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In recent years, large usage of wireless networks puts forward challenge to the utilization of spectrum resources, and it is significant to improve the spectrum utilization and the system sum data rates in the premise of fairness. However, the existing algorithms have drawbacks in efficiency to maximize the sum data rates of orthogonal frequency division multiple access (OFDMA) systems in the premise of fairness threshold. To address the issue, a novel artificial bee colony algorithm with update quantities of nectar sources is proposed for OFDMA resource allocation in this paper. Firstly, the population of nectar sources is divided into several groups, and a different update quantity of nectar sources is set for each group. Secondly, based on the update quantities of nectar sources set for these groups, nectar sources are initialized by a greedy subcarrier allocation method. Thirdly, neighborhood searches and updates are performed on dimensions of nectar sources corresponding to the preset update quantities. The proposed algorithm can not only make the initialized nectar sources maintain high levels of fairness through the greedy subcarrier allocation but also use the preset update quantities to reduce dimensions of the nectar sources to be optimized by the artificial bee colony algorithm, thereby making full use of both the local optimization of the greedy method and the global optimization of the artificial bee colony algorithm. The simulation results show that, just in the equal-power subcarrier allocation stage, the proposed algorithm can achieve the required fairness threshold and effectively improve the system sum data rates.

The plateau vortex is one of the dominant weather systems that modulate summer rainfall in northwestern China, a typical arid/semi-arid area worldwide. Two heavy rainfall cases in the summer of 2012 and 2013, accompanying the northeastward movement of plateau vortexes, are selected here. The role of plateau vortexes in these two cases is explored by diagnosing their moist potential vorticity, helicity, and convective clouds characteristics, using numerous data (ERA-Interim reanalysis, multi-sourced precipitation data, FY-2E satellite images). The plateau vortexes enhanced convective instability in the rainfall area by entraining upper-level dry and cold air, manifested as salient vertical gradients of moist potential vorticity and steep pseudo-equivalent potential temperature isolines. The rainfall occurred eastward relative to the vortex movement track and intensified on the left of cloud black body temperature (TBB) with larger gradients at the middle to late stages of the vortex life span. The generation of moisture helicity is dictated by the water vapor transportation, with the negative value concentrated in the water vapor-rich areas. Notably, the two cases differ in tracks, life spans, water vapor sources, and precipitation amounts, which may be attributed to large-scale circulation background, vertical vortex structure, and moisture condition.

Additional file 1: Methods. Table S1. Participant characteristics in the GNHS. Table S2. Participant characteristics of the CHNS. Table S3. Associations between α-diversity and cognitive impairment in the GNHS (n = 1430). Table S4. Associations between α-diversity and cognitive decline in the CHNS (n = 1300). Table S5. Weights for genus features contributing to the LASSO models. Table S6. Associations between bacterial taxonomy and cognitive performance in the CHNS (n = 1300). Table S7. Associations between Odoribacter and SCFAs in the GNHS. Table S8. Associations between serum acetic acid and brain structure in the GNHS. Table S9. Distribution of metagenomic features in the GNHS. Table S10. Associations between intra-individual alterations in gut microbial composition and cognitive impairment in the GNHS. Table S11. Metagenomic and metabolomic features associated with cognitive impairment in the GNHS. Table S12. Correlations of metagenomic and metabolic features with MMSE domains in the GNHS. Table S13. Correlations between metagenomic features and metabolites in the GNHS. Fig. S1 Overview of the multi-omics datasets of the GNHS. Fig. S2 Distribution of metagenomic and metabolomic features in the GNHS. Fig. S3 Correlation analyses on cognition-related metagenomic and metabolic traits in the GNHS. Fig. S4 Association of metagenomic pathways and serum metabolomics with cognitive function in the GNHS. Fig. S5 Networks of metagenomic pathways in the GNHS.