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Sensitizers are used to prepare photosensitive materials for ink-printing and thermal-recording media. Typical sensitizers contain an aromatic ring and exhibit aryl hydrocarbon receptor-mediated activity. They can accumulate in aquatic biota, leading to the disruption of ecosystems. Effluent from paper recycling plants has been identified as a source of contamination, and the presence of sensitizers in toilet paper may represent a new source of contamination in aquatic environments. To examine possible sources of contamination by sensitizers, this study investigated the levels of sensitizers in recycled toilet paper. We also studied the prevalence of sensitizers in rivers receiving effluents from sewage treatment plants in Japan, from 2020 to 2022. We detected eight sensitizers and related compounds in both toilet paper and river water samples; their total concentrations ranged from 0.78 to 34 µg/g (mean: 12 µg/g) in toilet paper and 58–1167 µg/L (median: 77 µg/L) in water. In the water samples, sensitizers were partitioned between liquid and suspended solid phases, and in suspended solids, sensitizers accounted for an average of 42% of the total. Correlation analysis showed a high correlation between sensitizer composition in the water from a river receiving sewage treatment plant effluents and toilet paper. Since these compounds were also detected in the river water analyzed in this study, toilet paper should be considered a novel source of sensitizers contamination in sewage treatment systems. [ABSTRACT FROM AUTHOR]

Traditional papermaking was a significant innovation that intrigued the information-sharing society in a wide range of the globe. The importance of finding the historical background hidden behind the historical traditional paper is critical to understanding societies in history. Advanced molecular biological research is applied to many forensic studies to identify the origin and criminal events. Yet, biological research refrained from the heritage science of traditional papers due to its damage to the samples. The current study explores the Pearson correlation between DNA in traditional paper and non-destructive measurement of characteristics of traditional paper. DNA concentration and purity were found to have a proportional relationship based on the CIEL*a*b* color system. The color of the traditional paper was assessed assessment shows green and yellow rather than red and blue. The DNA concentration demonstrated a positive linear correlation with the peaks of 992 cm−1, 1599 cm−1, and 1628 to 1638 cm−1 region, which were lignin-specific functional groups. The presence of lignin in traditional paper defines the colors green and yellow, which is related to the presence of a cell wall. The quantity of cell walls present is linked to DNA survival during the traditional papermaking process. Furthermore, the partial least square (PLS) regression model is established. The prediction model based on DNA, paper properties, and smoothing + the second derivative preprocessed attenuated total reflection-Fourier-transform infrared (ATR-FTIR) spectral data demonstrated valid purity predictions. The evaluation parameter of the cross-validated prediction model contains a low Root Mean Square Error of Cross-Validation (RMSECV) of 0.091 for DNA purity. The determination coefficient was close to 1. However, the further establishment of a validated DNA extraction methodology is required for the consistency of the prediction model for DNA concentration. The hierarchical clustering analysis demonstrated the agglomerative dendrogram on traditional paper samples. For the dendrogram, the Nasu paper mulberry (originating in northern Japan) was distinguished from another paper mulberry (originating in southwestern Japan) by clustering. By multivariate statistical analysis, the DNA presence and quality were predictable from the Japanese traditional papers. In addition, the clustering analysis provided the possible origin of paper mulberry materials for traditional papers. Based on the preliminary information, the need for DNA analysis on historical traditional paper can be determined. [ABSTRACT FROM AUTHOR]

The XY model of oil‐paper insulation connects the dielectric properties of oil, paper and oil‐paper system. Its equivalent circuit is the basis of the application of dielectric response technology in the state diagnosis of transformer oil‐paper insulation. To better apply the XY model to the study of the dielectric properties of oil‐paper insulation, the dielectric properties of the coaxial oil‐paper insulation system built in the laboratory, XY model and its equivalent circuits are compared. Then an improved circuit of XY model which can reflect the properties of the oil‐paper insulation more truly is proposed. The results show that there is no difference between the XY model and the coaxial model, and both can well characterize the oil‐paper insulation system. The two equivalent circuits of XY model, "Series to Parallel" circuit and "Parallel to Series" circuit, ignore the current tangential component and the electric field distortion in oil‐paper insulation, respectively, resulting in deviations from the XY model. By introducing a new branch C*XY, the improved circuit can reflect the tangential current and electric field distortion in oil‐paper insulation, which can be completely equivalent to XY model. In addition, there is a characteristic frequency point in the oil‐paper insulation system, where the dielectric loss factor tan δ of oil, paper and oil‐paper system are the same. There is no difference between the XY model and its two equivalent circuits at this point. The characteristic frequency is determined by the dielectric properties of oil and paper, and not affected by the insulation structure. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background: Health-related patient-reported outcomes (HR-PROs) are crucial for assessing the quality of life among individuals experiencing low back pain. However, manual data entry from paper forms, while convenient for patients, imposes a considerable tallying burden on collectors. In this study, we developed a deep learning (DL) model capable of automatically reading these paper forms., Methods: We employed the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire, a globally recognized assessment tool for low back pain. The questionnaire comprised 25 low back pain-related multiple-choice questions and three pain-related visual analog scales (VASs). We collected 1305 forms from an academic medical center as the training set, and 483 forms from a community medical center as the test set. The performance of our DL model for multiple-choice questions was evaluated using accuracy as a categorical classification task. The performance for VASs was evaluated using the correlation coefficient and absolute error as regression tasks., Result: In external validation, the mean accuracy of the categorical questions was 0.997. When outputs for categorical questions with low probability (threshold: 0.9996) were excluded, the accuracy reached 1.000 for the remaining 65 % of questions. Regarding the VASs, the average of the correlation coefficients was 0.989, with the mean absolute error being 0.25., Conclusion: Our DL model demonstrated remarkable accuracy and correlation coefficients when automatic reading paper-based HR-PROs during external validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)