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Achieving controlled mobility of microparticles in viscous fluids can become pivotal in biologics, biotechniques, and biomedical applications. The self-assembly, trapping, and transport of microparticles are being explored in active matter, micro and nanorobotics, and microfluidics; however, little work has been done in acoustics, particularly in active matter and robotics. This study reports the discovery and characterization of microbubble behaviors in a viscous gel that is confined to a slight opening between glass boundaries in an acoustic field. Where incident waves encounter a narrow slit, acoustic pressure is amplified, causing the microbubbles to nucleate and cavitate within it. Intermittent activation transforms microbubbles from spherical to ellipsoidal, allowing them to be trapped within the interstice. Continuous activation propels ellipsoidal microbubbles through shape and volume modes that is developed at their surfaces. Ensembles of microbubbles self-assemble into a train-like arrangement, which in turn capture, transport, and release microparticles. Experiments show how confined microbubbles in a viscous gel can be controllably moved by means of an acoustic field. Ensembles of such microbubbles can be made to self-assemble into a train-like arrangement, which can trap, transport and release microparticles.

Small water bodies are widely spread and play crucial roles in supporting regional agricultural and aquaculture activities. PlanetScope imagery has a high resolution (3 m) with daily global coverage and has obviously enhanced small water body mapping. Recent studies have demonstrated the effectiveness of deep learning for mapping small water bodies using PlanetScope; however, a persistent challenge remains in the scarcity of high-quality, manually annotated water masks used for model training, which limits the generalization capability of data-driven deep learning models. In this study, we propose a transfer learning framework that leverages Sentinel-2 data to improve PlanetScope-based small water body mapping, capitalizing on the spectral interoperability between PlanetScope and Sentinel-2 bands and the abundance of open-source Sentinel-2 water masks. Eight state-of-the-art segmentation models have been explored. Additionally, this paper presents the first assessment of the VMamba model for small water body mapping, building on its demonstrated success in segmentation tasks. The models were pre-trained using Sentinel-2-derived water masks and subsequently fine-tuned with a limited set (1292 image patches, 256 × 256 pixels in each patch) of manually annotated PlanetScope labels. Experiments were conducted using 5648 image patches and two areas of 9636 km2 and 2745 km2, respectively. Among the evaluated methods, VMamba achieved higher accuracy compared with both CNN- and Transformer-based models. This study highlights the efficacy of combining global Sentinel-2 datasets for pre-training with localized fine-tuning, which not only enhances mapping accuracy but also reduces reliance on labor-intensive manual annotation in regional small water body mapping.

As the world strides into the digital world, cybersecurity has become an indispensable part of connected devices. Although we have developed cybersecurity measures that can effectively defend against malicious software, we don’t have an accurate solution against attacks like social engineering attack, scam calls or phishing. In this work, a novel detection system called m-isds(mobilized intrusion and spam detection system) architecture is proposed, borrowing ideas from the widely utilized advanced hybrid intrusion detection system(ids), combining with some novel concepts including machine learning, advanced hashing technologies and pattern-matching technologies that are secure and cryptographically safe to provide a solution to the proposed system with low false-positive rate and privacy infringement while remaining responsive and flexible against all types of attacks. The system aims to scan the content of the whole terminal on the fly, not only containing and defending against the threat of malicious softwares but also alerting the user of possible scams and spams, bringing the security goal of mobile devices to a whole new level.

This study was conducted by searching electronic databases from January 1, 2000 to August 8, 2023: Web of Science, PubMed, Cochrane Library, Embase, Scopus, Google Scholar, China Biomedical Literature Database (CBM), China National Knowledge Network (CNKI), China Science and Technology Journal Database (VIP) and Wan-fang Database were included in 14 studies with a total sample size of 1630 cases. This study conducted a meta-analysis of the literature published in recent years on the occurrence of dysgeusia in Head and neck squamous cell carcinoma (HNSCC) patients with non-pharmacological treatment, in order to provide the latest evidence-based evidence for medical staff and provide a basis for further intervention of dysgeusia in HNSCC patients. Compared with conventional care in the control group, In the experimental group, the non-pharmacological intervention reduced the score of dysgeusia [ MD  = − 0.76, 95% CI (− 1.04, − 0.48), P  < 0.00001] and the incidence of dysgeusia [ MD  = 0.17, 95% CI (0.09, 0.31), P  < 0.00001].

In remote-sensing images, a detected oil-spill area is usually affected by spot noise and uneven intensity, which leads to poor segmentation of the oil-spill area. This paper introduced a deep semantic segmentation method that combined a deep-convolution neural network with the fully connected conditional random field to form an end-to-end connection. On the basis of Resnet, it first roughly segmented a multisource remote-sensing image as input by the deep convolutional neural network. Then, we used the Gaussian pairwise method and mean-field approximation. The conditional random field was established as the output of the recurrent neural network. The oil-spill area on the sea surface was monitored by the multisource remote-sensing image and was estimated by optical image. We experimentally compared the proposed method with other models on the dataset established by the multisensory satellite image. Results showed that the method improved classification accuracy and captured fine details of the oil-spill area. The mean intersection over the union was 82.1%, and the monitoring effect was obviously improved.

Sea cucumber polysaccharide is a kind of heteropolysaccharide rich in sulfate groups, which has complex structures and various biological activities. Its biological activity is closely related to its chemical composition, molecular weight, and sulfated patterns. Therefore, in order to study the biological activity of sea cucumber polysaccharide, the analysis methods of a sea cucumber polysaccharide structure were comprehensively summarized, and the neutral polysaccharide, fucosylated chondroitin sulfate, and sulfated fucan of sea cucumber were reviewed. The structural characteristics of sea cucumber polysaccharide were expected to provide a theoretical basis for subsequent studies on the biological activity and structure–activity relationship of sea cucumber polysaccharide.

The research of metamaterial shows great potential in the field of solar energy harvesting. In the past decade, the design of broadband solar metamaterial absorber (SMA) has attracted a surge of interest. The conventional design typically requires brute‐force optimizations with a huge sampling space of structure parameters. Very recently, deep learning (DL) has provided a promising way in metamaterial design, but its application on SMA development is barely reported due to the complicated features of broadband spectrum. Here, this work develops the DL model based on metamaterial spectrum transformer (MST) for the powerful design of high‐performance SMAs. The MST divides the optical spectrum of metamaterial into N patches, which overcomes the severe problem of overfitting in traditional DL and boosts the learning capability significantly. A flexible design tool based on free customer definition is developed to facilitate the real‐time on‐demand design of metamaterials with various optical functions. The scheme is applied to the design and fabrication of SMAs with graded‐refractive‐index nanostructures. They demonstrate the high average absorptance of 94% in a broad solar spectrum and exhibit exceptional advantages over many state‐of‐the‐art counterparts. The outdoor testing implies the high‐efficiency energy collection of about 1061 kW h m−2 from solar radiation annually. This work paves a way for the rapid smart design of SMA, and will also provide a real‐time developing tool for many other metamaterials and metadevices. This work develops the deep learning model based on metamaterial spectrum transformer (MST) for the powerful design of high‐performance solar metamaterial absorbers. The MST divides the optical spectrum of metamaterial into N patches, which overcomes the severe problem of overfitting in traditional deep learning model and boosts the learning capability significantly.

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R2) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R2 > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making.

Background Equity is one of the major goals of China’s new medical reforms launched in 2009. This study aimed to analyze the disequilibrium in primary health care (PHC) workforce among various economic zones in China and to compare the fairness between urban and rural areas since the implementation of the new medical reforms. Method According to China’s 11th Five-Year Plan, China is divided into eight economic regions. The data of this study were obtained from China Statistical Yearbook 2009–2016. The Atkinson index was used to depict the trend of PHC workforce fairness; the Gini coefficient was used to compare the fairness of workforce distribution between urban and rural areas; the health resource agglomeration degree was used to analyze the distributional equity of the workforce in the eight regions; and the Theil Index was used to compare the fairness of urban and rural workforce distribution across eight regions. Result The Atkinson index indicated that the equity of the entire PHC workforce allocation had generally improved during the new medical reforms; the Gini coefficient indicated that the fairness of the entire workforce allocation had improved in cities, but only the nurse allocation became fairer in rural areas. The agglomeration degree and the Theil index indicated that the fairness gaps across the eight regions were still large. These analyses differed from previous studies where China was divided into western, central and eastern regions. In what was previously defined as eastern region, the northeast was under-resourced, while the eastern coastal areas were observing a resource surplus. In western region, we found that the fairness in the northwest was significantly worse than southwest. Conclusion In China, the distribution of healthcare workforce has been improved with continuous effort. The gaps in the distribution of PHC workforce across different economic regions and between urban and rural areas are still large, with different regions facing different problems. The government should consider the population and geographical factors in allocation of PHC workforce, especially nurses.

Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R2) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning.