A state-dependent linear recurrent formula with application to time series with structural breaks

An underlying assumption in Multivariate Singular Spectrum Analysis (MSSA) is that the time series are governed by a linear recurrent continuation. However, in the presence of a structural break, multiple series can be transferred from one homogeneous state to another over a comparatively short time breaking this assumption. As a consequence, forecasting performance can degrade significantly. In this paper, we propose a state-dependent model to incorporate the movement of states in the linear recurrent formula called a State-Dependent Multivariate SSA (SD-MSSA) model. The proposed model is examined for its reliability in the presence of a structural break by conducting an empirical analysis covering both synthetic and real data. Comparison with standard MSSA, BVAR, VAR and VECM models shows the proposed model outperforms all three models significantly.     

Modulation of protein kinases and phosphoprotein phosphatases by a small acidic protein from bovine brains

Summary A small, acidic and heat-stable protein was purified from bovine brains by column chromatography on DEAE-cellulose, Bio-Gel HTP, Affi-Gel phenothiazine and Sephadex G-75. This protein stimulates megamodulin-dependent protein kinase I from brains and phosphoprotein phosphatases from either brain or yeast. However, it inhibits cyclic AMP-dependent protein kinases from skeletal muscle.Acknowledgments. This work was supported by a grant (RR-08229) from the National Institutes of Health, USA. W.N. Kuo is a recipient of a Distinguished Faculty Scholar Award from United Negro College Fund, Inc., USA.     

Modulation of protein kinases and phosphoprotein phosphatases by a small acidic protein from bovine brains

A small, acidic and heat-stable protein was purified from bovine brains by column chromatography on DEAE-cellulose, Bio-Gel HTP, Affi-Gel phenothiazine and Sephadex G-75. This protein stimulates megamodulin-dependent protein kinase I from brains and phosphoprotein phosphatases from either brain or yeast. However, it inhibits cyclic AMP-dependent protein kinases from skeletal muscle.     

不同应变速率下Mg?B4C纳米复合材料的力学性能表征

Magnesium has wide application in industry. The main purpose of this investigation was to improve the properties of magnesium by reinforcing it using B₄C nanoparticles. The reinforced nanocomposites were fabricated using a powder compaction technique for 0, 1.5vol%, 3vol%, 5vol%, and 10vol% of B₄C. Powder compaction was conducted using a split Hopkinson bar (SHB), drop hammer (DH), and Instron to reach different compaction loading rates. The compressive stress–strain curves of the samples were captured from quasi-static and dynamic tests carried out using an Instron and split Hopkinson pressure bar, respectively. Results revealed that, to achieve the highest improvement in ultimate strength, the contents of B₄C were 1.5vol%, 3vol%, and 3vol% for Instron, DH, and SHB, respectively. These results also indicated that the effect of compaction type on the quasi-static strength of the samples was not as significant, although its effect on the dynamic strength of the samples was remarkable. The improvement in ultimate strength obtained from the quasi-static stress–strain curves of the samples (compared to pure Mg) varied from 9.9% for DH to 24% for SHB. The dynamic strength of the samples was improved (with respect to pure Mg) by 73%, 116%, and 141% for the specimens compacted by Instron, DH, and SHB, respectively. The improvement in strength was believed to be due to strengthening mechanisms, friction, adiabatic heating, and shock waves.