The presented paper proposes a photovoltaic module hot spot detection algorithm based on YOLOv8‐BCB. The algorithm addresses issues such as component efficiency degradation and poor contact in long‐term operation of PV systems, with a focus on the hot spot effect’. To achieve rapid detection and localization of small targets in complex scenes, the algorithm incorporates the Weighted Bi‐directional Feature Pyramid Network (BiFPN) into YOLOv8. Additionally, a lightweight upsampling operator called Content‐Aware ReAssembly of FEatures (CARAFE) is used to reduce the detection load on the unmanned aerial vehicle (UAV) detection system. The BiFormer attention mechanism is also integrated into the C2f module to better capture dependencies between sequences and reduce background interference. To highlight the superiority of the authors’ algorithm compared to other high‐quality algorithms, the authors conducted comparative experiments on the same dataset. The YOLOv8‐BCB algorithm surpasses the SSD, faster‐cnn, and RetinaNet algorithms in both accuracy and detection speed. It achieves a precision of 97.1% and a recall rate of 94.9%, with an FPS of 110. The result is a target detection algorithm that is both fast and accurate.

SnO₂ is deemed a potential candidate for high energy density (1494 mAh g⁻¹) anode materials for Li-ion batteries (LIBs). However, its severe volume variation and low intrinsic electrical conductivity result in poor long-term stability and reversibility, limiting the further development of such materials. Therefore, we propose a novel strategy, that is, to prepare SnO₂ hollow nanospheres (SnO₂-HNPs) by a template method, and then introduce these SnO₂-HNPs into one-dimensional (1D) carbon nanofibers (CNFs) uniformly via electrospinning technology. Such a sugar gourd-like construction effectively addresses the limitations of traditional SnO₂ during the charging and discharging processes of LIBs. As a result, the optimized product (denoted SnO₂-HNP/CNF), a binder-free integrated electrode for half and full LIBs, displays superior electrochemical performance as an anode material, including high reversible capacity (~735.1 mAh g⁻¹ for half LIBs and ~455.3 mAh g⁻¹ at 0.1 A g⁻¹ for full LIBs) and favorable long-term cycling stability. This work confirms that sugar gourd-like SnO₂-HNP/CNF flexible integrated electrodes prepared with this novel strategy can effectively improve battery performance, providing infinite possibilities for the design and development of flexible wearable battery equipment.

Traditional deep learning methods for the satellite gyroscope’s rolling bearing fault diagnosis often suffer from low efficiency and poor robustness. The paper presents an advanced fault diagnosis model that enhances traditional convolutional neural networks (CNN) to address these limitations. The proposed model utilizes an innovative architecture consisting of a hierarchical fusion of multidimensional features, convolutional block attention module (CBAM), residuals and fast fourier transform (FFT) to process raw data into a one-dimensional signal format suitable for model input. This design facilitates efficient feature extraction and data processing, leading to the output of classification results through a specially designed classification header. Compared with a single model, the proposed model has a 4.97% improvement on the gyroscope-bearing fault diagnosis dataset. In addition, compared with existing deep learning methods, our model offers significant improvements in feature extraction and parallel computing capabilities. Notably, it achieves good performance in presenting strong noise interference with only a small amount of computational resources (parameters: 10.005 K) and (MFLOPs: 0.792). The diagnostic model can be practically applied to sensor-equipped rotating machinery for real-time online monitoring and early detection of potential bearing faults. It provides new ideas for subsequent research; Based on the existing methods, more efficient and robust solutions can be developed through the optimization combination of different methods.

In this paper, we propose a novel kinematic and inverse dynamic model for the flybar-less (FBL) swashplate mechanism of a small-scale unmanned helicopter. The swashplate mechanism is an essential configuration of helicopter flight control systems. It is a complex, multi-loop chain mechanism that controls the main rotor. In recent years, the demand for compact swashplate designs has increased owing to the development of small-scale helicopters. The swashplate mechanism proposed in this paper is the latest architectures used for hingeless rotors without a Bell-Hiller mixer. Firstly, the kinematic analysis is derived from the parallel manipulators concepts. Then, based on the principle of virtual work, a methodology for deriving a closed-form dynamic equation of the FBL swashplate mechanism is developed. Finally, the correctness and efficiency of the presented analytical model are demonstrated by numerical examples and the influence factors of the loads acted on actuators are discussed.

Phthalocyanine-sensitized TiO₂ significantly enhances photocatalytic performance, but the method of phthalocyanine immobilization also plays a crucial role in its performance. In order to investigate the effect of the binding strategy of phthalocyanine and TiO₂ on photocatalytic performance, a dual-pathway study has been conducted. On the one hand, zinc-tetra (N-carbonylacrylic) aminephthalocyanine (Pc) was directly grafted onto the surface of Fe₃O₄@SiO₂@TiO₂ (FST). On the other hand, Pc was immobilized on a silane coupling agent ((3-aminopropyl) triethoxysilane) grafted onto the surface of the FST. Through photocatalytic experiments on the two types of composite materials synthesized, the results showed that the photocatalyst obtained by directly sensitizing Pc (FSTP) exhibited better performance on rhodamine B(RhB) removal than did the other photocatalyst using the silane coupling agent (FSTAP). Further mechanistic studies showed that directly sensitized FSTP exhibited more efficient photogenerated electron–hole pair separation, whereas FSTAP linked by a silane coupling agent created an additional transport distance that might greatly affect the photogenerated electron transport. Therefore, the dual-pathway research in this work provides new guidance for efficiently constructing phthalocyanine-sensitized TiO₂ photocatalysts.

With the development of energy internet, multi‐energy coupling system has received wide attention. This paper addresses the economic dispatch optimization problem in coupled electricity‐gas energy systems, with a particular focus on the participation of renewable energy sources. A comprehensive model is proposed, incorporating renewable energy power generation, gas production, and electricity‐gas conversion processes. The model further refines the composition of multi‐energy loads, transitioning from traditional single‐energy loads to more complex multi‐energy loads. Additionally, a cost model is established that considers conversion losses, calculation coefficients, and conversion intervals, enhancing adaptability to future new energy system developments and optimal scheduling. A fully distributed optimization algorithm based on neural dynamics is introduced, allowing each energy node to minimize information exchange and perform local optimal power calculations. Theoretical and simulation proofs are provided to validate the effectiveness and efficiency of the proposed model and algorithm.