The Cognitive Load of Observation Tasks in 3D Video is Lower Than That in 2D Video

We are exposed to more and more 3D videos, some for entertainment and some for scientific research. Some experiments using 3D video as a stimulus focus only on its visual effect. We studied the cognitive difference between 3D and 2D videos by analyzing EEG. This research adopts a 2 x 4 experimental design, including 2D and 3D versions of 4 video scenes. These four video scenes can be classified into two simple task scenes and two complex task scenes. The simple task scenario and the complex task scenario each contain a video with violent content changes and a calm video. Subjects need to watch eight videos. We recorded the EEG information of the subjects and analyzed the power of alpha and theta oscillations. On this basis, we calculated the cognitive load index (CLI), which can be used as an indicator of cognitive load. The results showed that 3D videos that required subjects to perform simple tasks brought higher cognitive load to most subjects. When the video contains complex tasks, the cognitive load of subjects does not show similar regularity. Specifically, only half of the people had higher cognitive load when watching the 3D version of the video than when watching the 2D version. In addition, the cognitive load level of subjects showed significant individual differencesComment: 7 pages, 18 figure

Efficient regeneration and genetic transformation platform applicable to five Musa varieties

Background: Banana ( Musa spp.) is an important staple food, economic crop, and nutritional fruit worldwide. Conventional breeding has been seriously hampered by their long generation time, polyploidy, and sterility of most cultivated varieties. Establishment of an efficient regeneration and transformation system for banana is critical to its genetic improvement and functional genomics. Results: In this study, a vigorous and repeatable transformation systemfor banana using direct organogenesiswas developed. The greatest number of shoots per explant for all five Musa varieties was obtained using Murashige and Skoog medium supplemented with 8.9 \u3bcM benzylaminopurine and 9.1 \u3bcM thidiazuron. One immature male flower could regenerate 380\u2013456, 310\u2013372, 200\u2013240, 130\u2013156, and 100\u2013130 well-developed shoots in only 240\u2013270 d for Gongjiao, Red banana, Rose banana, Baxi, and Xinglongnaijiao, respectively. Longitudinal sections of buds were transformed through particle bombardment combined with Agrobacterium -mediated transformation using a promoterless \u3b2-glucuronidase (GUS) reporter gene; the highest transformation efficiency was 9.81% in regenerated Gongjiao plantlets in an optimized selection medium. Transgenic plants were confirmed by a histochemical assay of GUS, polymerase chain reaction, and Southern blot. Conclusions: Our robust transformation platform successfully generated hundreds of transgenic plants. Such a platform will facilitate molecular breeding and functional genomics of banana

Comprehensive Ocean Information-Enabled AUV Motion Planning Based on Reinforcement Learning

Motion planning based on the reinforcement learning algorithms of the autonomous underwater vehicle (AUV) has shown great potential. Motion planning algorithms are primarily utilized for path planning and trajectory-tracking. However, prior studies have been confronted with some limitations. The time-varying ocean current affects algorithmic sampling and AUV motion and then leads to an overestimation error during path planning. In addition, the ocean current makes it easy to fall into local optima during trajectory planning. To address these problems, this paper presents a reinforcement learning-based motion planning algorithm with comprehensive ocean information (RLBMPA-COI). First, we introduce real ocean data to construct a time-varying ocean current motion model. Then, comprehensive ocean information and AUV motion position are introduced, and the objective function is optimized in the state-action value network to reduce overestimation errors. Finally, state transfer and reward functions are designed based on real ocean current data to achieve multi-objective path planning and adaptive event triggering in trajectorytracking to improve robustness and adaptability. The numerical simulation results show that the proposed algorithm has a better path planning ability and a more robust trajectory-tracking effect than those of traditional reinforcement learning algorithms

High Value of Information Guided Data Enhancement for Heterogeneous Underwater Wireless Sensor Networks

Ensuring the freshness of high Value of Information (VoI) data has a significant practice meaning for marine observations and emergencies. The traditional forward method with an auv-aid is used to ensure the freshness of high VoI data. However, the methods suffer from two issues: an insufficient high VoI data throughput and random forwarding for cluster heads (CHs). The AUV (Autonomous Underwater Vehicle) with limited energy cannot meet the demand for the random generation of high VoI data. Low VoI data packets compete with high VoI data packets for channels, resulting in an insufficient high VoI data throughput and a low freshness. To address the above issues, we propose the Data Access Channel Scheme based on High Value of Information (DACS-HVOI), which is suitable for prioritizing the transmission packets with a high VoI. First, according to the level of VoI, the packets are divided into K classes, and the packets that are collected and forwarded by the AUV are defined as the highest K+1 class. Second, based on prior knowledge in the network, a Markov chain algorithm-based method is employed to predict which nodes should preferentially use the channel, to avoid conflict between a low and high VoI. Third, based on the stochastic fluid theory, a multilevel queueing system for CHs are constructed to avoid random forwarding. Last, compared with state-of-art protocols, experimental simulation shows that the proposed scheme has a low latency and high network throughput, while improving the throughput of high-VoI packets and ensuring the priority transmission of high-VoI packets