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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]

Due to the compact and complex internal structure of 110 kV three‐phase enclosed GIS disconnector, the voltage distribution between the contact breaks of each phase has an influence on the insulation performance of the GIS disconnector, and its internal insulation design is a difficult point to be solved. To solve this problem, this paper firstly establishes a three‐dimensional model of three‐phase enclosed GIS disconnecting switch and conducts a research on the distribution of electric field under a specific working condition, it is found that the outer corner of the static contact, the inner corner of the static arc contact, the inner corner of the outer end of the moving contact and the inner corner of the moving contact are the internal electric field concentration of the three‐phase enclosed GIS disconnector. Then, in order to further reduce the electric field strength and improve the insulation margin and performance, this paper proposes a method based on the combination of Extreme Learning Machine (ELM) and Multi‐objective Grasshopper Optimization Algorithm (MOGOA) to optimize the structural design of three‐phase enclosed GIS disconnector, and a ELM model is established, which takes the internal fillet radius of stationary arcing contact, the internal fillet radius of moving contact, the internal fillet radius of external end of dynamic and static contact, the radius of the outer corner of the two static side contacts and the opening distance of the grounding contact as the inputs, and takes the maximum electric field strength of the two static side contacts and the moving side contacts as outputs; Finally, the structural parameters are optimized by MOGOA algorithm. Compared to the pre‐optimization period, the electric field strengths of the bus bar static‐side contact, dynamic contact and static side contact of the B‐phase after optimization decreased from 20.2, 9.28 and 17.5 to 11.4, 6.65 and 11.5 kV/mm, respectively. The electric field strengths of the dynamic and static side contacts are significantly reduced, which is beneficial for improving the insulation performance of the three‐phase enclosed GIS disconnector. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [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]

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple data holders to collaborate on building machine learning models while preserving the privacy of their data. FL can be categorized as horizontal or vertical, depending on the distribution characteristics of the data. Specifically, horizontal FL uses data partitioned in the sample space, whereas vertical FL uses data partitioned in the feature space. Traditional vertical FL methods aim to facilitate collaboration among clients to infer a single global target. However, these methods may be impractical because each client often has a unique target to be inferred. In this paper, we propose a novel vertical FL method, called personalized vertical federated learning (pvFed), which addresses this limitation by allowing each client to perform inferences specific to their individual task. To the best of our knowledge, no existing method currently addresses this limitation. The objective of pvFed is to construct a global model that generates a representation vector to support client inference. The global model, constructed using distillation and dimensionality reduction, takes a sample ID common to all clients as input and outputs a sample‐specific representation vector. Clients utilize the intermediate representation of their own model and the representation vectors output by the global model for inference. Because these vectors are not dependent on client‐specific tasks, clients can repurpose them for any additional tasks. Our experiments, conducted on two distinct data types—image and tabular data sets, under a vertical partitioning where each client had its own specific task, demonstrated the efficacy of vectors generated by the global model in pvFed for client inference. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Object tracking tasks often face problems such as illumination and appearance changes due to various scenarios. To effectively solve such problems, single object tracking algorithms in recent years have improved the stability of the tracker by introducing online learning, reducing tracking drift, and tracking objects stably even with large deformations. However, on the one hand, employing online learning increases computation costs, causing the tracker to run slower. On the other hand, because it learns from the tracking results, the tracking template will only be correctly updated if the object is accurately tracked. Any incorrect update will result in tracking failure. In this paper, we propose a filtering algorithm for online learning sample processing in object tracking to remedy these issues. Our method can select valid training samples and discard unnecessary training samples in object tracking. Numerous experiments demonstrate that our algorithm improves the tracking accuracy, reduces the computation complexity, and improves the running speed and run‐time. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The shear wave velocity is among the key parameters that are responsible for damage caused during an earthquake. Determining shear wave velocity is a costly and time-consuming direct geophysical method. In the present paper, a machine learning model has been developed to obtain the subsurface shear wave velocity profile without using direct methods. The bore log information and the subsurface shear wave velocity profile available at various stations of Japan's Kyoshin network (K-NET) have been utilized for training this machine learning model. The parametric correlation study indicates that simple parameters like rock/soil type, the thickness of the layer in the model, and standard penetration test (SPT-N) value directly correlate with the medium's shear wave velocity. A stacked ensemble machine learning model named VelProfES (an acronym for Velocity Profiler using Ensemble machine learning models) has been developed in this paper and has been utilized for effective prediction of the shear wave velocity profile using basic information from borelog data. The dataset used in the training and testing of the machine learning model consists of borelog and shear wave velocity information from 1101 stations. Of 1101 stations, 657, 283, and 71 stations have been utilized for training, testing, and validating the machine learning model. Training, testing, and validation of the developed machine learning model consist of parameters from 12351, 5279, and 1388 velocity layers. The problem of data imbalance based on soil type has been addressed using an additional 10510 layers of synthetic borelog data generated from conditional generative adversarial networks (CTGANs). A feature and model ablation study was conducted for the VelProfES model to provide substantiation for the model and feature selection choices. The predicted shear wave velocity profiles were compared at specific stations, focusing on average velocities at 5, 10, 15, and 20 depths. Further, the predicted values have been compared with the empirical relation of Sil and Haloi (2017) and a trained polynomial model. The machine learning model demonstrates close alignment between predicted and actual values across a broad spectrum of velocities, a contrast not observed in the empirical relation and polynomial model. The results show that the machine learning models and augmented data generated using CTGANs can efficiently minimize the error between actual and predicted subsurface shear wave velocity values. • Describe detailed machine learning strategy for predicting shear wave velocity. • Generative Adversarial Network used to handle unbalanced data problem. • Describes correlation of shear wave velocity with Rock/soil type, layer, and SPT-N. • Comparison of described model with recently developed methods for all regions. • Performance of the machine learning model is predicted. [ABSTRACT FROM AUTHOR]

Ground motion prediction is an important and complex research subject in earthquake engineering and traditional approaches based on statistical regression have much room for improvement in prediction accuracy. Utilizing the 67,164 ground motion records from KiK-net and K-Net for 777 shallow crustal earthquakes between 1997 and 2019 in Japan, this paper proposes a ground motion prediction model XGBoost-SC based on the machine learning algorithm of eXtreme Gradient Boosting (XGBoost) for Japan. Magnitude, focal depth, hypo-central distance, V s30 , site altitude, and focal mechanism were used as the feature parameters, and XGBoost, Random Forest, and Deep Neural Networks (DNN) algorithms were selected for model training while Bayesian optimization was used to search for optimized hyperparameters to improve the prediction accuracy. XGBoost algorithm was selected for further study based on the comparison of results from the three algorithms. Residual change with magnitude and hypo-central distance, the probability distribution of residuals, residual standard deviation (σ), residual mean squared error (MSE), and Pearson correlation coefficient (R) were used as the evaluation parameters, and a comparison study was performed against the ground motion prediction equation based on traditional approaches. Actual earthquake events were selected to compare the prediction results against the observation records. To further validate and explain the proposed model, SHapley Additive exPlanations (SHAP) analysis was performed to explain the impact of selected feature parameters on the proposed model. The results demonstrate that the proposed XGBoost-SC model has good prediction stability for all periods, and its residual errors are smaller than those of other models. Therefore, the proposed model can better reflect the ground motion attenuation for shallow crustal earthquakes in Japan and can serve as a better model for ground motion prediction in future aseismic design and earthquake disaster mitigation efforts. • A Ground Motion Prediction Model was proposed for shallow crustal earthquakes. • The proposed model was more precise than traditional approaches. • Explained the effect of feature parameters on the prediction results. [ABSTRACT FROM AUTHOR]