Plastic photodegradation naturally takes 300–500 years, and their chemical degradation typically needs additional energy or causes secondary pollution. The main components of global plastic are polymers. Hence, new technologies are urgently required for the effective decomposition of the polymers in natural environments, which lays the foundation for this study on future plastic degradation. This study synthesizes the in-situ growth of TiO₂ at graphene oxide (GO) matrix to form the TiO₂@GO photocatalyst, and studies its application in conjugated polymers’ photodegradation. The photodegradation process could be probed by UV-vis absorption originating from the conjugated backbone of polymers. We have found that the complete decomposition of various polymers in a natural environment by employing the photocatalyst TiO₂@GO within 12 days. It is obvious that the TiO₂@GO shows a higher photocatalyst activity than the TiO₂, due to the higher crystallinity morphology and smaller size of TiO₂, and the faster transmission of photogenerated electrons from TiO₂ to GO. The stronger fluorescence (FL) intensity of TiO₂@GO compared to TiO₂ at the terephthalic acid aqueous solution indicates that more hydroxyl radicals (•OH) are produced for TiO₂@GO. This further confirms that the GO could effectively decrease the generation of recombination centers, enhance the separation efficiency of photoinduced electrons and holes, and increase the photocatalytic activity of TiO₂@GO. This work establishes the underlying basic mechanism of polymers photodegradation, which might open new avenues for simultaneously addressing the white pollution crisis in a natural environment.

Non-Intrusive Load Monitoring (NILM) is an effective energy consumption analysis technology, which just requires voltage and current signals on the user bus. This non-invasive monitoring approach can clarify the working state of multiple loads in the building with fewer sensing devices, thus reducing the cost of energy consumption monitoring. In this paper, an NILM method combining adaptive Recurrence Plot (RP) feature extraction and deep-learning-based image recognition is proposed. Firstly, the time-series signal of current is transformed into a threshold-free RP in phase space to obtain the image features. The Euclidean norm in threshold-free RP is scaled exponentially according to the voltage and current correlation to reflect the working characteristics of different loads adaptively. Afterwards, the obtained adaptive RP features can be mapped into images using the corresponding pixel value. In the load identification stage, an advanced computer vision deep network, Hierarchical Vision Transformer using Shifted Windows (Swin-Transformer), is applied to identify the adaptive RP images. The proposed solution is extensively verified by four real, measured load signal datasets, including industrial and household power situations, covering single-phase and three-phase electrical signals. The numerical results demonstrate that the proposed NILM method based on the adaptive RP can effectively improve the accuracy of load detection.

The recognition of non-line-of-sight (NLOS) state is a prerequisite for alleviating NLOS errors and is crucial to ensure the accuracy of positioning. Recent studies only identify the line-of-sight (LOS) state and the NLOS state, but ignore the contribution of occlusion categories to spatial information perception. This paper proposes a bidirectional search algorithm based on maximum correlation, minimum redundancy, and minimum computational cost (BS-mRMRMC). The optimal channel impulse response (CIR) feature set, which can identify NLOS and LOS states well, as well as the blocking categories, are determined by setting the constraint thresholds of both the maximum evaluation index, and the computational cost. The identification of blocking categories provides more effective information for the indoor space perception of ultra-wide band (UWB). Based on the vector projection method, the hierarchical structure of decision tree support vector machine (DT-SVM) is designed to verify the recognition accuracy of each category. Experiments show that the proposed algorithm has an average recognition accuracy of 96.7% for each occlusion category, which is better than those of the other three algorithms based on the same number of CIR signal characteristics of UWB.

In rotary ultrasonic machining (RUM), the utiliza-tion of contactless energy transfer (CET) theory addresses the limitations imposed by traditional contact power supply systems on rotational speed, however, it also introduces the challenge of inadequate transmission performance. Specifically, impedance variations of oscillator pose challenges for the contactless energy transfer system (CETS). These challenges include maintaining high transmission efficiency and desirable impedance charac-teristics, which can impact the performance of system and the quality of workpiece. To address the issue, this paper proposes the implementation of a bi-lateral compensation network using an LC-CL structure to against the low output power and poor anti-interference capabilities of series-series (SS) topology. Addi-tionally, a variable loads parameter optimization (VLPO) model, based on the fluctuations of equivalent dynamic component parameters during actual machining, is proposed. This model integrates an efficiency evaluation function aimed at achieving higher transmission efficiency and reducing fluctuations, as well as evaluation functions for the quality factor, admittance circle radius, and bandwidth to enhance impedance characteristics. Through the implementation of VLPO, the system impedance characteristics of LC-CL are significantly improved in compar-ison to SS and carrying capacity is effectively enhanced. The effectiveness of this scheme is validated through the construction of CETS experimental platform.