• Geomechanics and Engineering
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• v.36 no.5
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• 한국균학회소식:학술대회논문집
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• 2015.05a
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• pp.41-41
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• 2015

BSA-seq technologies, combined Bulked Segregant Analysis (BSA) and Next-Generation Sequencing (NGS), are making it faster and more efficient to establish the association of agronomic traits with molecular markers or candidate genes, which is the requirement for marker-assisted selection in molecular breeding. Clubroot disease, caused by Plasmodiophora brassicae, is a serious threat to Brassica crops. Even we have breed new clubroot resistant varieties of Chinese cabbage (B. rapa ssp. pekinesis), the underlying genetic mechanism is unclear. In this study, an $F_2$ population of 340 plants were inoculated with P. brassicae from Xinye (Pathotype 2 on the differentials of Williams). Resistance phenotype segregation ratio for the populations fit a 3:1 (R:S) segregation model, consistent with a single dominant gene model. Super-BSA, using re-sequencing the parents, extremely R and S DNA pools with each 50 plants, revealed 3 potential candidate regions on the chromosome A03, with the most significant region falling between 24.30 Mb and 24.75 Mb. A linkage map with 31 markers in this region was constructed with several closely linked markers identified. A Major QTL for clubroot resistance, CRq, which was identified with the peak LOD score at 169.3, explaining 89.9% of the phenotypic variation. And we developed a new co-segregated InDel marker BrQ-2. Joint BSA-seq and traditional QTL analysis delimited CRq to an 250 kb genomic region, where four TIR-NBS-LRR genes (Bra019409, Bra019410, Bra019412 and Bra019413) clustered. The CR gene CRq and closely linked markers will be highly useful for breeding new resistant Chinese cabbage cultivars.