The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

In this study, we proposed an application of voxelwise detection of cerebral microbleed in CADASIL patients by genetic algorithm (GA) and back propagation neural network (BPNN).We collected in total 20 subjects, and obtained 69,356 CMB voxels, and 124,063,981 non-CMB voxels. We employed BPNN as the classification tool. For BPNN has better prediction, we used GA to optimize BPNN. Finally, we used 10-fold cross validation to verify classifier performance. Our method obtained a sensitivity of 72.90±1.38%, a specificity of 72.89±1.18%, and an accuracy of 72.90±1.28%.

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The precise simulation of urban space evolution and grasping of the leading factors are the most important basis for urban space planning. However, the simulation ability of current models is lacking when it comes to complicated/unpredictable urban space changes, resulting in flawed government decision-making and wasting of urban resources. In this study, a macro–micro joint decision model was proposed to improve the ability of urban space evolution simulation. The simulation objects were unified into production, living and ecological space to realize “multiple planning in one”. For validation of the proposed model and method, remote sensing images, geographic information and socio-economic data of Xuzhou, China from 2000 to 2020 were collected and tested. The results showed that the simulation precision of the cellular automata (CA) model was about 87% (Kappa coefficient), which improved to 89% if using a CA and multi-agent system (MAS) joint model. The simulation precision could be better than 92% using the prosed model. The result of factor weight determination indicated that the micro factors affected the evolution of production and living space more than the macro factors, while the macro factors had more influence on the evolution of ecological space than the micro factors. Therefore, active policies should be formulated to strengthen the ideological guidance towards micro individuals (e.g., a resident, farmer, or entrepreneur), and avoid disordered development of living and production space. In addition, ecological space planning should closely link with the local environment and natural conditions, to improve urban ecological carrying capacity and realize urban sustainable development.

The Bdelloidea rotifer, a kind of asexually microscopic invertebrate, is the largest Metazoan group that reproduces only through parthenogenesis. Here the potential evolutionary species composition was analyzed using a coalescent approach to infer independently evolving entities from a phylogenetic tree obtained from cytochrome oxidase I sequences. Three clones (HX4, HX8 and HX19) of Bdelloidea Rotaria rotatoria were selected to be the representatives of three sympatric putative cryptic taxa for detecting the effects of temperature (24, 28 and 32 ℃) on their life history traits. The results showed that the responses of life table parameters to increasing temperature were different among the three evolving entities. Evolutionary species, temperatures and their interaction significantly affected all life history parameters except that evolutionary species did not significantly affect the durations of post-reproductive period and mean lifespan. In addition, the interaction of evolutionary species and temperatures did not significantly affect the durations of postreproductive period, offspring production or net reproductive rate. No matter what the evolutionary species was, the age-specific survival curves tended to decrease earlier and more quickly, and the peak of age-specific fecundity curves appeared earlier with increasing temperature. The three potential cryptic R. rotatoria taxa adopted variable life history strategies, low reproduction and high survivorship at low temperature, as well as high reproduction and low survivorship at high temperature. The similar adaptation abilities of HX4, HX8 and HX19 to water temperatures could be the best explanation for their coexistence in the subtropical shallow pond at a high temperature.