Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks.

With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing (MEC) server, the proposed architecture aims to release the burden on roadside units (RSUs) servers. Specifically, we propose a two-layer edge intelligence scheme to allocate network computing resources. The first layer intelligently offloads and allocates tasks generated by wireless devices in the vehicular system, and the second layer utilizes the partially observable stochastic game (POSG), solved by duelling deep Q-learning, to allocate the computing resources of each processing node (PN) to different tasks. Meanwhile, we propose a weighted position optimization algorithm for the UAV movement in the system to facilitate task offloading and task processing. Simulation results demonstrate the improved performance by applying the proposed scheme.

Analysis of genetic information from the antlers of Rangifer tarandus (reindeer) at the rapid growth stage.

Reindeer is the only deer species in which both males and females regularly grow antlers, providing an excellent model for studying the rapid growth and annual regeneration of antlers. The study of genetic information from reindeer is the basis for revealing the unique mechanism of antler growth. In the present study, we obtained 18.86 GB of clean reads, which were assembled to obtain 94,575 unigenes (average length: 704.69). Among these reads, 30,980 sequences were identified by searching a database of known proteins and then annotated with Gene Ontology (GO) terms, Clusters of Orthologous Groups (COG) classifications and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. All 7,480 simple sequence repeats (SSRs) were detected. A total of 84,435 and 82,226 high-quality single-nucleotide polymorphisms (SNPs) were identified in male and female reindeer, respectively. We identified 31 genes that were highly expressed in reindeer antlers. These genes regulate cell activities that are closely associated with the process of rapid tissue growth. Our results provide a basis for studying reindeer antlers and for further studying the molecular genetics, population genetics, and functional genomics of reindeer.

[Estrogen receptor as a candidate gene for prolificacy of small tail Han sheep].

Single nucleotide polymorphism in exon 1 of the estrogen receptor (ESR) gene was detected by PCR-SSCP in both high fecundity sheep breeds (Small Tail Han sheep, Hu sheep and German Mutton Merino sheep) and low fecundity sheep breeds (Dorset sheep,Suffolk sheep). Results indicated that there were three genotypes (AA, AB and BB) in all three high fecundity sheep breeds, but only two genotypes (AA, AB) in both low fecundity breeds. In Hu sheep,German Mutton Merino sheep, Small Tail Han sheep, Suffolk sheep and Dorset sheep,the frequency of allele A was 0.672, 0.786, 0.846, 0.857 and 0.867, respectively, and the frequency of allele B was 0.328, 0.214, 0.154, 0.143, and 0.133, respectively. Sequencing revealed a C-->G mutation at 363 bp of exon 1 of ESR gene in the BB genotype in comparison to the AA genotype. The genotype distribution was significantly different between Small Tail Han sheep and Hu sheep (P<0.01) and between Dorset sheep and Hu sheep (P <0.05). There was no difference in genotype distribution between other sheep breeds. The Small Tail Han sheep ewes with genotypes AB or BB had 0.51 (P < 0.05) and 0.7 (P < 0.05) more lambs than those with genotype AA, respectively. These results showed that the estrogen receptor locus is either a major gene that influences the prolificacy in Small Tail Han sheep or in close linkage with such a gene. In view of our results, marker-assisted selection using ESR is warranted to increase litter size in sheep and will be of considerable economic value to mutton producers.