The origin and demographic history of the ethnic populations of China have not been clearly resolved. In this study, we examined the hypervariable segment I sequences (HVSI) of the mitochondrial DNA control region in 372 individuals from nine Chinese populations and one northern Thai population. A relatively high percentage of individuals was found to share sequences with those from other populations of the same ethnogenesis. In general, the populations of southern or Pai-Yuei tribal origin showed high haplotype diversity and nucleotide diversity compared with the populations of northern or Di-Qiang tribal origin. Mismatch distributions from these populations showed concordant features. All except the northern groups Nu, Lisu, Tibetan, and Mongolian showed typical signatures of ancient population expansions in the mismatch distributions and neutrality tests. Episodes of extreme size reduction in the past are one of the likely explanations for the absence of evidence of expansion in northern populations. Small sample sizes as well as samples from isolated subpopulations contributed to the bumpy mismatch distributions observed. Phylogenetic analysis and haplotype sharing among populations suggest that current mtDNA variation in these ethnic populations could reveal their ethnohistory to some extent, but in general, linguistic and geographic classifications of the populations did not agree well with classification by mtDNA variation.

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The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.