heterostructures, oxides, etc
Hopf bifurcation and stability analysis of flexible rotor-bearing system
Analytical model of a long bearing was used to study the self-excited vibration of a single disc flexible rotor-bearing system on sliding bearing support. A shooting method was applied to track and acquire periodic solution of flexible rotor system after the Hopf bifurcation. Stability of periodic solution was analyzed on the basis of Floquet theory. Gas film eddying, oscillation and other nonlinear features were considered. High-speed air hybrid bearing test-bed was used to verify gas film oscillation arising from coupling between natural frequency and gas film eddying frequency. The “bounded” nature of chaotic vibration and the process of rubbing caused by instability of air film were observed. Finally, a distinguishing criterion named “practical stability” was provided
Detecting and quantifying natural selection at two linked loci from time series data of allele frequencies with forward-in-time simulations
Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modeling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses
Can ChatGPT reduce human financial analysts’ optimistic biases?
This paper examines the potential of ChatGPT, a large language model, as a financial advisor for listed firm performance forecasts. We focus on the constituent stocks of the China Securities Index 300 and compare ChatGPT’s forecasts for major financial performance measures with human analysts’ forecasts and the realised values. Our findings suggest that ChatGPT can correct the optimistic biases of human analysts. This study contributes to the literature by exploring the potential of ChatGPT as a financial advisor and demonstrating its role in reducing human biases in financial decision-making
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