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In the field of paper conservation and archival research, identifying the raw materials of paper is important to elucidate its history and culture. As the most basic element of the raw materials for paper, fibers have not been sufficiently investigated. In this study, we propose a nondestructive method for estimating paper fibers from macro photographs ( $4000\times3000$ pixels) captured using a digital camera. The proposed method consists of background patch (500 pixels per side) detection (BPD), wherein background regions with no text are identified; patch fiber classification (PFC), wherein background patches obtained after BPD are analyzed for fiber classification; and paper fiber estimation (PFE), wherein macro images obtained after PFC are analyzed for fiber estimation. BPD and PFC are employed to perform patch-based classification on segmented macro images, which are reconstructed during PFE to obtain the final fiber estimation results. We performed experiments using 1337 macro images (64176 patches) to evaluate the fiber estimation accuracy for kozo, mitsumata, and gampi via three-fold cross-validation. The average fiber classification accuracy for patch images was observed to be 79.1%; accordingly, the average fiber estimation accuracy for macro images was 85.8%. Experimental results indicated that PFE can be realized in a nondestructive manner on macro images of paper captured using a digital camera.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

Abstract Oil‐paper insulation material is an essential component for oil‐filled transformers, and polyimide (PI) insulation paper is a novel material in the field of electricity. The improvement of insulation class for insulation paper has gained significant attention in recent years. In this study, SiO2 nanoparticles were modified with a silane coupling agent (KH550), and the KH550‐SiO2 was added to the fibre membrane through in situ polymerisation. The chemical composition and microcosmic characteristics of the PI porous fibre membrane were characterised using FT‐IR and SEM FT‐IR and SEM. Consequently, the dielectric constant of the composite paper was reduced to 2.3 upon adding 3 wt% of KH550‐SiO2, and the dielectric strength reached its maximum value of 85 kV/mm. The results demonstrate that the insulation performance of the KH550‐SiO2/PI composite was significantly improved due to the incorporation of nano‐SiO2. This establishes an efficient approach for achieving excellent electrical and thermal properties in practical applications using the KH550‐SiO2/PI fibre membrane.

Abstract The moisture generated by the ageing of the oil‐paper insulating system can easily lead to the deterioration of the performance of the insulating paper. In order to alleviate the impact of moisture on the performance of the insulating paper, we developed a kind of lipophilic hydrophobic self‐repairing cellulose insulating paper (new insulating paper) based on the ternary system of polytetrafluoroethylene, fluorocarbon surfactant and fluorinated alkyl silane, and accelerated thermal ageing test for 45 days at 130°C was carried out. The experimental results show that the new insulating paper with excellent lipophilic property can realise hydrophobic self‐repairing through heat treatment after physical damage. The tensile strength and elongation at break of the new insulating paper increased by 5%–8% and 6.25%–27% respectively compared with the original cellulose insulating paper. At the end of ageing, the relative dielectric constant and dielectric loss factor of new insulating paper are 10.4% and 9.9% lower than that of the original cellulose insulating paper, while the breakdown voltage is 6.8% higher. The acid and moisture content produced during the ageing process of the insulating oil impregnated with new insulating paper are also less, indicating that the coating can delay the ageing of the insulating paper.

Abstract The ionic transport process is an important counterpart to charge transport in oil‐paper insulation, and it significantly impacts oil flow electrification at the oil‐paper interface. Despite this, the dynamics of this phenomenon and the underlying mechanisms remain unclear, particularly at the molecular level. To understand this fundamental aspect, we conduct Molecular Dynamics study on the transport behaviour of an impurity ion in different oil‐paper insulation models under various external electric fields. Different influence factors, such as external electric fields, temperatures, and local structural characteristics, are investigated in relation to the corresponding ionic mobility in different polymer models. According to the simulations, ionic mobility and its response to electric fields are higher in weaker electrostatic models. As a result, mineral oil exhibits the highest ionic mobility and the most substantial enhancement of ionic mobility by external electric fields, followed by vegetable oil, oil‐paper blends, and insulating paper. This significant deviation in ionic mobility between oil and paper leads to the formation of an electric double layer near the oil‐paper interface. The underlying physical mechanisms of different ionic mobility and its response to the electric field in different polymer models could be explained by the different polymer structural influences in terms of interaction energy and coordination numbers in a static manner, as well as by the interactions between the impurity ion and its surrounding atoms in terms of lifetime correlation functions and velocity autocorrelation functions in a dynamic manner. In addition, the influence of temperature on ionic mobility in mineral and vegetable oil is examined, and their activation energies are calculated. Advancing in the fundamental understanding of the dynamics of the ion transport process in oil‐paper insulation is vital to improving their insulating properties for oil‐impregnated power transformers.

This paper discusses the results of the micro-Raman analysis performed on paper-like graphene oxide (GO) samples consisting of many functionalised graphene layers and annealed at moderate temperatures (≤500 °C) under vacuum conditions (p ≃ 10−4 mbar). The analysis of the standalone samples revealed that the obtained material is characterised by a noticeable disorder level but still stays below the commonly accepted threshold of high or total disorder. GO formed in a simple way showed two spectral bands above 1650 cm−1 recorded very rarely or not at all and their origin has been discussed in detail. The results also confirmed the metastable character of multilayer GO after the annealing process at moderate temperatures as the C/O ratio was kept between 2 and 3 and the spectral features were stable within the annealing temperature range.

The dynamic mechanical behavior and cushioning performance of honeycomb sandwich panels, which are extensively employed in product cushioning packaging due to their exceptional energy absorption capabilities, were examined using a combination of experimental and numerical methods. Several factors, such as maximum acceleration–static stress, cushioning coefficient–static stress, and other curves, were analyzed under various impact conditions. The simulated stress–strain, deformation modes, cushioning coefficients, and other parameters demonstrate consistency with the experimental results. The acceleration, maximum compression, and cushioning coefficient obtained from the experiment and simulation calculation were 30.68 g, 15.44 mm, and 2.65, and 31.96 g, 14.91 mm, and 2.79, respectively. The results indicate that all error values were less than 5%, confirming the precision and reliability of the model. Furthermore, the model was utilized to simulate and predict the cushioning performance of honeycomb sandwich panels with different cell structures and paper thicknesses. These results provide a solid basis for enhancing the design of subsequent honeycomb element structures.

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

The surface-enhanced Raman scattering (SERS) properties of low-dimensional semiconducting MXene nanoflakes have been investigated over the last decade. Despite this fact, the relationship between the surface characteristics and SERSing performance of a MXene layer has yet to be comprehensively investigated and elucidated. This work shows the importance of surface morphology on the overall SERS effect by studying few-layer Ti3C2Tx MXene-based SERS substrates fabricated by vacuum-assisted filtration (VAF) and spray coating on filter paper. The VAF deposition results in a dense MXene layer suitable for SERS with high spot-to-spot and substrate-to-substrate reproducibility, with a significant limit of detection (LoD) of 20 nM for Rhodamine B analyte. The spray-coated MXenes film revealed lower uniformity, with a LoD of 50 nM for drop-casted analytes. Moreover, we concluded that the distribution of the analyte deposited onto the MXene layer is affected by the presence of MXene aggregates created during the deposition of the MXene layer. Accumulation of the analyte molecules in the vicinity of MXene aggregates was observed for drop-casted deposition of the analyte, which affects the resulting SERS enhancement. Ti3C2Tx MXene layers deposited on filter paper by VAF offer great potential as a cost-effective, easy-to-manufacture, yet robust, platform for sensing applications.