The issue of optimal placement of wireless sensor networks (WSNs) is one of the major challenges for dynamic monitoring of bridges. It should be solved based on combining the effective monitoring of the dynamic performance of a bridge with the energy consumption of WSNs. Thus, the relationship between the bridge modal and the energy consumption of wireless networks is derived. Optimizing sensor location is achieved by using the improved wavelet particle swarm algorithm, which overcomes the disadvantage of the conventional particle swarm algorithm by applying mutation operator to the selected particles with certain small probability. Based on the comparison between the ANSYS simulation results and the measured bridge data, as well as the energy distribution diagram of WSNs, the proposed method is shown to reflect the dynamic performance of the test bridge well. Moreover, the method effectively controls the energy consumption of WSNs and achieves reasonable optimization effects. Therefore, this method can be used for dynamic monitoring of wireless sensors in various bridge types.

Background The Qinba region is the transition region between Indica and Japonica varieties in China. It has a long history of Indica rice planting of more than 7000 years and is also a planting area for fine-quality Indica rice. The aims of this study are to explore different genetic markers applied to the analysis population structure, genetic diversity, selection and optimization of molecular markers of Indica rice, thus providing more information for the protection and utilization on germplasm resources of Indica rice. Methods Fifteen phenotypic traits, a core set of 48 SSR markers which originated protocol for identification of rice varieties-SSR marker method in agricultural industry standard of the People's Republic of China (Ministry of Agriculture of the PRC, NY/T1433-2014, Protocol for identification of rice varieties-SSR marker method, 2014), and SNPs data obtained by genotyping-by-sequencing (GBS, NlaIII and MseI digestion, referred to as SNPs-NlaIII and SNPs-MseI, respectively) for this panel of 93 samples using the Illumina HiSeq2000 sequencing platform, were employed to explore the genetic diversity and population structure of 93 samples. Results The average of coefficient of variation (CV) and diversity index (He) were 29.72% and 1.83 ranging from 3.07% to 137.43%, and from 1.45 to 2.03, respectively. The correlation coefficient between 15 phenotypic traits ranged from 0.984 to -0.604. The first four PCs accounted for 70.693% phenotypic variation based on phenotypic analysis. A total of 379 alleles were obtained using SSR markers, encompassing an average of 8.0 alleles per primer. Polymorphic bands (PPB) and polymorphism information content (PIC) was 88.65% and 0.77, respectively. The Mantel test showed that the correlation between the genetic distance matrix based on SNPs-NlaIII and SNPs-MseI was the largest (R2=0.88), and that based on 15 phenotypic traits and SSR was the smallest (R2=0.09). The 93 samples could be clustered into two subgroups by 3 types of genetic markers. Molecular variance analysis revealed that the genetic variation was 2% among populations and 98% within populations (the Nm was 0.16), Tajima’s D value was 1.66, the FST between the two populations was 0.61 based on 72,824 SNPs. Conclusions The population genetic variation explained by SNPs was larger than that explained by SSRs. The gene flow of 93 samples used in this study was larger than that of naturally self-pollinated crops, which may be caused by long-term breeding selection of Indica rice in the Qinba region. The genetic structure of the 93 samples was simple and lacked rare alleles.

Ultrasonic guided waves detecting technology has promising application prospects in structural health monitoring. In order to detect defects in the aluminum sheet, a kind of defect localization imaging algorithm, combining multi-parameter compensation and pixel partitioning optimization is proposed in this paper. Based on the analysis of imaging principles, the waveform signal of time domain was mapped onto the wavenumber domain through a backward propagation compensation, so dispersion parameters can be compensated. Reference signal compensation can be achieved by the baseline signal differential method from wavenumber domain, which overcame influences of environmental changes. During the imaging process, a reasonable threshold was used for pixel partitioning and optimization to improve image quality. Experimental results demonstrated that positioning error about the algorithm is small, defects imaging of sheet is clear and intuitive, this optimization and compensation of guided-wave defects imaging can be used in structural health monitoring.

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