Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning.

Accurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall thermal load of a box structure. To determine the location and magnitude of the thermal load accurately, the proposed method segments a structure into several subregions and applies a cascade of deep learning models to gradually reduce the solution domain.

[Genetic polymorphisms of 30 InDel loci in Chinese ethnic population residing in Tibet].

Objective: To analyze the genetic data of 30 insertion and deletion polymorphisms (InDel) loci included in an InvestigatorR DIPplex diagnostic kit, and to evaluate the forensic application in ethnic Tibetan population from China.

Methods: By detecting 226 unrelated individuals with the Investigator(R) DIPplex kit, allelic frequencies and population genetics parameters of the 30 InDels were statistically analyzed and compared with available data derived from other populations from various regions.

Results: After the Bonferroni correction at a 95% significance level (P=0.

Population data of 30 insertion-deletion markers in four Chinese populations.

In this study, we assessed 30 insertion-deletion polymorphisms (Indels) (Investigator DIPplex® kit) in four Chinese populations (n = 952) and evaluated their usefulness in forensic genetic applications. After the Bonferroni correction at a 95 % significance level (p = 0.0017), there were no deviations from the Hardy-Weinberg equilibrium observed except for the HLD114 locus in the Tibetan ethnic group studied.