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Background Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is a major threat to wheat production and food security worldwide. Breeding stably and durably resistant cultivars is the most effective approach for managing and controlling the disease. The success of FHB resistance breeding relies on identification of an effective resistant germplasm. We conducted a genome-wide association study (GWAS) using the high-density wheat 90 K single nucleotide polymorphism (SNP) assays to better understand the genetic basis of FHB resistance in natural population and identify associated molecular markers. Results The resistance to FHB fungal spread along the rachis (Type II resistance) was evaluated on 171 wheat cultivars in the 2016–2017 (abbr. as 2017) and 2017–2018 (abbr. as 2018) growing seasons. Using Illumina Infinum iSelect 90 K SNP genotyping data, a genome-wide association study (GWAS) identified 26 loci (88 marker-trait associations), which explained 6.65–14.18% of the phenotypic variances. The associated loci distributed across all chromosomes except 2D, 6A, 6D and 7D, with those on chromosomes 1B, 4A, 5D and 7A being detected in both years. New loci for Type II resistance were found on syntenic genomic regions of chromsome 4AL (QFhb-4AL, 621.85–622.24 Mb) and chromosome 5DL (QFhb-5DL, 546.09–547.27 Mb) which showed high collinearity in gene content and order. SNP markers wsnp_JD_c4438_5568170 and wsnp_CAP11_c209_198467 of 5D, reported previously linked to a soil-borne wheat mosaic virus (SBWMV) resistance gene, were also associated with FHB resistance in this study. Conclusion The syntenic FHB resistant loci and associated SNP markers identified in this study are valuable for FHB resistance breeding via marker-assisted selection.

Information entropy and fuzzy analytical theory were combined and applied on the state assessment of power plant steam turbine unit. This method can be combined with the measured data of power plant, and made a comprehensive evaluation of the different working conditions. Fuzzy analysis of the original matrix can be a evaluation matrix, and the information entropy was introduced to solve the problem of calculating the weight of multi attribute factor set. Once the attribute weight is determined, the comprehensive evaluation value corresponding to the different working conditions can be defined and calculated. The results obtained by this method were compared with those obtained by the principal component analysis method. And the results obtained by information entropy method were objective and unique. The superiority of the method was verified. Under all conditions, the comprehensive evaluation value can be used as one index of the thermal archives as a benchmark for fault diagnosis, which provided a new idea and evaluation method for steam turbine fault diagnosis, and had a strong basic research and practical application value.

Based on big data analysis, for the actual operating data, the method of dividing non-uniform working conditions was studied to obtain typical data of different working conditions. The traditional sliding pressure curve is only a shortcoming of the single value function of the load. Convolution neural network is applied to build a nonlinear model of load, main steam temperature, reheated steam temperature, ambient temperature and main steam pressure. And then, the real-time optimized value of the main steam pressure is obtained. After verification, the model is proved to be satisfactory in both precision and regularity. Finally, the main steam pressure optimization model is applied to the actual 300MW thermal power unit, and closed-loop control is performed. The results prove that the application of real-time main steam pressure optimization can effectively reduce the energy consumption of the unit, and have higher energy saving potential at different ambient temperatures.

The use of biomass-derived fuels as an alternative energy has become a key solution to solve fossil fuels shortage and reduce greenhouse gas emission. With the development on biomass energy conversion, biofuels show great improvement in product yield, fuel quality and combustion characteristics, making it possible for gas turbine applications. The stable combustion flame of the gas turbine makes it adaptable to various biofuels. The main research of utilizing biomass-derived fuels in gas turbine in recent years was summarized. Characteristics and combustion feasibility of typical biofuels were discussed including both liquid phase biofuels and gas phase biofuels. The combustion performance, pollutant emission and combustion optimization of biofuels combustion in gas turbines was analysed. The main problems and effective solutions for biofuel applications were summarized, providing guidance for clean and efficient use of biofuels in gas turbines.