Estimating Behavioural Heterogeneity Under Regime Switching

Financial markets are typically characterized by high (low) price level and low (high) volatility during boom (bust) periods, suggesting that price and volatility tend to move together with different market conditions/states. By proposing a simple heterogeneous agent model of fundamentalists and chartists with Markov chain regime-dependent expectations and applying S&P500 data from January 2000 to June 2010, we show that the estimation of the model matches well with the boom and bust periods in the US stock market. In addition, we find evidence of time-varying behavioural heterogeneity within-group and that the model exhibits good forecasting accuracy.estimation; heterogeneity; regime switching; boom and bust

Mechanism of Surface Faulting and its Seismic Effect

The mechanism and reoccurrence rule of surface faulting have been analyzed based on macroscopic inspection and testing in the background of Tangshan earthquake 1976 and other events. It has been proved that such surface faulting bas only limited effect on surface structures nearby. And most of the surface ruptures in soil layer occurred in most earthquake zones are not the causative faults from focus up to the ground surface. Thus its seismic effect should be reestimated

GRB 120729A: External Shock Origin for Both the Prompt Gamma-Ray Emission and Afterglow

Gamma-ray burst (GRB) 120729A was detected by Swift/BAT and Fermi/GBM, and then rapidly observed by Swift/XRT, Swift/UVOT, and ground-based telescopes. It had a single long and smooth \gamma-ray emission pulse, which extends continuously to the X-rays. We report Lick/KAIT observations of the source, and make temporal and spectral joint fits of the multiwavelength light curves of GRB 120729A. It exhibits achromatic light-curve behavior, consistent with the predictions of the external shock model. The light curves are decomposed into four typical phases: onset bump (Phase I), normal decay (Phase II), shallow decay (Phase III), and post-jet break (Phase IV). The spectral energy distribution (SED) evolves from prompt \gamma-ray emission to the afterglow with photon index from Γγ=1.36 to Γ≈1.75. There is no obvious evolution of the SED during the afterglow. ...(Please see article full tet for complete abstract.

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter for important model inputs and a new reduced-form model (RFM) that couples the high-order decoupled direct method (HDDM) and the stochastic response surface model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM2.5 simulations in the Pearl River Delta (PRD) region and found five precursor emissions, two pollutants in lateral boundary conditions (LBCs), and three meteorological inputs out of 203 model inputs to be important model inputs based on sensitivity analysis. Among these selected inputs, primary PM2.5 emissions, PM2.5 concentrations of LBCs, and wind speed were identified as key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM2.5 simulations, which can be attributed to the two-product method that describes the formation of secondary organic aerosol

Deep Charge: A Deep Learning Model of Electron Density from One-Shot Density Functional Theory Calculation

Electron charge density is a fundamental physical quantity, determining various properties of matter. In this study, we have proposed a deep-learning model for accurate charge density prediction. Our model naturally preserves physical symmetries and can be effectively trained from one-shot density functional theory calculation toward high accuracy. It captures detailed atomic environment information, ensuring accurate predictions of charge density across bulk, surface, molecules, and amorphous structures. This implementation exhibits excellent scalability and provides efficient analyses of material properties in large-scale condensed matter systems

Gut microbiota regulates K/BxN autoimmune arthritis through Tfh but not Th17 cells

The bacterial community that colonizes mucosal surfaces helps shape the development and function of the immune system. The K/BxN autoimmune arthritis model is dependent on the microbiota, and particularly on segmented filamentous bacteria, for the autoimmune phenotype. The mechanisms of how the gut microbiota affects arthritis development are not well understood. In this study, we investigate the contribution of two T cell subsets, Th17 and follicular helper T (Tfh), to arthritis and how microbiota modulates their differentiation. Using genetic approaches, we demonstrate that IL-17 is dispensable for arthritis. Antibiotic treatment inhibits disease in IL-17-deficient animals, suggesting that the gut microbiota regulates arthritis independent of Th17 cells. In contrast, conditional deletion of Bcl6 in T cells blocks Tfh cell differentiation and arthritis development. Furthermore, Tfh cell differentiation is defective in antibiotic-treated mice. Taken together, we conclude that gut microbiota regulates arthritis through Tfh but not Th17 cells. These findings have implications in our understanding of how environmental factors contribute to the development of autoimmune diseases