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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.

There is a relative lack of research on the development of creepage discharge in aged oil-paper insulation subjected to AC-DC combined voltage in converter transformers. In this study, based on the physical and chemical properties of oil-paper, we propose a sphere-plate electrode model to study the creepage discharge parameters and characteristic spectra of aged oil-paper subjected to AC-DC combined voltage. The results show that the creepage discharge inception voltage of aged oil-paper is related to the AC voltage component of the AC-DC combined voltage. However, when the DC voltage component is 60 kV, the flashover phenomenon was found to occur in one of the severely aged samples before the AC voltage was applied. Despite being subjected to the same DC voltage, the flashover phenomenon did not occur in three other samples aged to different degrees. Large-amplitude discharges were found to appear earlier in the severely aged samples. At the same creepage discharge stage, as the aging time of the oil-paper increased, the number of large-amplitude discharges was found to rapidly increase, and the time interval between adjacent discharges was found to gradually decrease. At each stage of the creepage discharge, the small-amplitude discharge has a “hump” shaped distribution. The larger the DC voltage component, the shorter the endurance time of aged oil-paper, and the more severe the damage to the oil-paper. The DC voltage component was found to have an accelerating effect on the development of creepage discharge in aged oil-paper insulation.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.

Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.