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Lithium-ion batteries are yet to realize their full promise because of challenges in the design and construction of electrode architectures that allow for their entire interior volumes to be reversibly accessible for ion storage. Electrodes constructed from the same material and with the same specifications, which differ only in terms of dimensions and geometries of the constituent particles, can show surprising differences in polarization, stress accumulation and capacity fade. Here, using operando synchrotron X-ray diffraction and energy dispersive X-ray diffraction (EDXRD), we probe the mechanistic origins of the remarkable particle geometry-dependent modification of lithiation-induced phase transformations in V 2 O 5 as a model phase-transforming cathode. A pronounced modulation of phase coexistence regimes is observed as a function of particle geometry. Specifically, a metastable phase is stabilized for nanometre-sized spherical V 2 O 5 particles, to circumvent the formation of large misfit strains. Spatially resolved EDXRD measurements demonstrate that particle geometries strongly modify the tortuosity of the porous cathode architecture. Greater ion-transport limitations in electrode architectures comprising micrometre-sized platelets result in considerable lithiation heterogeneities across the thickness of the electrode. These insights establish particle geometry-dependent modification of metastable phase regimes and electrode tortuosity as key design principles for realizing the promise of intercalation cathodes. Designing electrode architectures for Li-ion batteries that can be reversibly accessible for ion storage can be challenging. Using operando techniques the mechanistic origin of lithiation-induced phase transformations in a V 2 O 5 model cathode is now clarified.

This paper proposes an underwater-acoustic class-incremental few-shot learning (UACIL) method for streaming data processing in practical underwater-acoustic target recognition scenarios. The core objective is to expand classification capabilities for new classes while mitigating catastrophic forgetting of existing knowledge. UACIL’s contributions encompass three key components: First, to enhance feature discriminability and generalization, an enhanced frequency-domain attention module is introduced to capture both spatial and temporal variation features. Second, it introduces a prototype classification mechanism with two operating modes corresponding to the base-training phase and the incremental training phase. In the base phase, sufficient pre-training is performed on the feature extraction network and the classification heads of inherent categories. In the incremental phase, for streaming data processing, only the classification heads of new categories are expanded and updated, while the parameters of the feature extractor remain stable through prototype classification. Third, a joint optimization strategy using multiple loss functions is designed to refine feature distribution. This method enables rapid deployment without complex cross-domain retraining when handling new data classes, effectively addressing overfitting and catastrophic forgetting in hydroacoustic signal classification. Experimental results with public datasets validate its superior incremental learning performance. The proposed method achieves 92.89% base recognition accuracy and maintains 68.44% overall accuracy after six increments. Compared with baseline methods, it improves base accuracy by 11.14% and reduces the incremental performance-dropping rate by 50.09%. These results demonstrate that UACIL enhances recognition accuracy while alleviating catastrophic forgetting, confirming its feasibility for practical applications.

In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using an improved sparse iterative covariance-based (SPICE) method, unaffected by the coherent signal, and it can provide highly accurate DOA estimation for multiple targets. The fitted signal energy distribution obtained from the SPICE is then utilized for the reconstruction of the signal coherence matrix. The reconstructed ICM matrix is used to construct a blocking masking matrix and an eigen-projection matrix to suppress the mainlobe interference signal. Compared with existing methods, the method in this paper possesses better mainlobe interference suppression ability. Within the mainlobe interference interval angle of 3° to 13.5° from the signal of interest (SOI) based on eight-element uniform linear arrays, the method in this paper can enhance the signal-to-interference ratio (SIR) by about 15.59 dB on average compared with the interference-free suppression of conventional beamforming (CBF) and outperforms the other interference suppression methods simultaneously. Simulations and experiments demonstrate the effectiveness of this method in mainlobe interference scenarios.

In this study, we developed an innovative method for one-step enzyme purification and immobilization utilizing polysaccharide-based microspheres through a chitosan-binding module that mediated affinity adsorption. The chitosan-binding domain derived from Paenibacillus sp. IK-5 was genetically fused with multiple target enzymes (lysine decarboxylase, glutamate oxidase, and formate dehydrogenase), all of which were successfully expressed in soluble forms. Three distinct polysaccharide microspheres with optimized surface characteristics were engineered to facilitate the concurrent purification and immobilization of these fusion enzymes. Comprehensive characterization using organic elemental analysis, fluorescence microscopy, and thermogravimetric analysis confirmed the efficient immobilization of fusion enzymes. Remarkably, the immobilized enzymes demonstrated exceptional operational stability, maintaining over 80% of their initial catalytic activity after ten consecutive reuse cycles. This study establishes a robust and versatile platform for enzyme immobilization, providing significant advantages in biocatalyst engineering applications.

To investigate the effects of (BC) and tributyrin (TB) on Danzhou chickens, a 2 × 2 factorial design was adopted. A total of 480 chickens were randomly assigned to four dietary treatments, consisting of two BC levels (0 and 1.5 g/kg) and two TB levels (0 and 1.0 g/kg), for a 35-day trial. The results showed that supplementation with BC or TB alone significantly increased the average daily gain (ADG), serum immune parameters (immunoglobulin A, immunoglobulin G, and anti-inflammatory cytokines interleukin-4 and interleukin-10), total antioxidant capacity, and catalase activity, while reducing pro-inflammatory cytokine levels and the feed-to-gain ratio ( < 0.05). In addition, individual supplementation with BC or TB also enhanced digestive enzyme activities in the intestine, increased villus height in the small intestine, and optimized the structure of the cecal microbiota ( < 0.05). More importantly, significant synergistic interactions between BC and TB were observed across multiple parameters ( < 0.05). Combined supplementation further increased ADG, serum immunoglobulin M levels, superoxide dismutase activity, lipase activity in the ileum, and villus height in the jejunum ( < 0.05). Meanwhile, the combined supplementation also significantly elevated the abundance of beneficial bacteria such as , , and . In conclusion, supplementation with BC or TB effectively improved the growth performance, antioxidant status, immune function, intestinal morphology, and cecal microbiota composition of Danzhou chickens, and the combined supplementation demonstrated superior effects compared to individual supplementation.

Classifying modulated signals for non-cooperative underwater acoustic communication is challenging due to signal distortion caused by fading and multipath effects in the underwater acoustic channel. Our proposed method utilizes channel estimation parameters to measure and correct signal distortion, thereby enhancing the recognition performance of the received signal. Modulation classification experiments were conducted on a public dataset with various modulation schemes, as well as on the same dataset with simulated underwater acoustic channels. The results indicate that our method effectively mitigates the impact of the underwater acoustic channel on modulation signal classification, improves recognition accuracy, and is broadly applicable to a wide range of machine learning classifiers. Finally, we validated these findings using real underwater communication data.

To investigate the effects of low net-energy (NE) diets on intestinal health in Tunchang pigs, 96 animals (25.40 ± 1.11 kg) were randomly assigned to four dietary treatment groups with NE levels of 9.82 (CG), 9.57 (EY1), 9.32 (EY2), and 9.07 (EY3) MJ/kg. Each group consisted of six replicates with four pigs per replicate. The experiment lasted for 63 days. The results showed that compared with the CG, the EY2 increased jejunal villus height and villus height-to-crypt depth ratio, as well as reduced crypt depth in the colon (p < 0.05). Both the EY1 and EY2 demonstrated improved intestinal barrier function through upregulation of zonula occludens-1 and occludin expression in the jejunum, zonula occludens-1 in the ileum, and zonula occludens-1, occludin, and claudin-1 in the colon (p < 0.05). Furthermore, EY2 significantly increased the activities of superoxide dismutase, glutathione peroxidase, and catalase, while reducing malondialdehyde content in both the jejunum and colon (p < 0.05). EY2 showed significantly downregulated relative expression of pro-inflammatory cytokines, including interleukin-1β, tumor necrosis factor-α, and interleukin-6, in the jejunum, ileum, and colon (p < 0.05). Microbial and short-chain fatty acid (SCFA) analyses showed that the EY2 increased the abundance of beneficial bacteria such as Faecalibacterium, CF231, Coprococcus, Ruminococcus, and Blautia and elevated the concentrations of acetate, propionate, and butyrate. In summary, reducing dietary NE levels to no less than 9.32 MJ/kg improved intestinal health by modulating the gut microbiota and increasing SCFA production.

Global cloud thermodynamic phase (CP) is normally derived from polar-orbiting satellite imaging data with high spatial resolution. However, constraining conditions and empirical thresholds used in the MODIS (Moderate Resolution Imaging Spectroradiometer) CP algorithm are closely associated with spectral properties of the MODIS infrared (IR) spectral bands, with obvious deviations and incompatibility induced when the algorithm is applied to data from other similar space-based sensors. To reduce the algorithm dependence on spectral properties and empirical thresholds for CP retrieval, a machine learning (ML)-based methodology was developed for retrieving CP data from China’s new-generation polar-orbiting satellite, FY-3D/MERSI-II (Fengyun-3D/Moderate Resolution Spectral Imager-II). Five machine learning algorithms were used, namely, k-nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), Stacking and gradient boosting decision tree (GBDT). The RF algorithm gave the best performance. One year of EOS (Earth Observation System) MODIS CP products (July 2018 to June 2019) were used as reference labels to train the relationship between MODIS CP (MYD06 IR) and six IR bands of MERSI-II. CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), MODIS, and FY-3D/MERSI-II CP products were used together for cross-validation. Results indicate strong spatial consistency between ML-based MERSI-II and MODIS CP products. The hit rate (HR) of random forest (RF) CP product could reach 0.85 compared with MYD06 IR CP products. In addition, when compared with the operational FY-3D/MERSI CP product, the RF-based CP product had higher HRs. Using the CALIOP cloud product as an independent reference, the liquid-phase accuracy of the RF CP product was higher than that of operational FY-3D/MERSI-II and MYD06 IR CP products. This study aimed to establish a robust algorithm for deriving FY-3D/MERSI-II CP climate data record (CDR) for research and applications.

Recent works have extensively probed contextual relevance to enhance the scene understanding. However, most approaches tend to model the relationships between local regions due to the limitation of the convolution kernel, while rarely exploring long-range dependencies. In this paper, we come up with the Dual Context Aggregation Module (DCM) to effectively capture such important information. DCM splits into two attention modules to obtain dense contextual information via modeling relations between positions and channels. The spatial attention module generates huge attention maps by constructing pairwise relationships between positions in the same row or column. The channel attention module applies the Weight Calibrate Block to generate weights for all the channels to effectively get the correlation between different channels. We adopt an element addition to integrate the feature maps of the two modules. Moreover, we design a two-step decoder module to improve the feature representation. On the basis of these developments, we construct the Dual Context aggregation Network (DCNet). Extensive evaluation experiments on the benchmarks prove that our model leads to robust feature representation. Our method demonstrates competitive performance compared to state-of-the-art models, achieving the MIoU scores of 81.9% on Cityscapes and 45.54% on ADE20K.