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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.

Computer scientists often publish their source code accompanying their publications, prominently using code repositories across various domains. Despite the concurrent existence of scholarly articles and their associated official code repositories, explicit references linking the two are often missing. Traditionally, identifying whether scholarly content and code repositories pertain to the same research project requires manual inspection, a time-consuming task. This paper proposes a deep learning-based algorithm for automatically matching scholarly articles with their corresponding official code repositories. Our findings indicate that the most common linking information includes the paper title and BibTeX entries, typically found in the repository’s readme document. In this study, we employed SPECTER for vector embedding of paper and repository metadata. Utilizing these embedding representations with the Light Gradient Boosting Machine (LGBM), our method achieved an F1 score of 0.94. Moreover, combining our best model with a rule-based approach improved performance by 5.31%. This study successfully delineates a connection between academic papers and associated official code repositories, minimizing reliance on explicit bibliographic information in repositories.

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.

Recently, Polarization Depolarization current (PDC) measurement is widely accepted time domain spectroscopy-based method for assessing the insulation condition. Various performance parameters like Dissipation factor (%tan $\delta $ ), Paper Moisture (%pm), Dielectric Adsorption Ratio (DAR), Polarization index (PI) etc. can be estimated by analyzing the PDC data. During field measurement various factors influences the recorded PDC data. As per existing literature, presence of low frequency noise, effect of temperature variation and influence of residual charge are common during field measurement. These factors significantly affect recorded polarization current and hence estimated performance parameters. Hence, analysis using recorded polarization current data may provide misleading information regarding insulation condition. Under such practical situation where polarization current is affected by above mentioned factors that generally observed during field measurement, depolarization current should be used for analysis of insulation condition. The depolarization current does not influence by such external factors. The present work shows the importance of depolarization current where polarization current is influenced by external low frequency noise and residual charge. The analysis firstly applied on sample prepared in the laboratory and then on data collected from real life in-situ transformers. The results obtained from the analysis shows that the data obtained from depolarization current is more reliable.

Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced.

Flashover characteristics are closely related to the electrical field distribution on the surface of oil-paper insulation. In this study, the effects of electrical field and DC voltage components on the creepage flashover characteristics of oil-paper insulation under combined AC-DC voltage (CADV) are investigated using two electrode models. The results demonstrate that when the DC voltage component is large, the flashover voltage under the influence of strong parallel electric field component is approximately 1.2 times higher than that under the influence of strong normal electric field component. When the DC voltage component value increases under the influence of strong normal electric field component, the value of corresponding AC voltage component of the creepage flashover voltage exhibits a decreasing trend. It is believed that due to the strong normal electric field component, the electrons intensify the collision on the surface of the insulating pressboard. Under the influence of the DC voltage component and due to the presence of space charge on the insulating pressboard, the distribution shape of carbonized particles on the surface of the insulating pressboard after creepage flashover is different. Furthermore, the instantaneous energy release during creepage flashover under CADV is higher than that under AC voltage, and the greater the DC voltage component value, more severe is the damage to the fibers on the insulating pressboard surface.

In on-site measurement, as the transformer’s structure is often unknown, the application of frequency domain spectroscopy (FDS) is greatly limited in moisture assessment of transformer. This paper proposes a moisture assessment method for oil-immersed paper based on the dynamic characteristic frequency even for an unknown transformer structure. FDS tests are performed on the oil-paper insulation, corresponding oil-immersed paper, and mineral oil under different moisture conditions. The moisture criterion of oil-immersed paper and the influence of the structural parameters on the calculated frequency spectroscopy at different frequencies are discussed. It is concluded that the moisture criterion’s effectiveness, which indicates the sensitivity of assessed moisture content to the error of the tan $\delta $ , changes at different frequencies. Moreover, there are some frequencies at which tan $\delta $ is not influenced by structural parameters and is close to the measured value, which are regarded as “characteristic frequencies.” Based on the characteristic frequency, the moisture content of oil-immersed paper is assessed according to the frequency spectroscopy of oil-paper insulation without the structural parameters. The result shows that the assessing frequency changes under different moisture conditions, and the error of the assessed moisture content is less than 0.1%.

The availability of the raw materials needed to produce fibre is decreasing as demand for fibre increases over time. In an effort, to preserve natural biodiversity and improve waste management, this research aims to substitute available raw materials for wood fibres. Empty fruit bunch (EFB) is regarded as waste in the oil palm industry and is abundantly available as a renewable energy source, particularly in Southeast Asian countries. The primary objective of this study is to investigate the utilisation of EFB as a substitute for insulating paper in transformers. The morphology, degree of polymerization, tensile strength, thermal characteristics, permittivity, conductivity, and electrical properties (breakdown field strength and lightning impulse performance) of EFB were measured and compared to the properties of Kraft insulating paper. Using FESEM at a magnification of $500\times $ , the morphological properties were examined. Based on FTIR measurement, the chemical bonding between Kraft and EFB samples varied slightly. Compared to EFB, Kraft-pulped insulating paper has a higher degree of polymerization and the ability to endure high temperatures. Furthermore, the breakdown field strength performance demonstrates that Kraft insulating paper is 20.30% higher than EFB insulating paper. In terms of tensile strength and lightning impulse, EFB insulating paper demonstrated comparable performance to conventional insulating paper. A new approach to the manufacturing of EFB insulating paper could result in improvements to the material’s insulation qualities.

This study aims to develop a prediction model for paper quality assessment to support technology-assisted peer review. The prediction technique is intended to reduce the review burden, which is becoming a critical issue in today’s paper submission process. However, most existing works on this topic were built by involving the reviewers’ comments, which is considered unfair and inapplicable for reducing the review burden. Therefore, our prediction method relies only on features extracted from the paper to address this issue. The method covers three tasks as follows: two are classification tasks and one is a regression task. The classification tasks predict the final review decision (accepted-rejected) and estimate the paper quality (good-poor), while a regression task predicts the review scores. Additionally, the classification and regression tasks are implemented using three main features i.e., citing sentence features developed based on the labeling scheme of citation functions, regular sentence features created by applying the label of citation functions to non-citation text, and reference-based features constructed by identifying the source of citations. Furthermore, the classification experiments on the dataset obtained from the International Conference on Learning Representations 2017–2020 showed that our methods are more effective in the good-poor task than the accepted-rejected task by demonstrating the best accuracy of 0.75 and 0.73, respectively. Moreover, we also reached a satisfactory recall of 0.99 using only the citing sentence features to obtain as many good papers as possible in the good-poor task. Our regression experiments indicate that the best result in predicting the average review score is higher than the individual review score by showing Root Mean Square Error (RMSE) of 1.34 and 1.71, respectively.

Reading newly-published papers in time is important for researchers since these papers provide the latest research findings. However, it is challenging to retrieve newly-published papers through common query-based search engines because the papers lacking sufficient citations and links are usually ranked too low in the search list. To this end, we design a time-aware joint model to infer users’ preference for the newly-published papers with the help of subsidiary relations of social and article linkages. The temporal preference of researchers for articles is jointly modeled with social and article relations by a group of matrices sharing common dimensions of researchers and articles. A joint multi-relational factorization algorithm is devised to approximate the latent factor matrices along with a temporal recommendation algorithm to predict the personalized new referential papers based on the factor matrices. The experimental results on real-world datasets show that the proposed model outperforms the state-of-the-art methods.

Robotic process automation (RPA) is automating various job processes in many offices. However, most existing RPA systems automate only processes that are performed on computers. It is highly desirable to develop physical RPA systems that incorporate real robots to automate physical job processes. When developing physical RPA systems, handling paper is particularly important because many physical office jobs involve handling paper documents. However, it is not easy for robots to handle paper because paper is fragile. To address this issue, we propose an electroadhesive paper gripper in this study. The use of electroadhesion enables a robot to handle paper in a simple, non-destructive, and quiet manner. We provide (1) a mechanism for lifting a sheet of paper without accurate control of robot hands, (2) a mechanism for a small gripper to hold a large sheet of paper without bending it, and (3) a method for quickly releasing a sheet of paper. Furthermore, we apply the proposed paper gripper to a document-sorting robot. The robot that we have developed automatically arranges a given stack of paper documents in a predetermined order (e.g., alphabetical or numerical). Experimental results show the validity of the paper gripper and the sorting robot.

The hydrophilic nature of cellulose compromises the dielectric properties of kraft paper insulation when exposed to water and moisture. The effort to address this setback is an ongoing research enquiry. In the present work, surface-modified rutile-titanium dioxide nanoparticles (rutile-TiO2 NPs) are reinforced into cellulose pulp to fabricate a nanocomposite kraft paper insulation. Surface modification of the nanofiller particles is intended to reduce the hydrophilicity of the resultant nanocomposite kraft paper insulation. The nanoparticles (rutile-TiO2 NPs) improve other dielectric properties of the insulation. The chemicals used for the surface modification of the rutile-TiO2 NPs were the alkyl ketene dimer (AKD) and alkenyl succinic anhydride (ASA). The nanofiller retention, dispersion and hydrophobic properties of the resultant reinforced kraft paper were experimentally analyzed. The results show that more than 50% of the nanofillers were retained within the reinforced kraft paper and translated to 1.5% by weight loading of the nanoparticles in the kraft paper. Scanning electron microscopy (SEM) images showed evenly distributed nanofillers, with some traces of agglomeration. The moisture absorption property of the kraft paper specimen modified with rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA improved by 74% compared to the control (reference) specimen. Water vapor transmission rate of the surface-modified nanoparticles reinforced kraft paper insulation decreased by 30%. Compared to the unfilled paper, the contact angle of water droplets on the surface of the reinforced kraft paper improved by 12%. Water absorption rate improved by being 4 times slower in paper specimens containing rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA. Dielectric dissipation factor measurement results showed that the specimen modified with rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA had 40% lower dielectric losses than the reference samples. This study, therefore, has successfully improved the hydrophobic properties of kraft paper by filling it with surface-modified rutile TiO2 using 5% ASA as the nanoparticle surfactant.

Thermal aging and ultraviolet (UV) irradiation are used as methods to prepare samples of kraft paper spanning its full lifecycle within a high-voltage transformer, from new to end of life. The mechanical break strain is correlated with the degree of polymerization (DP), representing a progressive deterioration of mechanical properties during aging. Subsequent exposure to oil flow mimicking both normal plant operation and reclamation activities shows that aged paper exposed to high rates of flow experiences noticeable surface roughening. Despite this, no measurable erosion occurs even after long-term temperature/flow cycling in used mineral oil containing a notable particulate content. This indicates that kraft paper is unlikely to be eroded by normal reclamation activities, even in a severely aged asset. This confirms the validity of using this approach to extend plant life. [ABSTRACT FROM AUTHOR]

This special section of the IEEE Open Journal of Circuits and Systems (OJCAS) aims to highlight a selection of papers from 2022 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). Due to COVID-19 and travel restrictions, APCCAS 2022 was organised as a hybrid conference during 11 - 13 November 2022 in Shenzhen China. As the regional flagship conference of IEEE Circuits and Systems Society, APCCAS 2022 was driven by the theme “Building a Fully-connected AIoT World” to emphasize the CAS Society’s potential for finding multidisciplinary solutions to societal and industrial challenges. The papers in this special issue were selected from a comprehensive list of papers presented in the sessions of APCCAS 2022.

Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines. We aim to automatically construct research highlights given certain segments of a research paper. We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings. We test our model on a benchmark dataset, CSPubSum, and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation. For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature. On the CSPubSum dataset, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78, 9.76 and 29.3, respectively, METEOR score of 24.00, and BERTScore F1 of 85.25.

The paper manufacturing process is a large-scale process in which the control performance is required to be optimal in the machine direction (MD) and cross direction (CD). This work focuses on CD control, which is more complicated than MD control. Uncertainties in the process arise from different sources, so there is a requirement for better modeling techniques. The control of paper profile properties, namely basis weight, moisture, and caliper, is done with the help of actuators that are distributed spatially. Basis weight is taken as the profile parameter in this work. The work aims to improve robust performance and simplify the control design problem. Here, $\mu $ synthesis methods are used for controller design, as this method provides guaranteed robust performance. To simplify the design problem two-dimensional modeling is explained explicitly. The conventional methods of $\mu $ analysis are extended to spatial coordinates. The performance of the $\mu $ synthesis designed method is compared with the open loop shaping method. Control performance is judged in terms of sensitivity, stability margin, $\sigma $ performance, and robust performance. The effectiveness of the developed $\mu $ synthesis designed method is demonstrated using the industrial example from the pulp and paper industry.

Recently reconfigurable intelligent surface (RIS) has attracted great attention because it can create a smart wireless environment. Hence it can enhance the capacity and coverage of the wireless network significantly. A thorough review of RISs has been presented in this paper focusing on the hardware aspect of the RIS. Beyond-5G/6G communication will have a smart propagation environment, where RIS can be used for such communications. RIS consists of various small unit cells. The unit cells should have some tunning mechanism to reflect or transmit the incoming waves in the desired direction. It is possible to tune the impedance of the unit cells using PIN diodes, varactor didoes, microelectromechanical (MEMS), thermal, and other ways. In this paper, the background of RIS has been discussed where RIS will play a significant role in beyond-5G/6G communications. We have also added the theoretical background of RIS and motivations to writing this paper. After that several published papers in the literature have been presented so that the readers can get an overall idea about the RIS and its hardware. Hence, this paper will be very useful for practitioner engineers and researchers. RISs have been presented in various tables and various parameters have been presented. We have discussed challenges and solutions for the hardware of the RIS design. We have also discussed potential research and research gap that can be explored in the future. Lastly, we have added a conclusion for this review paper. In our manuscript, we have added 154 references. There are various kinds of RIS available in the literature. We have added different types of RISs in this manuscript. The magnitude and phase of the reflection and transmission coefficients are the main parameters of any kind of RIS.

Personalized PageRank is a variant of PageRank, widely developed for citation recommendation. However, the personalized PageRank that works with a vast amount and rich scholarly data still results in information overload. Sometimes, junior scholars still need help to arrange queries quickly because of limited domain knowledge. Senior researchers need reference papers regarding a similar topic they intend to search for and related topics as a new insight. In this research, scientific citation recommendation aims to find the most influential papers with similar and related topics. Related topic papers in serendipitous perspectives are reference papers that are novel, diversified and unexpected to a user. The unexpectedness of recommended papers can be papers with different topics to queries but still relevant. To accomplish these challenges, we propose a framework of scientific citation recommendation with serendipitous perspectives. The framework includes feature extraction of an academic citation network, selection of multi-topic communities, and ranking papers in the selected multi-topic communities by modified PageRank. Papers in the chosen communities tend to link to similar and related papers. Modified PageRank is an extension of personalized PageRank, which works on multi-topic communities and manuscript queries. The experiments reveal that the proposed models outperform some models of personalized PageRank and some models of Content-Based Filtering. The multi-topic communities-based models work more effectively than the baselines if they run in a large dataset since the topic communities become more cohesive.

With the accelerating development of science and technology, the academic papers being published in various fields are increasing rapidly. Academic papers specially in science and technology fields are a crucial media for researchers who develop new technologies by identifying knowledge regarding the latest technological trends and conduct derivative studies in science and technology. Therefore, the continual collection of extensive academic papers, structuring of metadata, and construction of databases are significant tasks. However, research on automatic metadata extraction from Korean papers is not being actively conducted currently owing to insufficient Korean training data. We automatically constructed the largest labeled corpus in South Korea to date from 315,320 PDF papers belonging to 503 Korean academic journals and this labeled corpus can be used for training the models of automatic extraction for 12 metadata types from PDF papers. This labeled corpus is available at https://doi.org/10.23057/48. Moreover, we developed inspection process and guidelines for the automatically constructed data and performed a full inspection of the validation and testing data. The reliability of the inspected data was verified through the inter-annotator agreement measurement. Using our corpus, we trained and evaluated the BERT based transfer learning model to verify its reliability. Furthermore, we proposed new training methods that can improve the metadata extraction performance of Korean papers, and through these methods, we developed KorSciBERT-ME-J and KorSciBERT-ME-J+C models. The KorSciBERT-ME-J showed the highest performance with an F1 score of 99.36%, as well as robust performance in automatic metadata extraction from Korean academic papers in various formats.

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers selected from all papers published between October 1, 2020 and September 30, 2023 in OAJPE.

During the past two decades, a number of two-terminal switching devices have been demonstrated in the literature. They typically exhibit hysteric behavior in the current-to-voltage characteristics. These devices have often been also referred to as memristive devices. Their capacity to switch and exhibit electrical hysteresis has made them well-suited for applications such as data storage, in-memory computing, and in-sensor computing or in-memory sensing. The aim of this perspective paper is to is twofold. Firstly, it seeks to provide a comprehensive examination of the existing research findings in the field and engage in a critical discussion regarding the potential for the development of new non-Von-Neumann computing machines that can seamlessly integrate sensing and computing within memory units. Secondly, this paper aims to demonstrate the practical application of such an innovative approach in the realm of cancer medicine. Specifically, it explores the modern concept of employing multiple cancer markers simultaneously to enhance the efficiency of diagnostic processes in cancer medicine.

This paper presents the prediction of aging state of oil impregnated insulating Kraft paper using textural properties. Under conditions of prolonged thermal stress, the insulating capacity of Kraft paper decreases due to carbonization and degradation of cellulose. To analyze these effects, a new accelerated aging kit is developed for the experiments. Four groups of samples are collected, and the morphology is examined. Texture feature is extracted for the samples using normalization and Gray-Level Co-occurrence Matrix (GLCM). Supervised (Support Vector Machine (SVM)) and unsupervised (K-means) machine intelligence methods are trained to classify images based on the feature. In addition, new samples are checked for feasibility of the prediction of aging state. The calculated training accuracy of SVM is 92.67%, and for k-means is 95.0%, on the other hand, the testing accuracy of SVM is 91.0%, and for k-means is 92.6%. Therefore, this machine intelligence-based methodology using image features is effective in classifying the aging state of the Kraft Insulating Paper. [ABSTRACT FROM AUTHOR]