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Ultra high frequency (UHF) partial discharge (PD) detection is a common method for transformer oil-paper insulation defect location. However, the accuracy of PD positioning in practical application is easily affected by noise and sensor layout. In order to ensure the effectiveness of PD positioning detection of transformer oil-paper insulation defect, an UHF PD positioning detection platform for oil-paper insulation defects is established. Based on the conventional K-means method, a modified clustering demarcation PD anti-interference positioning method is proposed, which effectively reduces the positioning error. Aiming at the problem of sample clustering demarcation aliasing, when the optimal correction coefficient L is 1.1, the UHF PD positioning error can be reduced to less than 0.1 m, which verifies the effectiveness of the proposed method. Finally, the variation law of positioning error of different sensor layout is analyzed, and the optimal layout scheme of UHF sensor for transformer oil-paper insulation defect detection is proposed, which can provide reference for the layout and positioning of transformer on-line monitoring sensor.

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The insulation performance of transformer bushings is a crucial aspect that directly affects the safe operation of equipment. To diagnose the insulation status of transformer bushings and mitigate the impact of small-sample imbalanced data on diagnostic results, a particle swarm optimization combined with back propagation neural network (PSO-BPNN) and adaptive synthetic sampling (ADASYN) method are employed to fault diagnosis of transformer bushing. Initially, historical fault data of transformer bushings are gathered, and a sample set of dissolved gases in transformer oil with distinct fault categories is established. The ADASYN algorithm is used to synthesize the minority class samples in the original data, which allowed for obtaining balanced fault data. The balanced dissolved gases in oil served as the model input, and the fault status is used as the label output to diagnose the transformer bushings using the PSO-BPNN model. To diagnose the bushings under the original sample set, the back propagation neural network (BPNN), genetic combined with back propagation neural network (G-BPNN), cuckoo search combined with back propagation neural network (CS-BPNN), and PSO-BPNN models are used. The results reveal that the PSO-BPNN model based on ADASYN balanced data exhibited the highest accuracy among the various models for fault diagnosing the insulation status of transformer bushings. This approach effectively mitigate the impact of small sample imbalanced data on diagnostic results, and provide an effective method for assessing the insulation performance of transformer bushings.

Three kinds of commercial ink-jet printing paper were taken as the research objects, which were represented by sample A, sample B and sample C respectively. Sample A and sample C were domestic products Abbott and Jiayi, and sample B was foreign product Gulou. The structure and composition of the three sample coatings were determined, and the surface properties of the three coatings were analyzed. This helps to understand the performance status of inkjet paper products on the market, and has guiding significance for the study of various factors affecting the performance of inkjet paper coatings. AFM and SEM were used to observe the surface morphology of the three samples. It was found that the coating pigment particles of sample A and sample B had good dispersion uniformity, and the coating pigment particles of sample C aggregated and the dispersion uniformity was poor. The results of cross-section SEM and EDS analysis show that only sample B coating has a double-layer structure, in which the bottom layer is aluminum-based pigment and silicon-based pigment, and the surface layer is only aluminum-based pigment. The coatings of sample A and sample C are single-layer aluminum-based materials. The properties of the coating surface of the three samples were analyzed. The results showed that the basic properties of the three samples met the product quality standards. Among them, the coating of sample B had the highest gloss (74.30%), hardness (3H) and water resistance. In terms of ink absorption, the ink absorption of sample B coating (64.7%) was higher than that of sample C (57.9%), slightly lower than that of sample A (69.2%). Sample B had the best comprehensive performance among the three samples. Finally, a double-layer coating process scheme for improving the performance of inkjet printing paper is proposed, which provides a direction for improving the performance of inkjet printing paper and has practical significance for the research of advanced inkjet printing paper. [ABSTRACT FROM AUTHOR]

Utilizing the iron-carbon microelectrolysis technology to carry out in-depth treatment of waste paper and pulp wastewater, the optimal process parameters were determined based on single factor experiments. In addition, the response surface methodology was employed to optimize the process parameters by analyzing the experimental results. Under the optimized conditions, the excita1tion-emission-matrix spectra and GC-MS were used to analyze the water quality of the treated wastewater. The results showed that the optimal treatment conditions of iron-carbon microelectroly1sis process were initial pH=3.0, filler addition amount 255g L-1, reaction time 120 min, aeration amount 0.9 L min-1, and the COD removal efficiency was 32. 14% under these conditions; the maximum predicted value of COD removal rate calculated under the optimal process conditions was 34.57% from the response surface analysis, which was basically consistent with the experimental results. The results of excitation-emission-matrix spectra analysis showed that the iron-carbon micro1electrolysis process was effective in degrading humic acid and fulvic acid in waste paper and paper wastewater, and GC-MS analysis showed that the relative content of eicosanoids in wastewater was reduced from 43.31% to 4.79% of the original water after the iron-carbon microelectrolysis treat1ment, but a small amount of new pollutants were generated at the same time. [ABSTRACT FROM AUTHOR]

Traditional paper dyes are easy to fade under photothermal conditions, in contrast. waterborne organic pigments have excellent tinctorial strength and high light and weather resistance, which can meet the requirements of highend paper yarn to coloring efficacy and light fastness of the colorants. However, the lack of chemical and hydrogen bonds between organic pigment particles and plant fibers, and the pigments are difficult to fix in the structure of the paper. In this study, cellulose nanofibers (CNF/TOCNF) were used as the primary skeleton network and carboxymethyl cellulose (CMC) macromolecules were used as the secondorder reinforcement network, together construct a multilevel gradient network to enclose and fix organic pigment particles. The dispersion and fixation of the pigment particles are realized by double coating. The results show that the coloring efficacy and colorfastness of the paper yarn base paper can be improved significantly by using the gradient network to fix the pigment particles, and has a positive effect on improving the strength and roughness of the paper. This study has an important role in solving the problem of coloring technology in the production of high-end paper yarn. [ABSTRACT FROM AUTHOR]

With the rapid development of flexible electronics industry, the conventional PET, PEN and other plastic substrates are unable to satiate the requirement due to higher cost and low light extraction efficiency. Paper has broad prospects as a flexible substrate due to its wide access, low-cost and thinness. In this paper, we integrated epoxy resin into sulfuric acid paper to fabricate a new type of substrate: plastic-paper. The plastic-paper has excellent optical properties, mechanical properties as well as water and chemical stability, as found through testing. Results show that the surface flatness and wettability of the paper are improved by the application of epoxy resin coating. The plastic-paper has a high optical transmittance up to 88.25%, reaching the same level with PET substrate. And the plastic-paper also has a high transmittance haze of 62.9%, which is far higher than PET substrate. The plastic-paper has a tensile strength up to 108.4 MPa, along with excellent water and chemical stability. Successful fabrication the organic light-emitting device on the plastic-paper surface and emitted uniform light based on the principle of light-emitting electrochemical cell (LEC), under atmospheric condition. The best device performance with a maximum luminance of 396. 2 cd m-2, a maximum current efficiency of 0.64 cd A-1, which is 2.9 times higher than that of PET substrates, providing more excellent optoelectronic properties. The developed plastic-paper with high optical transmittance and high optical haze holds a great potential in flexible optoelectronic devices. [ABSTRACT FROM AUTHOR]

Considering the uncertainty of market demand, the uncertainty of the number of returned products and the risk of facility interruption, the carbon quota policy and the construction of new energy facilities are taken as the key factors affecting the decision-making. A dual-objective robust optimization model with minimum total cost and carbon emissions of supply chain network is established, and a Prim-NSGA II algorithm is designed to solve the model. The effectiveness of the algorithm is verified by comparing with the IGD and SM indexes of the traditional NSGA II algorithm. The optimal network planning scheme under different uncertainties and the management countermeasures under different parameters are obtained. The results show that the algorithm achieves the purpose of accelerating the convergence speed and maintaining the diversity of the population. It is verified that the robust optimization model can realize the comprehensive improvement of the robustness, economic and environmental benefits of the closed-loop supply chain network, and provide decision-making basis for decision makers to formulate specific carbon emission reduction schemes. [ABSTRACT FROM AUTHOR]

Traditional federated learning faces challenges such as high communication costs, structural heterogeneity,and insufficient privacy protection. To address these issues, this paper proposes a federated learning evolutionary algorithm that applies sparse evolutionary training algorithm to reduce communication costs and integrates local differential privacy protection for participants’ privacy. Additionally, it utilizes the NSGA-Ⅲ algorithm to optimize the network structure and sparsity of the global federated learning model, adjusting the relationship between data availability and privacy protection. This achieves a balance between the effectiveness, communication costs, and privacy of the global federated learning model. Experimental results under unstable communication environments demonstrate that, on the MNIST and CIFAR-10 datasets, compared to the solution with the lowest error rate using the FNSGA-Ⅲ algorithm, the proposed algorithm improves communication efficiency by 57. 19% and 52. 17%, respectively. The participants also achieved（3. 46, 10-4） and（6. 52, 10-4）-local differential privacy. This algorithm can effectively reduce communication costs and protect participant privacy without significantly compromising the accuracy of the global model. [ABSTRACT FROM AUTHOR]