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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]

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquakes occurring in the Eurasian plate of Japan Islands from 1996 to 2021. Feature engineering has given 29 new features identified from the early P wave phase of the record, which show a high correlation with the magnitude of an earthquake. The comparison of predicted and actual magnitude shows that a trained XGboost model, which uses a single input record for magnitude prediction, gives an average prediction error of 0.004 ± 0.57 for earthquakes in the test dataset. In contrast, the average prediction error of –1.1 ± 0.80 and –0.65 ± 0.69 has been obtained for the magnitude estimated from conventional τc and Pd methods using the same test dataset. It is further seen that the average predicted magnitude of a single earthquake of magnitude 4.5 and 6.1 (MJMA) obtained by using multiple nearfield records using XGBoost model is 4.58 ± 0.33 and 6.32 ± 0.29, which is close to the actual magnitude of the earthquake. The results presented in this paper clearly show that the structured data can be effectively used by complex machine learning or deep learning models to predict earthquake magnitude from single or multiple records. [ABSTRACT FROM AUTHOR]

This work is the extended version of a paper presented at the conference HMM2021, about the history of mechanical engineering. First, the initial cultural and industrial steps in the robotic field in Japan are introduced, to display the beginning of this interlaced path, before WW2; then, in the context of the aftermaths of the war, some famous anime heroes are presented as ancestors of the coming mecha anime series. The rising research in the field of robotics and more generally the developments of contemporary Japanese automation industry are then drawn, as a technological substrate for mecha conception. In particular, Masahiro Mori studies are taken into consideration to identify an actual robot design pattern to apply to the following comparative description and examination of the different mecha; finally, thanks to this analysis, the common breeding ground whence robotics and mecha (and their reciprocal influences) arise, is outlined. [ABSTRACT FROM AUTHOR]

Japan is one of the countries with the most frequent natural disasters in the world and is faced with various threats of natural disasters every year, which significantly impact Japan's social economy and people's lives. A great deal of information about disasters is preserved in Japanese literature. Interpreting and organizing this information help us to analyze the regularity of disasters and understand the preventive measures of ancient people. This paper aims to organize, analyze and save disaster data by collecting various information about disasters. Then a disaster spatiotemporal database system is constructed by using deep learning, image processing, and database technology. The system consists of two parts, namely, the disaster database and disaster website. The disaster database is the core of the whole system, which saves the disaster data after organizing and summarizing. The database collects disaster information from various sources, including key information such as disaster type, time, location, scale, and scope of impact. The Disaster website is the system's user interface, providing an interactive platform for users to access and use disaster data easily. The website has many functions, including search, visual display, disaster information query, etc. We also make a detailed analysis of the collected data, aiming to predict the causes and occurrence rules of disasters so as to achieve the target of disaster prediction. [ABSTRACT FROM AUTHOR]

Electrodynamic coupling between the ionospheric E and F regions is widely recognized as the underlying mechanism for generating medium-scale traveling ionospheric disturbances (MSTIDs) during nighttime at midlatitudes. Recently, the double-thin-shell approach has proven to be a useful tool for studying the E–F coupling. By using total electron content (TEC) measurements, this approach enables the simultaneous reconstruction of electron density perturbations in both the E and F regions with broad and continuous coverage. However, the current reconstruction performance is limited when using only GPS-TEC measurements from GEONET, a dense network of ground-based Global Navigation Satellite System (GNSS) receivers over Japan. The expansion of available data sources and the integration of multi-GNSS observation data are considered important to enhance the double-thin-shell model. Fortunately, SoftBank Corp., a Japanese telecommunications provider, has recently developed a dense independent GNSS observation network to improve positioning services. In this paper, we analyze the potential of the improved double-thin-shell approach and emphasize the importance of incorporating multi-GNSS observation data from both GEONET and SoftBank networks. The solvability analysis, simulation, and observation results collectively indicate a substantial improvement in the spatiotemporal resolution. Specifically, the longitudinal and latitudinal resolution is improved from 0.15° to 0.1° in the E region, and from 0.5° to 0.3° in the F region. The temporal resolution is also improved from 2 to 1 min. In addition, significant improvements have been achieved in the reconstruction performance, particularly for the E region under complex background conditions. Based on these assessments, we conclude that the incorporation of GEONET and SoftBank GNSS observation data holds significant potential for improving the double-thin-shell model and advancing our understanding of MSTIDs. [ABSTRACT FROM AUTHOR]

A cross-region prediction model named SeisEML (an acronym for Seismological Ensemble Machine Learning) has been developed in this paper to predict the peak ground acceleration (PGA) at a given site during an earthquake. The SeisEML model consists of hybridized models, kernel-based algorithms, tree regression algorithms, and regression algorithms. The model ablation study is conducted to examine the performance and the selection of meta-machine learning models in the SeisEML. The training and testing dataset consists of 20852 and 6256 accelerograms recorded by the Kyoshin Network, Japan. The mean absolute error (MAE) and root mean square error (RMSE) have been utilized to compare the predicted peak ground acceleration (PGA) for the test data. The SeisEML model yields approximately half the MAE and RMSE values obtained with conventional attenuation relations. The SeisEML model has been used to compute Japan's iso acceleration contour map of three earthquakes ( M JMA 7.4, 6.6, and 6.1). The qualitative comparison of iso acceleration contours obtained from actual and predicted PGA using SeisEML clearly shows that the model can reliably predict the PGA distribution during an earthquake compared to the regional ground motion prediction equation (GMPE). The cross-region prediction was performed on the datasets of the Iranian earthquakes using SeisEML. The comparison of predicted and observed peak ground acceleration in terms of MAE and RMSE shows that the machine learning model's performance is superior to the regional attenuation relation. The predictions of PGA from the developed ML model indicate that this trained model has the potential for predicting regional and global scenarios with similar tectonic setups. The ML model developed in this paper can considerably enhance the reliability of PGA prediction for seismic hazard mapping of any region and can serve as a reliable substitute for GMPEs. [ABSTRACT FROM AUTHOR]

Partial periodic pattern (3P) mining is a vital data mining technique that aims to discover all interesting patterns that have exhibited partial periodic behavior in temporal databases. Previous studies have primarily focused on identifying 3Ps only in row temporal databases. One can not ignore the existence of 3Ps in columnar temporal databases as many real-world applications, such as Facebook and Adobe, employ them to store their big data. This paper proposes an efficient single database scan algorithm, Partial Periodic Pattern-Equivalence Class Transformation (3P-ECLAT), to identify all 3Ps in a columnar temporal database. The proposed algorithm compresses the given database into a novel list-based data structure and mines it recursively to find all 3Ps. The 3P-ECLAT leverages the "downward closure property" and "depth-first search technique" to reduce the search space and the computational cost. Extensive experiments have been conducted on synthetic and real-world databases to demonstrate the efficiency of the 3P-ECLAT algorithm. The memory and runtime results show that 3P-ECLAT outperforms its competitor considerably. Furthermore, 3P-ECLAT is highly scalable and is superior to the previous approach in handling large databases. Finally, to demonstrate the practical utility of our algorithm, we provide two real-world case studies, one on analyzing traffic congestion during disasters and another on identifying the highly polluted areas in Japan. [ABSTRACT FROM AUTHOR]

Background: Bibliometric analysis is a mature method for quantitative evaluation of academic productivity. In view of the rapid development of research in the field of systemic lupus erythematosus (SLE) in the past decade, we used bibliometric methods to comprehensively analyze the literature in the field of SLE from 2013 to 2022. Methods: The relevant literature in the field of SLE from 2013 to 2022 was screened in the Web of Science Core Collection database. After obtaining and sorting out the data, CiteSpace and VOSviewer software were used to visualize the relevant data, and SPSS software was used for scientific statistics. Results: A total of 18,450 publications were included in this study. The number of articles published over the past 10 years has generally shown an upward trend, while Altmetric attention scores have also shown a clear upward trend in general and in most countries. Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers. The USA, China, Japan, Italy, and the UK are the most productive countries, but China and Japan are significantly inferior to other countries in terms of research influence. Four of the top ten authors are at the center of the collaboration network. LUPUS is the most contributing journal. The theme of systemic lupus erythematosus research mainly focuses on the pathogenesis, treatment, and management of SLE, and the emerging trend is related research on machine learning and immune cells. Conclusion: This study shows the research status of SLE, clarifies the main contributors in this field, discusses and analyzes the research hotspots and trends in this field, and provides reference for further research in this field to promote the development of SLE research. Key Points • Through bibliometric analysis, Altmetric analysis, and visual analysis, we reveal the global productivity characteristics of SLE-related papers in the past 10 years. • The number of global SLE-related studies has shown a significant increase, indicating that SLE is still a hot topic and deserves further study. • Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers, and the attention of related literature among non-professional researchers is increasing. • The theme of SLE research mainly focuses on the pathogenesis, treatment, and management of SLE. The emerging trend is machine learning and immune cells, which may provide new strategies for the diagnosis and treatment of SLE in the future. [ABSTRACT FROM AUTHOR]

To explore the current status of anesthesia research activity in Japan, we analyzed the number of abstracts presented at the Japanese Society of Anesthesiologists (JSA) annual meetings by several factors including gender, society branches, and subspecialty categories. The number of abstracts at JSA annual meetings has declined sharply since 2016 with no gender gap. A decrease in the neurological field predated the overall decline, but other subspecialty categories showed a similar decline. Although the Tokyo, Tokai-Hokuriku, and Kyushu branches were responsible for more than half of the reduction, the trend was similar among all branches. In a survey regarding academic activities of university hospital residents and faculty, Ph.D. aspirants' rate was only 20–30%. Residents had never presented an abstract at scientific conferences and never published any papers at nearly 40% and 30% of the university hospitals, respectively. Our survey suggests that junior anesthetists are losing interest in research. Senior faculty and mentors must redouble efforts to embed and encourage research in departments and by anesthetists in training. If a revival of anesthesia research in Japan does not occur then a service only specialty awaits. [ABSTRACT FROM AUTHOR]

Some global error bounds with undetermined parameters, which are not always valid, for the extended vertical linear complementarity problems (LCP) of CKV-type matrices and CKV-type B -matrices, are presented by Yan and Wang (Jpn. J. Ind. Appl. Math. 41:129–150, 2024). In this paper, new global error bounds for the extended vertical LCP of CKV-type matrices and CKV-type B -matrices are given, which depend only on the entries of the involved matrices. Numerical examples show that the new bounds are better than those provided in Zhang et al. (Comput. Optim. Appl. 42(3):335–352, 2009) and Wang et al. (Comput. Appl. Math. 40:148, 2021) in some cases. [ABSTRACT FROM AUTHOR]