Search

Social comparison is an important way for individuals to define their social characteristics, and online games with a large number of social information provide a convenient platform for social comparison between players. At the same time, the spontaneous self-classification brought by social information makes the social comparison process among players potentially affected by group identity. This study explored the neural mechanism of intergroup and intragroup social comparison among players through fMRI and point estimation paradigm. 26 subjects participated in our experiment, and 25 of their head movement amplitudes were less than 2.5mm and were included in the statistics. We found that the downward comparison led to significantly different brain activation compared with the upward comparison. The fusiform gyrus, putamen, lentiform nucleus, precuneus and precentral gyrus were significantly activated when the group identity of the comparison object was the same as that of the player. When the two have different identities, downward comparison significantly activated the angular gyrus, middle frontal gyrus and superior frontal gyrus. Our research has proved that group identity has a moderating effect on social comparison cognitive process among players. When the object of downward comparison is an out-of-group member, the player will receive the reward information and positive emotional valence, which can positively predict their continuous game behavior in theory. This study provides a new possibility for the cause of game addiction at the perspective of neural mechanism.

Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands for processing various contents (e.g., videos). Generally, for efficient service delivery, the contents from the service providers are processed on the edge servers (ESs), as edge computing offers vehicular applications low-latency services. However, due to the reusability of the same contents required by different distributed vehicular users, processing the copies of the same contents repeatedly in an edge server leads to a waste of resources (e.g., storage, computation, and bandwidth) in ESs. Therefore, it is a challenge to provide high-quality services while guaranteeing the resource efficiency with edge content caching. To address the challenge, an edge content caching method for smart cities with service requirement prediction, named E-Cache, is proposed. First, the future service requirements from the vehicles are predicted based on the deep spatiotemporal residual network (ST-ResNet). Then, preliminary content caching schemes are elaborated based on the predicted service requirements, which are further adjusted by a many-objective optimization aiming at minimizing the execution time and the energy consumption of the vehicular services. Eventually, experimental evaluations prove the efficiency and effectiveness of E-Cache with spatiotemporal traffic trajectory big data.

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

The emerging of the Internet of Things (IoT) enables the interconnection among everything. With edge computing serving low-latency services, IoT makes intelligent energy management become a possibility, thereby enhancing the energy sustainability for energy systems. Currently, renewable energy is widely applied in energy systems to alleviate the carbon footprint. However, the instability and discontinuity of renewable generation decrease the quality of service (QoS) of edge servers. To address the challenge, a renewable prediction-driven service offloading method, named ReSome, is proposed. Technically, a deep-learning-based approach is designed for renewable energy prediction firstly. Next, the service offloading process is abstracted to a Markov decision process (MDP). With the predicted renewable energy amount, asynchronous advantage actor-critic (A3C) is leveraged to determine the optimal service offloading strategy. Finally, by utilizing a real-world solar power generation dataset, the experimental evaluation validates the capability and effectiveness of ReSome.

Background The identification of chromosomes among Avena species have been studied by C-banding and in situ hybridization. However, the complicated results from several cytogenetic nomenclatures for identifying oat chromosomes are often contradictory. A universal karyotyping nomenclature system for precise chromosome identification and comparative evolutionary studies would be essential for genus Avena based on the recently released genome sequences of hexaploid and diploid Avena species. Results Tandem repetitive sequences were predicted and physically located on chromosomal regions of the released Avena sativa OT3098 genome assembly v1. Eight new oligonucleotide (oligo) probes for sequential fluorescence in situ hybridization (FISH) were designed and then applied for chromosome karyotyping on mitotic metaphase spreads of A. brevis, A. nuda, A. wiestii, A. ventricosa, A. fatua, and A. sativa species. We established a high-resolution standard karyotype of A. sativa based on the distinct FISH signals of multiple oligo probes. FISH painting with bulked oligos, based on wheat-barley collinear regions, was used to validate the linkage group assignment for individual A. sativa chromosomes. We integrated our new Oligo-FISH based karyotype system with earlier karyotype nomenclatures through sequential C-banding and FISH methods, then subsequently determined the precise breakage points of some chromosome translocations in A. sativa. Conclusions This new universal chromosome identification system will be a powerful tool for describing the genetic diversity, chromosomal rearrangements and evolutionary relationships among Avena species by comparative cytogenetic and genomic approaches.

Additional file 1: Table S1. The predicted total TRs in the chromosomes of oat genome assembly v1 by TRF., Table S2. Distribution and copy number prediction of centromeric repeats Oligo-CCS1 in oat genome assembly v1., Figure S1. The genomic distribution of TRs in assembled oat genome v1 revealed by each chromosome., Figure S2. ND-FISH karyotype of A. sativa revealed by multiple oligo probes of tandem repeats., Figure S3. Sequential ND-FISH analysis by predicted TR Oligo-probes on metaphase chromosomes of oat BaiyanII., Figure S4. The comparative genome between the A-genome of A. sativa to A-genome of A. atlantica (a), C-genome of A. sativa to AE genome of A. eriantha (b) by Circos software with the annotated genes.

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.