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Direct wireless communication through sea–air media is essential for constructing an integrated communication network that spans space, air, land, and sea. The amplitude of acoustically induced micromotion surface waves is much smaller than the noise interference in complex sea states, making the accurate extraction of these signals from the raw signals detected by an FMCW millimeter-wave radar a major challenge. In this paper, Butterworth filtering is used to extract underwater acoustic signals from the surface waves detected by radar. The physical processes of the channel were simulated theoretically and verified experimentally. The results demonstrate a fitting coefficient of 0.99 between the radar-detected water surface waves and the simulation outcomes, enabling the effective elimination of noise interference and the extraction of acoustically induced micromotion signals in environments with a signal-to-noise ratio (SNR) of −20 dB to −10 dB. Experiments modifying frequency and linear frequency modulation have verified that the usable frequency range for underwater acoustic signals is at least 400 Hz, meeting the frequency requirements of Binary Frequency Shift Keying (2FSK) modulation encoding methods. This research confirms the accuracy of the simulation results and the feasibility of filtering and extracting underwater acoustic signals, providing a theoretical basis and an experimental foundation for building cross-media communication links.

Alcohol abuse can lead to alcoholic liver disease, becoming a major global burden. Hovenia dulcis fruit peduncle polysaccharides (HDPs) have the potential to alleviate alcoholic liver injury and play essential roles in treating alcohol-exposed liver disease; however, the hepatoprotective effects and mechanisms remain elusive. In this study, we investigated the hepatoprotective effects of HDPs and their potential mechanisms in alcohol-exposed mice through liver metabolomics and gut microbiome. The results found that HDPs reduced medium-dose alcohol-caused dyslipidemia (significantly elevated T-CHO, TG, LDL-C), elevated liver glycogen levels, and inhibited intestinal-hepatic inflammation (significantly decreased IL-4, IFN-γ and TNF-α), consequently reversing hepatic pathological changes. When applying gut microbiome analysis, HDPs showed significant decreases in Proteobacteria, significant increases in Firmicutes at the phylum level, increased Lactobacillus abundance, and decreased Enterobacteria abundance, maintaining the composition of gut microbiota. Further hepatic metabolomics analysis revealed that HDPs had a regulatory effect on hepatic fatty acid metabolism, by increasing the major metabolic pathways including arachidonic acid and glycerophospholipid metabolism, and identified two important metabolites—C00157 (phosphatidylcholine, a glycerophospholipid plays a central role in energy production) and C04230 (1-Acyl-sn-glycero-3-phosphocholine, a lysophospholipid involved in the breakdown of phospholipids)—involved in the above metabolism. Overall, HDPs reduced intestinal dysbiosis and hepatic fatty acid metabolism disorders in alcohol-exposed mice, suggesting that HDPs have a beneficial effect on alleviating alcohol-induced hepatic metabolic disorders.

Anti-tumor activity of Tremella fuciformis polysaccharides (TFPS) has been widely reported, but its mechanism remains poorly understood. In this study, we established an in vitro co-culture system (B16 melanoma cells and RAW 264.7 macrophage-like cells) to explore the potential anti-tumor mechanism of TFPS. Based on our results, TFPS exhibited no inhibition on the cell viability of B16 cells. However, significant apoptosis was observed when B16 cells were co-cultured with TFPS-treated RAW 264.7 cells. We further found that mRNA levels of M1 macrophage markers including iNOS and CD80 were significantly upregulated in TFPS-treated RAW 264.7 cells, while M2 macrophage markers such as Arg-1 and CD 206 remained unchanged. Besides, the migration, phagocytosis, production of inflammatory mediators (NO, IL-6 and TNF-α), and protein expression of iNOS and COX-2 were markedly enhanced in TFPS-treated RAW 264.7 cells. Network pharmacology analysis indicated that MAPK and NF-κB signaling pathways may be involved in M1 polarization of macrophages, and this hypothesis was verified by Western blot. In conclusion, our research demonstrated that TFPS induced apoptosis of melanoma cells by promoting M1 polarization of macrophages, and suggested TFPS may be applied as an immunomodulatory for cancer therapy.

The pollution of heavy metal ions can cause damage to the human body through food, so developing a new biocompatible material that can remove the damage of heavy metal ions has a good application prospect. In this study, we obtained a new homogeneous polysaccharide composed of seven monosaccharides from Zingiber strioatum by using the method of separation and purification of polysaccharide. The results of adsorption behavior showed that the concentration, temperature and pH value could affect the adsorption effect of Zingiber strioatum polysaccharide (ZSP). Through model fitting of the data of adsorption time and metal concentration, the pseudo second-order kinetic model can well describe the kinetics of the adsorption process, and the adsorption isotherm data fit well with the Langmuir model. In the preliminary research results of adsorption mechanism, SEM showed the appearance of ZSP as flake and porous surface; EDX analysis confirmed the metal adsorption capacity of ZSP. Meanwhile, XPS and FT-IR further clarified the adsorption based on functional groups composed of C and O. The cells preprotected by ZSP can resist heavy metal ions. The above results show that ZSP can be used as a new macromolecule to bind heavy metal ions, which can broaden the research scope of polysaccharides in contaminated food systems.

Solar irradiance prediction is a crucial component in the application of photovoltaic power generation, playing a vital role in optimizing energy production, managing energy storage, and maintaining grid stability. This paper proposes an irradiance prediction method based on a functionally structured inverted transformer network, which maintains the channel independence of each feature in the model input and extracts the correlations between different features through an Attention mechanism, enabling the model to effectively capture the relevant information between various features. After the channel mixing of different features is completed through the Attention mechanism, a linear network is used to predict the irradiance sequence. A data processing method tailored to the prediction model used in this paper is designed, which employs a comprehensive data preprocessing approach combining mutual information, multiple imputation, and median filtering to optimize the raw dataset, enhancing the overall stability and accuracy of the prediction project. Additionally, a Dingo optimization algorithm suitable for the self-tuning of deep learning model hyperparameters is designed, improving the model’s generalization capability and reducing deployment costs. The artificial intelligence (AI) model proposed in this paper demonstrates superior prediction performance compared to existing common prediction models in irradiance data forecasting and can facilitate further applications of photovoltaic power generation in power systems.

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method’s effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.