Potential of Geo-neutrino Measurements at JUNO

The flux of geoneutrinos at any point on the Earth is a function of the abundance and distribution of radioactive elements within our planet. This flux has been successfully detected by the 1-kt KamLAND and 0.3-kt Borexino detectors with these measurements being limited by their low statistics. The planned 20-kt JUNO detector will provide an exciting opportunity to obtain a high statistics measurement, which will provide data to address several questions of geological importance. This paper presents the JUNO detector design concept, the expected geo-neutrino signal and corresponding backgrounds. The precision level of geo-neutrino measurements at JUNO is obtained with the standard least-squares method. The potential of the Th/U ratio and mantle measurements is also discussed.Comment: 8 pages, 6 figures, an additional author added, final version to appear in Chin. Phys.

Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation

Deep learning-based image compression has made great progresses recently. However, many leading schemes use serial context-adaptive entropy model to improve the rate-distortion (R-D) performance, which is very slow. In addition, the complexities of the encoding and decoding networks are quite high and not suitable for many practical applications. In this paper, we introduce four techniques to balance the trade-off between the complexity and performance. We are the first to introduce deformable convolutional module in compression framework, which can remove more redundancies in the input image, thereby enhancing compression performance. Second, we design a checkerboard context model with two separate distribution parameter estimation networks and different probability models, which enables parallel decoding without sacrificing the performance compared to the sequential context-adaptive model. Third, we develop an improved three-step knowledge distillation and training scheme to achieve different trade-offs between the complexity and the performance of the decoder network, which transfers both the final and intermediate results of the teacher network to the student network to help its training. Fourth, we introduce $L_{1}$ regularization to make the numerical values of the latent representation more sparse. Then we only encode non-zero channels in the encoding and decoding process, which can greatly reduce the encoding and decoding time. Experiments show that compared to the state-of-the-art learned image coding scheme, our method can be about 20 times faster in encoding and 70-90 times faster in decoding, and our R-D performance is also $2.3 \%$ higher. Our method outperforms the traditional approach in H.266/VVC-intra (4:4:4) and some leading learned schemes in terms of PSNR and MS-SSIM metrics when testing on Kodak and Tecnick-40 datasets.Comment: Submitted to Trans. Journa

Expanding CRISPR/Cas9 Genome Editing Capacity in Zebrafish Using SaCas9.

The type II CRISPR/Cas9 system has been used widely for genome editing in zebrafish. However, the requirement for the 5'-NGG-3' protospacer-adjacent motif (PAM) of Cas9 from Streptococcus pyogenes (SpCas9) limits its targeting sequences. Here, we report that a Cas9 ortholog from Staphylococcus aureus (SaCas9), and its KKH variant, successfully induced targeted mutagenesis with high frequency in zebrafish. Confirming previous findings, the SpCas9 variant, VQR, can also induce targeted mutations in zebrafish. Bioinformatics analysis of these new Cas targets suggests that the number of available target sites in the zebrafish genome can be greatly expanded. Collectively, the expanded target repertoire of Cas9 in zebrafish should further facilitate the utility of this organism for genetic studies of vertebrate biology

Damage detection of concrete piles subject to typical damages using piezoceramic based passive sensing approach

Pile foundations are typically comprised in concealed construction work. In recent years, some major categories of concrete piles subject to typical damages have caused a lot of engineering disasters and accidents. These accidents have been caused by collapse of civil structures resulting in great casualties and economic loss. Therefore, damage detection and real-time health monitoring on foundation piles is an urgent research requirement. In this research, a piezoceramic based passive sensing approach is proposed to detect typical damages types of concrete piles, including partial mud intrusion, secondary concrete pouring interface, circumferential crack, and full mud intrusion. In this passive sensing approach, induced stress waves are generated by the impact hammer on the top surface of a pile and one smart aggregate embedded on the bottom of each pile is used as a sensor to receive the propagating wave signals. These sensors are embedded before pouring concrete. Structural defects affect the natural frequency of the pile. The power spectrum of piles with different types of damage were compared by plotting the sensor signals in frequency domain. The natural frequency decreases with the increase in defect severity. The experimental results demonstrate that the proposed approach can detect all four typical damage types in concrete piles

Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation

Neighbor discovery (ND) is a key step in wireless ad hoc network, which directly affects the efficiency of wireless networking. Improving the speed of ND has always been the goal of ND algorithms. The classical ND algorithms lose packets due to the collision of multiple packets, which greatly affects the speed of the ND algorithms. Traditional methods detect packet collision and implement retransmission when encountering packet loss. However, they does not solve the packet collision problem and the performance improvement of ND algorithms is limited. In this paper, the successive interference cancellation (SIC) technology is introduced into the ND algorithms to unpack multiple collision packets by distinguishing multiple packets in the power domain. Besides, the multi-packet reception (MPR) is further applied to reduce the probability of packet collision by distinguishing multiple received packets, thus further improving the speed of ND algorithms. Six ND algorithms, namely completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC), and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and verified by simulation. The simulation results show that SIC and MPR reduce the ND time of SBA by 69.02% and CRA by 66.03% averagely.Comment: 16 pages, 16 figure