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Abstract Background Nematodes are the most abundant metazoans in marine sediments, many of which are bacterivores; however, how habitat bacteria affect physiological outcomes in marine nematodes remains largely unknown. Results Here, we used a Litoditis marina inbred line to assess how native bacteria modulate host nematode physiology. We characterized seasonal dynamic bacterial compositions in L. marina habitats and examined the impacts of 448 habitat bacteria isolates on L. marina development, then focused on HQbiome with 73 native bacteria, of which we generated 72 whole genomes sequences. Unexpectedly, we found that the effects of marine native bacteria on the development of L. marina and its terrestrial relative Caenorhabditis elegans were significantly positively correlated. Next, we reconstructed bacterial metabolic networks and identified several bacterial metabolic pathways positively correlated with L. marina development (e.g., ubiquinol and heme b biosynthesis), while pyridoxal 5’-phosphate biosynthesis pathway was negatively associated. Through single metabolite supplementation, we verified CoQ10, heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated growth. Notably, we found that only four development correlated metabolic pathways were shared between L. marina and C. elegans. Furthermore, we identified two bacterial metabolic pathways correlated with L. marina lifespan, while a distinct one in C. elegans. Strikingly, we found that glycerol supplementation significantly extended L. marina but not C. elegans longevity. Moreover, we comparatively demonstrated the distinct gut microbiota characteristics and their effects on L. marina and C. elegans physiology. Conclusions Given that both bacteria and marine nematodes are dominant taxa in sedimentary ecosystems, the resource presented here will provide novel insights to identify mechanisms underpinning how habitat bacteria affect nematode biology in a more natural context. Our integrative approach will provide a microbe–nematodes framework for microbiome mediated effects on host animal fitness.

Objective During the train debugging process, significant vibrations may occur in the steel rail beams of high-speed maglev maintenance bases due to their unique structural characteristics, potentially leading to bolt loosening issues. This phenomenon could alter the vibration characteristics of the rail beams, and changes in the parameters of the vehicle-track coupling system might induce vehicle-track resonance, posing potential harm to rail structure. Therefore, it is essential to investigate the impact of bolt loosening on the vibration of rail structures. Method After expounding the experimental design scheme and principles, the impact of bolt loosening on the modal and dynamic response characteristics of the rail beam and structure are investigated. For structural modal analysis, pulse and ambient excitations are employed to test the track modal properties under both unloaded (the rail is unoccupied) and loaded (the rail is occupied by a train) conditions. Modal identification is carried out using the method of Stochastic Subspace Identification (SSI). Finally, the impact of loose bolt on rail modal properties under both conditions are analyzed through stability diagrams. In terms of dynamic response characteristics, levitation excitation is applied to the rail, and the bolt is gradually loosened during multiple times of static levitation process. Acceleration response signals of the steel beams are collected and a detailed analysis is conducted in both time and frequency domains. Result & Conclusion Bolt loosening, under both loaded and unloaded conditions, caused a decrease in modal frequencies and a change in mode shapes. But under the loaded condition, a considerable increase in the damping ratio is observed after bolt loosening. In the static levitation process, resonance occurred between the rail beam and the train, where bolt loosening not only affected the amplitude of the rail beam vibrations but also altered the symmetry of rail beam vibrations.

Aiming at issues such as illegal dissemination of network audio data and privacy leakage, a stereo robust watermark algorithm based on parameter optimization was proposed.Firstly, the watermark was embedded by modifying the singular value ratio of odd segment based on the insensitivity of stereo channels to attack, and aiming at minimizing the relative distortion of the two channels.The embedding process was optimized and the perceived quality of the audio was improved.Then, a parameter optimization method was proposed to find optimal parameters on each audio file, and the imperceptibility and robustness were well balanced.The simulation results show that the proposed algorithm has good imperceptibility and robustness against conventional signal processing.

Aiming at the high performance requirements of signature verification for specific scenarios such as blockchain, a high-speed hardware architecture of Ed25519 was proposed.To reduce the number of calculations for point addition and point double, a multiple point multiplication algorithm based on interleaving NAF was conducted by using pre-computation and lookup tables.The modular multiplication operation was realized by using the Karatsuba multiplication and fast reduction method, and the point addition and point double operation was designed without modular addition and subtraction, which could effectively improve the performance of point addition and point double.Given that modular exponentiation was the most time-consuming operation in the decompression process, a new modular exponentiation approach was developed by parallelizing modular inverse and modular multiplication, and therefore the performance of the de-compression operation could be improved.The proposed architecture fully considers the use of resources and is implemented on the Zynq-7020 FPGA platform with 13 695 slices, achieving 8 347 verifications per second at 81.6 MHz.

The speech signal is different from the typical audio in terms of spectral bandwidth, intensity distribution, and signal continuity, thus how to achieve high imperceptibility and strong robustness for speech steganography is a big challenge. In this paper, we present a speech steganography scheme based on the parity-segmented method and the differential singular value decomposition (SVD). The selected discrete cosine transform (DCT) coefficients are divided into two segments according to parity order. In this way, the energy of the paired segments is approximately equal, therefore the changes in the singular values caused by data embedding are reduced, and high imperceptibility is achieved. Unlike the common SVD-based steganography, the differential SVD scheme can effectively remove the impact of amplitude scaling attack by embedding the secret message into the difference between the singular values. Experimental results show that the proposed method achieves high imperceptibility and strong robustness while resisting the state-of-the-art steganalytic methods.

The PHase-Aware pRojection Model (PHARM) feature set, built as phase-aware histograms of quantized projections obtained by convolving residuals with random matrices, achieves competitive detection performance against modern adaptive JPEG steganography while having significant computational cost. In this paper, we propose three improvements to the original PHARM, making it more efficient and effective. First, we reduce the maximum projection matrix size to decrease the computational complexity of convolution and better capture steganographic embedding changes. Second, we select more than one phase pair per projection to compute phase-aware histograms, thus correspondingly reducing the number of projections for each residual. Third, the transposition symmetry is also taken into consideration to make our features more robust while preserving the feature dimensionality. The numerous experiments are given to demonstrate the efficiency and effectiveness of our improved PHARM.

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.

We would like to add funding information regarding our article [1] “This work was supported by the Fundamental Research Funds for the Central Universities under Grant 328201905.”