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In this study, we examined the role of the eastern bent-winged bat (Miniopterus fuliginosus) in the dispersion of bat adenovirus and bat alphacoronavirus in east Asia, considering their gene flows and divergence times (based on deep-sequencing data), using bat fecal guano samples. Bats in China moved to Jeju Island and/or Taiwan in the last 20,000 years via the Korean Peninsula and/or Japan. The phylogenies of host mitochondrial D-loop DNA was not significantly congruent with those of bat adenovirus (m2XY = 0.07, p = 0.08), and bat alphacoronavirus (m2XY = 0.48, p = 0.20). We estimate that the first divergence time of bats carrying bat adenovirus in five caves studied (designated as K1, K2, JJ, N2, and F3) occurred approximately 3.17 million years ago. In contrast, the first divergence time of bat adenovirus among bats in the 5 caves was estimated to be approximately 224.32 years ago. The first divergence time of bats in caves CH, JJ, WY, N2, F1, F2, and F3 harboring bat alphacoronavirus was estimated to be 1.59 million years ago. The first divergence time of bat alphacoronavirus among the 7 caves was estimated to be approximately 2,596.92 years ago. The origin of bat adenovirus remains unclear, whereas our findings suggest that bat alphacoronavirus originated in Japan. Surprisingly, bat adenovirus and bat alphacoronavirus appeared to diverge substantially over the last 100 years, even though our gene-flow data indicate that the eastern bent-winged bat serves as an important natural reservoir of both viruses.

Aiming at the existent problems of complex structure, slow optimization rate and inadequate adaptability of multi-colony ant colony algorithm, a single colony adaptive heterogeneous ant colony algorithm is proposed to solve mobile robot path planning. The proposed algorithm employs a single colony to avoid the problem of complex structure of multi-ant colony algorithm. Each ant in the colony has its own control parameters to realize the hete-rogeneous behavior and increase the diversity of the population. In the first iteration, only the heuristic factor is used to construct the candidate solution, which improves the quality of the initial population. The information exchange period is adaptively determined based on the change of population information entropy to enhance the adaptability of the algorithm. The information exchange strategy transfers the control parameters of the optimal ant to the worst ant to enhance the guiding role of the optimal ant. Parameter mutation operation is helpful for exploring better algo-rithm parameters in a larger parameter space and improving the ability to escape from local optimization. The results of simulation experiments and statistical tests verify the effectiveness, stability and superiority of the pro-posed algorithm.