Substituting natural seawater (NS) for deionized water significantly reduces the electrolyte manufacturing cost of aqueous zinc (Zn) ion batteries, but it also poses severe corrosion challenges to the Zn metal anode, given the presence of the Cl-/H2O-rich Zn-electrolyte interface. Here, a NS electrolyte featuring NS solvent and the host-guest complex additive is designed. The 2-mercaptobenzothiazole (MBT) guest shows sustained-release behavior from the cyclodextrin host dominated by its aqueous solubility in the NS electrolyte. Crucially, Cl- ions facilitate a compact MBT shield at the interface via bridging effects, creating a Cl-/H2O-poor microenvironment that suppresses corrosion and extends Zn anode cycle life. Thus, the Zn anode achieves an extended cycling life of 400h in the Zn||Zn symmetric cell even under a practical depth of discharge of 42.7%. The Zn||NaV3O8·1.5H2O full cell with a low negative/positive capacity ratio of 1.92 exhibits 99% capacity retention at 0.5 A g-1 after 600 cycles, and the Ah-level pouch cell with an initial discharge capacity of 1.21 Ah maintains stable cycling for 50 cycles.

In the mining field, hydraulic fracturing of coal - seam boreholes generates a large number of weak microseismic signals. The accurate identification of these signals is crucial for subsequent positioning and inversion. However, when dealing with such signals, traditional automatic microseismic waveform identification algorithms have difficulty in accurately identifying weak waveforms and are prone to misjudging background noise. This study innovatively introduces the deep - learning convolutional neural network (CNN), integrating the concepts and methods of computational communication to analyze microseismic signals. 8,341 pieces of background noise data and 5,860 pieces of microseismic data are carefully selected from the data of coal - seam borehole hydraulic fracturing. After adding noise at 12 levels and performing translation with 10 different degrees of displacement, 101,123 pieces of background noise and 102,546 effective waveforms are obtained. Subsequently, by applying the information - propagation dynamics model of computational communication, microseismic signals are regarded as information carriers. A signal - propagation network is constructed, and features such as network degree distribution are extracted. These features, combined with traditional time - domain and frequency - domain features, are converted into time - domain and Fourier images and then input into a two - dimensional CNN model. Experiments show that the time - domain CNN model achieves a precision rate of 100% and a recall rate of 68% in microseismic event identification, significantly outperforming traditional methods such as AIC, STA/LTA, and the Fourier CNN model. Furthermore, the time-frequency fusion CNN model—integrating time-domain waveforms, Fourier frequency-domain features, and time-frequency characteristics (e.g., short-time Fourier transform)—achieves an identical precision rate of 100% and a higher recall rate of 72%, outperforming the single-domain time-domain CNN model. The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.

Abstract To improve the compactness, broadband, high gain and wide coverage performance of the shortwave antenna (array), this paper introduces the array technology from the LPDA unit antenna, establishes the compact optimization model of the 2×3 elements LPDA fan-shaped array, and proposes an optimization method applied to the broadband decoupling and grating lobe suppression for LPDA fan-shaped phased array, taking the broadband low coupling and non-grating lobe as constraints; By using phased array technology, the wide scanning characteristics of LPDA fan-shaped array are analysed, and the influence of antenna parameters on the mutual coupling is studied when LPDA phased array widely scan. Finally, the feasibility of the truss based 2×3 elements LPDA fan-shaped phased array with a scale of 1:60 is verified through tests. The fan-shaped phased array has a frequency coverage of 13~28 MHz, an average gain of 17.5 dBi in the band, an average beam width of ≥ 30 °, and a scanning range of ≥ 90 °. The proposed array has the characteristics of broadband, low coupling, high gain, wide scanning and compactness. The proposed joint optimization method provides a very promising technical means for the optimization design of complex multi-dimensional phased arrays.

Aiming at the problem that existing equipment importance evaluation methods fail to consider interconnectivity between pieces of equipment, variability after maintenance, and the impact of dynamically changing situations on importance, and focusing on the dynamic support needs of equipment in a conflict environment, this paper proposes a batch allocation method for equipment maintenance tasks considering dynamic importance. The purpose of this study is to determine the batch priority of equipment maintenance based on the dynamically changing importance of pieces of equipment. First, a dynamic importance index system is constructed: a real-time CRITIC-AHP combined weighting method is used to calculate team importance, a dynamic Bayesian network (DBN)-influenced method is used to calculate relative importance, an attention–LSTM time-series prediction method is used to calculate future importance, and then a dynamic entropy weight method is adopted to objectively integrate the three types of importance. Second, a dual-objective optimization model with the maximum equipment importance and the minimum total maintenance time is built, with mobile distance, maintenance time, and maintenance capacity as constraints. The Dynamic Particle Swarm Optimization (DPSO) algorithm is used to solve this model, and its dynamic adaptability is improved through environmental change detection and adaptive adjustment of inertia weight. Finally, the batch allocation of maintenance tasks is realized. Example verification shows that compared with the expert scoring method, the errors of the three importance calculation methods are all reduced by more than 60%, the optimization speed of the dynamic PSO algorithm is 47% faster than that of the static algorithm, and the constructed model has good stability. This method can provide a reference for maintenance support command decisions.

Aiming at the problems of high complexity in source domain data, inaccessibility of target domain data, and unknown fault patterns in real-world industrial scenarios for mechanical fault diagnosis, this paper proposes a Feature Decomposition-based Source-Free Universal Domain Adaptation (FD-SFUniDA) framework for mechanical equipment fault diagnosis. First, the CBAM attention module is incorporated to enhance the ResNet-50 convolutional network for extracting feature information from source domain data. During the target domain adaptation phase, singular value decomposition is applied to the weights of the pre-trained model’s classification layer, orthogonally decoupling the feature space into a source-known subspace and a target-private subspace. Then, based on the magnitude of feature projections, a dynamic decision boundary is constructed and combined with an entropy threshold mechanism to accurately distinguish between known and unknown class samples. Furthermore, intra-class feature consistency is strengthened through neighborhood-expanded contrastive learning, and semantic weight calibration is employed to reconstruct the feature space, thereby suppressing the negative transfer effect. Finally, extensive experiments under multiple operating conditions on rolling bearing and reciprocating mechanism datasets demonstrate that the proposed method excels in addressing source-free fault diagnosis problems for mechanical equipment and shows promising potential for practical engineering applications in fault classification tasks.

With increasingly scarce spectrum resources, frequency-domain signal overlap interference has become a critical issue, making multi-user modulation classification (MUMC) a significant challenge in wireless communications. Unlike single-user modulation classification (SUMC), MUMC suffers from feature degradation caused by signal aliasing, feature redundancy, and low inter-class discriminability. To address these challenges, this paper proposes a collaborative “separation–recognition” framework. The framework begins by separating overlapping signals via a band partitioning and FastICA module to alleviate feature degradation. For the recognition phase, we design a dual-branch network: one branch extracts prior knowledge features, including amplitude, phase, and frequency, from the I/Q sequence and models their temporal dependencies using a bidirectional LSTM; the other branch learns deep hierarchical representations directly from the raw signal through multi-scale convolutional layers. The features from both branches are then adaptively fused using a gated fusion module. Experimental results show that the proposed method achieves superior performance over several baseline models across various signal conditions, validating the efficacy of the dual-branch architecture and the overall framework.

In severe reactor accidents, molten corium solidifies within the core to form a corium crust. Under decay heat, the high-temperature corium crust induces contact melting of internal reactor components. Given the narrow and limited dimensions of these components, this study investigated the contact melting of a corium crust against a stainless steel plate. A three-dimensional plate contact melting model for plate-shaped corium is proposed, with its validity demonstrated through experimental verification. The patterns and factors influencing contact melting were analyzed. The results indicate that under constant heat flux boundary conditions, the melting rate depends solely on the magnitude of the heat flux density, while the effects of the contact surface geometry and heat source mass on the melting rate are negligible. The thickness of the molten liquid film is proportional to both the heat flux density and contact surface area, yet inversely proportional to both the heat source mass and aspect ratio of the contact surface. When the aspect ratio exceeds six, the model can be simplified to two dimensions.