Aggregation of nanosized materials in composite lithium-ion-battery electrodes can be a significant factor influencing electrochemical behavior. In this study, aggregation was controlled in magnetite, Fe3O4, composite electrodes via oleic acid capping and subsequent dispersion in a carbon black matrix. A heat treatment process was effective in the removal of the oleic acid capping agent while preserving a high degree of Fe3O4 dispersion. Electrochemical testing showed that Fe3O4 dispersion is initially beneficial in delivering a higher functional capacity, in agreement with continuum model simulations. However, increased capacity fade upon extended cycling was observed for the dispersed Fe3O4 composites relative to the aggregated Fe3O4 composites. X-ray absorption spectroscopy measurements of electrodes post cycling indicated that the dispersed Fe3O4 electrodes are more oxidized in the discharged state, consistent with reduced reversibility compared with the aggregated sample. Higher charge-transfer resistance for the dispersed sample after cycling suggests increased surface-film formation on the dispersed, high-surface-area nanocrystalline Fe3O4 compared to the aggregated materials. This study provides insight into the specific effects of aggregation on electrochemistry through a multiscale view of mechanisms for magnetite composite electrodes.
Abstract With the scale of the power grid is becoming larger and larger. The corresponding operation and maintenance work of the distribution network is becoming increasingly intelligent, which also puts forward corresponding requirements for the reliability of safe and stable operation of the power grid. The condition assessment of relay protection applies the scientific concept of condition-based maintenance to the actual work site, which is of great significance. To judge the operation status of the equipment and provide a scientific basis for maintenance, in this paper, based on the application of digital twin technology in microgrid operation and maintenance, the method of calculating reliability probability density by function is applied to solve the problems of fuzziness, gray, and randomness of the relay protection status index. The invention can evaluate the state of the relay protection of the power system and can timely and accurately put forward the corresponding relay protection inspection and maintenance scheme, thereby improving the maintenance efficiency of the relay protection and further strengthening the power supply reliability of the power system. It can ensure the security and stability of the power system and reduce the reliable power consumption of the majority of power users.
In this study, a novel gas-extruded membrane bearing was developed, and an optimization algorithm was applied to solve a Reynolds equation that describes the load-bearing characteristics of this bearing. This was effective in improving the solution rate of the Reynolds equation, significantly reducing the difficulty of obtaining a solution, avoiding high programming difficulty, and achieving a high solution accuracy. Through a comparative analysis, the error in the accuracy of the alternating implicit difference method was addressed, and the traditional finite element method for solving the same model was verified, with an average error of 2% reached to verify its applicability. The algorithm was also used to analyze the load-bearing capacity of the gas-extruded membrane bearing. This revealed not only a positive correlation of the average load-bearing capacity of the gas-extruded membrane bearing with the frequency and amplitude of vibration but also a negative correlation with radial clearance, with the cut-off frequency reaching 19 Khz. The load-bearing capacity of the gas-extruded membrane bearing proposed in this paper reached 1.28 N, which indicated an error of 3.28% with the theoretical approach. To sum up, this research provides an important reference for the design and manufacture of novel gas-extruded membrane bearings.
Power systems are essential to national security, economic prosperity, public health, and safety. However, as the frequency of extreme events and man-made attacks has increased dramatically in recent years, making resilience theory has become a new direction for responding to low-probability high-impact events. In power systems, resilience is essential in maintaining functionality, reducing losses, and speeding up recovery when encountering a disruptive event. This study develops a resource optimization allocation framework based on multiple resilience objectives by understanding the relationship between resilience performance and dynamic decisions. A multi-resilience-objective mixed-integer linear programming (MROMILP) model is formulated to optimize the resource allocation scheme for each resilience stage under limited internal resources of power systems under hurricane hazards. The IEEE 30-bus test system is used to validate the usability of the model.
Recent advancements have highlighted the Mamba framework, a state-space model known for its efficiency in capturing long-range dependencies with linear computational complexity. While Mamba has shown competitive performance in medical image segmentation, it encounters difficulties in modeling local features due to the sporadic nature of traditional location-based scanning methods and the complex, ambiguous boundaries often present in medical images. To overcome these challenges, we propose Uncertainty-Driven Mamba (UD-Mamba), which redefines the pixel-order scanning process by incorporating channel uncertainty into the scanning mechanism. UD-Mamba introduces two key scanning techniques: 1) sequential scanning, which prioritizes regions with high uncertainty by scanning in a row-by-row fashion, and 2) skip scanning, which processes columns vertically, moving from high-to-low or low-to-high uncertainty at fixed intervals. Sequential scanning efficiently clusters high-uncertainty regions, such as boundaries and foreground objects, to improve segmentation precision, while skip scanning enhances the interaction between background and foreground regions, allowing for timely integration of background information to support more accurate foreground inference. Recognizing the advantages of scanning from certain to uncertain areas, we introduce four learnable parameters to balance the importance of features extracted from different scanning methods. Additionally, a cosine consistency loss is employed to mitigate the drawbacks of transitioning between uncertain and certain regions during the scanning process. Our method demonstrates robust segmentation performance, validated across three distinct medical imaging datasets involving pathology, dermatological lesions, and cardiac tasks.
The paper puts forward the multi factor model for the assessment of information discourse effect,gives a simplified,applied example,and explores the expression forms of information disclosure effect and the assessment model for information efficiency.
In the face of complex scene images, the introduction of depth information can greatly improve the performance of salient object detection. However, up-sampling and down-sampling operations in neural networks maybe blur the boundaries of objects in the saliency map, thereby reducing the performance of salient object detection. Aiming at this problem, a boundary-driven cross-modal and cross-layer fusion network (BC 2 FNet) for RGB-D salient object detection is proposed in this paper, which preserves the boundary of the object by adding the guidance of boundary information to the cross-modal and cross-layer fusion, respectively. Firstly, a boundary generation module is designed to extract two kinds of boundary information from low-level features of RGB and depth modalities, respectively. Secondly, a boundary-driven feature selection module is designed, which is dedicated to simultaneously focusing on important feature information and preserving boundary details in the process of RGB and depth modality fusion. Finally, a boundary-driven cross-layer fusion module is proposed which simultaneously adds two kinds of boundary information in the process of up-sampling fusion on adjacent layers. By embedding this module into the top-down information fusion flow, the predicted saliency map can contain accurate objects and sharp boundaries. Simulation results on five standard RGB-D data sets show that the proposed model can achieve better performance.
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