Current genomic prediction (GP) models often fall short of fully capturing the genetic architecture of complex traits and providing practical breeding guidance, particularly under varying environments. Here, we propose the mmGEBLUP, an advanced GP scheme designed to tackle the current limitations in fully exploiting the genetic architecture of complex traits and to predict individual breeding value (BV) with multi-environment trial data. Our approach considers four genetic structural indicators to capture the genetic architectures stepwise across four models: the Genomic Best Linear Unbiased Prediction (GBLUP) model considers only main polygenic effects; the GEBLUP model includes both main and genotype-by-environment (GE) interaction polygenic effects; and the mmGBLUP and mmGEBLUP models further incorporate main and GE interaction effects of major genes. Through systematic simulations and applications to nine traits, three in rice and six in tobacco, we show stepwise increases in prediction accuracy from GBLUP to mmGEBLUP, providing evidence on the scale of heritability and polygenicity of traits. In practical terms, we predict four components of BV: major additive, minor additive, major interaction, and minor interaction. Interestingly, we discover that for traits like natural leaf number in tobacco, the major additive BVs for the top 20 individuals are substantially equal; it is the minor additive BV that causes the difference in the total BV. The relative size of major/minor additive BVs suggests performing either marker-assisted selection or genomic selection or both. Overall, mmGEBLUP is an advanced prediction scheme that enhances the understanding of genetic architectures and facilitate the genetic improvement of complex traits in crops under diverse environments.

As the wind turbine running in harsh environments, the vibration signal is often disturbed, and its own non-stationary characteristics, is not conducive to vibration signal analysis. The local mean decomposition (LMD) idea and its adaptive decomposition characteristics are introduced, and a de-noising method of vibration signal based on LMD is proposed. The denoising effect of this method is tested by AM / FM mixed simulation signal, and compared with the experiment based on dbl de-noising method and sym6 de-noising method. In addition, the de-noising effect based on the LMD method is further tested by the vibration experiment data of Case Western Reserve University, and the results are compared with those of the other two de-noising methods. The experimental results show that the de-noising method based on LMD can achieve the same denoising effect and purpose, with the de-noising methods based on dbl and based on sym6. The noise canceling effect of this proposed method is obviously better than that of the other two, and it is even better in signal-to-noise ratio (SNR) and root mean square error (RMSE). Moreover, the distortion after the signal de-noised by the proposed method is the least, which can be very good to maintain the original vibration information, with strong robustness.

By using dynamic reconfiguration, the flexibility, adaptability, and extensibility of distributed systems are enhanced. Treating interactions among components during dynamic reconfiguration is still one of the most crucial problems in this research area. This challenge should be met by middleware design and development. However, current standard middleware offers little support for dynamic reconfiguration of distributed systems. In this paper, we describe an extended Java RMI that supports efficiently dynamic reconfiguration of distributed systems. This extended Java RMI can automatically monitor and manipulate invocations between components during dynamic reconfiguration. Moreover, the extended Java RMI can automatically switch invocations from remote to local and vice versa.

The growing popularity of wired and wireless Internet requires distributed systems to be more flexible, adaptive and easily extensible. Dynamic reconfiguration of component-based distributed systems is one possible solution to meet these demands. However, there are some challenges for building dynamically reconfigurable distributed systems. Managing dependencies among components is one of the most crucial problems we have to solve before a system can be dynamically reconfigured at runtime. This paper describes a dependence management for dynamic reconfiguration of distributed systems. The dependence management analyzes not only the static dependencies among components, but also the dynamic dependencies that take place at runtime, in order to support an efficient consistent reconfiguration of distributed systems. In addition, the dependence management can deal with nested dependencies during a dynamic reconfiguration.