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This study investigated the relationship between the cosmic ray intensity (CRI) and the horizontal component of Earth's magnetic field (dH) during two intense geomagnetic storms that occurred on September 8, 2017, and August 26, 2018, and a moderate storm on February 18, 2020 over three stations: IRKT, YKTK, and HRMS. The findings of this study indicate that the CRI and dH do not exhibit a simple association over each station. A significant decrease in CRI and dH was seen during the intense geomagnetic storm on September 8, 2017, over all stations as compared to the other two storm events. The decrease was more pronounced over the YKTK station, which has low cutoff rigidity (1.65 GV), than the HRMS (4.58 GV) and IRKT (3.64 GV) stations with high cutoff rigidity. Furthermore, the cross-wavelet analysis reveals that the relationship between CRI and dH varies with the strength of the storm, the location, and the cutoff rigidity of the station. [ABSTRACT FROM AUTHOR]

This research aims to comprehensively examine the clearness index (KT), total ozone column (TOC), and ultraviolet A (UVA) and ultraviolet B radiation (UVB) over Lumbini, Nepal (27°28' N, 83°16' E, and 150 m above sea level) throughout the 11 years of solar cycle 24 (2008 to 2018). The Lumbini, a highly polluted region, is important in advancing the identification and analysis of TOC variations across regions with similar geographical and climatic attributes. Data from the Ozone Monitoring Instrument (OMI) of the EOS-AURA satellite of NASA were used to analyze the daily, monthly, seasonal, and annual trends in the clearness index (KT), ultraviolet A (UVA), ultraviolet B (UVB), and TOC from the Comprehensive Environmental Data Archive (CEDA). The study found that the yearly averages for KT, TOC, UVA, and UVB were 0.55 ± 0.13, 272 ± 14 DU, 12.61 ± 3.50 W/m2, and 0.32 ± 0.11 W/m2, respectively. These values provide insights into the long-term variations in atmospheric parameters at Lumbini. The study also applied the continuous wavelet transform (CWT) to analyze KT, TOC, UVA, and UVB temporal variations. The power density peak of 35,000 DU2 in the TOC was observed from the end of 2010 to the end of 2011, within 8.5 years, underscoring the significance of analyzing TOC dynamics over extended durations to understand atmospheric behavior comprehensively. [ABSTRACT FROM AUTHOR]

In the area of recommendation systems, one of the fundamental tasks is rating prediction. Most existing neural network methods independently extract user's and item's review features utilizing a parallel convolutional neural network(CNN) and use them as the representation of users and items to predict rating scores. There are two main drawbacks of these methods: (1) They typically only leverage user or item reviews but ignore the latent information provided by user-item interactions. (2) The historical rating scores are not integrated into the representation of users and items, they are simply used as labels to train models. Thus the rating information is not adequately utilized, leading to the prediction performance of these methods is not superior. To remedy these drawbacks mentioned above, in this paper, we build a unified graph convolutional network(GCN) to capture the interaction information between user and item, also obtain additional information provided by reviews and rating scores. As both reviews and ratings carry interactive messages among users and items, they would magnify the learning performance of user-item features. Specifically, we first construct a multi-attributed bipartite graph(MA-bipartite graph) to represent users, items, and their interactions through reviews and ratings. Then we divide the MA-bipartite graph into two sub-graphs according to the attributes of the edge types which represent the user-item interaction in review domain and item domain respectively. Next, an attributed GCN model is explicitly designed to learn latent features of users and items by incorporating review embeddings and rating score weights. Finally, the attention mechanism is carried to fuse user and item features dynamically to conduct the rating prediction. We conduct our experiments on two real-world datasets. The results demonstrate that the proposed model achieved the state-of-the-art performance, which increases the prediction accuracy by more than 3%, compared with baseline methods. [ABSTRACT FROM AUTHOR]