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Carbon fibre-reinforced ultra-high temperature ceramics (UHTCs) are considered a class of promising materials for several applications, the most appealing ones being in the aerospace sector. Reinforcement is necessary to overcome the brittleness and low thermal shock resistance of ceramics and is often provided through the addition of carbon fibres or other carbon-based phases, such as nanotubes, graphene, and graphite. The present work is focused on the toughening of UHTCs through incorporation of 30−50 vol% thin, ordered graphite layers from conventional filter paper followed by hot pressing sintering. Cellulose filter paper was selected because it undergoes thermolysis with no melting stage forming a strong carbonaceous residue that can be used as toughener. Microstructure and mechanical properties of toughened composites were compared to those of other materials reinforced with short carbon fibres and the effect of different distribution of graphite was studied. Addition of graphite allowed toughness to be increased from 3−4 MPa·m1/2 (for un-reinforced materials) to 5.1−5.5 MPa·m1/2, similar to results obtained with short fibre reinforcement. The high-temperature properties, such as strength and toughness as well as oxidation resistance at 1500 ℃, were also examined.

Paper pulp is a lignocellulosic fibrous material used in the industrial production of paper and board products. In addition to cellulose and hemicellulose, paper pulp contains 1 to 20% lignin, depending on the raw materials and pulping process used. Lignin is a heterogenous aromatic polymer that is hydrophobic and more resistant to microbial degradation compared to the easily biodegradable cellulose and hemicellulose. In this study, the biodegradation of paper pulps containing varying amounts of lignin was examined in soil and marine environments using ISO testing methods. Lignin significantly reduced the mineralization of paper pulps to CO2 in both environmental conditions, and a strong inverse correlation between lignin content and the mineralization to CO2 was observed. A similar impact was observed with natural materials containing lignin, such as birch sawdust. Since the calculation of biodegradability in most ISO and EN standards is based solely on the concept of mineralization to CO2, materials containing lignin can receive poor values in these tests. The implications of this for standardized requirements of biodegradability and possible options to overcome testing deficiencies are discussed.

Automatic test paper design is critical in education to reduce workloads for educators and facilitate an efficient teaching process. However, current designs fail to satisfy the realistic teaching requirements of educators, including the consideration of both test quality and efficiency. This is the main reason why teachers still manually construct tests in most teaching environments. In this paper, the quality of tests is quantitatively defined while considering multiple objectives, including a flexible coverage of knowledge points, cognitive levels, and question difficulty. Then, a model based on the technique of linear programming is delicately designed to explore the optimal results for this newly defined problem. However, this technique is not efficient enough, which cannot obtain results in polynomial time. With the consideration of both test quality and generation efficiency, this paper proposes a genetic algorithm (GA) based method, named dynamic programming guided genetic algorithm with adaptive selection (DPGA-AS). In this method, a dynamic programming method is proposed in the population initialization part to improve the efficiency of the genetic algorithm. An adaptive selection method for the GA is designed to avoid prematurely falling into the local optimal for better test quality. The question bank used in our experiments is assembled based on college-level calculus questions from well-known textbooks. The experimental results show that the proposed techniques can construct test papers with both high effectiveness and efficiency. The computation time of the test assembly problem is reduced from 3 hours to 2 seconds for a 5000-size question bank as compared to a linear programming model with similar test quality. The test quality of the proposed method is better than the other baselines.

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.

To improve the accuracy and efficiency of the aging life prediction and assessment of transformer oil-paper insulation, and to make up for the deficiencies of traditional characterizers, such as 2-furfural, carbon monoxide, and carbon dioxide, a method for the simultaneous determination of methanol, ethanol, $n-\mathbf{propanol}$, and $n$-butanol in oil with a single injection is established by headspace-gas chromatography-mass spectrometry. The measured results show that the determination limits of the four alcohol characterizers can be controlled to $\mathbf{10}\ \mu \mathbf{g}/\mathbf{kg}$ level. Based on this method, the change patterns of the above four alcohols with thermal aging time and degree of polymerization are obtained through thermal aging experimental research. Ethanol, $n$-propanol, and $n$-butanol in oil indicate nearly linear correlations with thermal aging time and degree of polymerization, similar to that of methanol. By analyzing 52 sets of measured data of 500 kV EHV transformers in operation, $n$-butanol is found to have excellent performance, and a new method to evaluate the aging state of oil-paper insulation employing $n$-butanol and methanol is proposed along with the aging attention value model. The measured data of 500 kV EHV transformers in operation indicate that the combination of $n$-butanol and methanol as the preferred characterizers can effectively compensate for the shortcoming of traditional characterizers in the early stages of aging, and the feasibility of the method is verified. Two possible pathways for the generation of $n$-butanol by cellulose cleavage during the aging of oil-paper are proposed from the chemical structure of cellulose.

The current study is related to fabrication of point-of-care molecularly imprinted paper-based analytical devices, specifically related to ovarian cancer early stage detection. The task involves creating a paper coated with silver nanoparticles (AgNPs) that can be used as a transducer via surface enhanced Raman spectroscopy (SERS) to detect the threshold level of the biomarker lysophosphatidic acid (LPA) as a template molecule to diagnose ovarian cancer. MIPs are synthetic receptors designed for a targeted molecule which are prepared from polymers in the presence of target such as small molecules, proteins or cells etc. Removal of the template results in cavities which are structurally and electrostatically complementary to the template molecule. The molecularly imprinted polymer (MIP) layer was anchored on vinyl trimethoxy silane modified-paper, which served as a detector by hydrolytically modifying the paper surface with vinyl trimethoxy silane. To detect LPA in serum samples for ovarian cancer, MIP-paper based on SERS was developed. Patients with benign ovarian tumors had an LPA level of 7.73 μM whereas those with ovarian cancer have a level of 16.99 μM. A healthy person's LPA level is 2.92 μM. Consequently, the rise in LPA levels may aid in the early detection of ovarian cancer. The study refers to the use of a point of care device to diagnose ovarian cancer early, which should be simple, effective, and cost-effective. The analytical tool in the study is a sensor with high selectivity and sensitivity, quick assay times, and inexpensive costs. For LPA in serum, the binding constant of modified paper-based MIP ranges from 11.00 μM to 0.08 μM. The amalgamation of nanoparticles on 2D-paper with MIP have lead to develop sensor which can effectively select LPA in untreated real samples like serum.

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.

Extant studies have focused on exploring the research output of universities in Nigeria, but the measure of the quality of research output using citations per paper (CPP) from the Web of Science (WoS) and Scopus is scarcely discussed. This paper investigated the quality of papers published by the top 76 universities in Nigeria. Five hypotheses on CPP were tested using Kruskal-Wallis and Mann-Whitney tests. These hypotheses tested whether CPP is the same or different i) across six geo-political regions (GPRs), ii) between the two major regions, iii) across three university funding types, iv) in Scopus and WoS, and v) if these two databases are correlated. Results using these two databases indicated that no significant median differences were detected among universities across the six GPRs, between northern and southern Nigeria, and between the three funding types (federal, state, and private). Moreover, no significant median differences in CPP between Scopus and WoS were detected, and CPP in both databases was highly positively correlated (0.879; p = 0.05). A comparison of the mean CPP of Nigeria (5.90) from Scopus with 57 other African countries ranked it 47th despite being ranked third in terms of volume of published articles. Policy prescriptions are made on the implications of using CPP for practice.

Poly(3-hydroxybutyrate) (PHB), a biodegradable polymer, has been studied as a substitute for petroleum-based polymers used for barrier coating to improve the barrier and mechanical properties of paper. In this study, ethyl cellulose (EC), a cellulose derivative, was used to prepare a polymer blend for enhancing the barrier properties of PHB. The prepared PHB/EC blend was then applied as a paper coating material. Additionally, the barrier and mechanical properties of the PHB/EC blend-coated paper based on the PHB/EC mixing ratio and coating weight were analyzed. The results showed that the EC could act as a binder for the PHB/EC blend-coated paper. Consequently, the PHB/EC blend-coated paper exhibited significant improvements in mechanical and barrier properties, including a substantial increase of over 100% in internal bond strength, more than 20% in tensile strength, exceeding 60% enhancement in water resistance, and a remarkable increase of over 90% in air permeability.

Herein, the challenge of accurately classifying the manufacturing origin of printing paper, including continent, country, and specific product, was addressed. One-dimensional convolutional neural network (1D CNN) models trained on infrared (IR) spectrum data acquired from printing paper samples were used for the task. The preprocessing of the IR spectra through a second-derivative transformation and the restriction of the spectral range to 1800 to 1200 cm-1 improved the classification performance of the model. The outcomes were highly promising. Models trained on second-derivative IR spectra in the 1800 to 1200-cm-1 range exhibited perfect classification for the manufacturing continent and country, with an impressive F1 score of 0.980 for product classification. Notably, the developed 1D CNN model outperformed traditional machine learning classifiers, such as support vector machines and feed-forward neural networks. In addition, the application of data point attribution enhanced the transparency of the decision-making process of the model, offering insights into the spectral patterns that affect classification. This study makes a considerable contribution to printing paper classification, with potential implications for accurate origin identification in various fields.