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Raster data represent one of the fundamental data formats utilized in GIS. As the technology used to observe the Earth continues to evolve, the spatial and temporal resolution of raster data is becoming increasingly refined, while the data scale is expanding. One of the key issues in the development of GIS technology is to determine how to make large-scale raster data better to provide computation, visualization, and analysis services in the Internet environment. This paper proposes a decentralized COG-pyramid-based map service method (DCPMS). In comparison to traditional raster data online service technology, such as GIS servers and static tiles, DCPMS employs virtual mapping to reduce data storage costs and combines tile technology with a cloud-native storage scheme to enhance the concurrency of supportable requests. Furthermore, the band calculation process is shifted to the client, thereby effectively resolving the issue of efficient customized band calculation and data rendering in the context of a large-scale raster data online service. The results indicate DCPMS delivers commendable performance. Its decentralized architecture significantly enhances performance in high concurrency scenarios. With a thousand concurrent requests, the response time of DCPMS is reduced by 74% compared to the GIS server. Moreover, this service exhibits considerable strengths in data preprocessing and storage, suggesting a novel pathway for future technical improvement of large-scale raster data map services. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model's generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson's Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale. [ABSTRACT FROM AUTHOR]

Continental alkaline magmatism has been suggested to play a significant role in releasing deep mantle carbon into the atmosphere, which can greatly impact the global climate. However, the temporal variations of alkaline magmatism and their potential to modulate climate over geologic time remain poorly constrained. The detrital zircon record is a frequently used proxy for tracking secular variations in particular magmatism. Here, we use a novel machine‐learning technique to discriminate zircon from carbonatites, kimberlites, and other alkaline rocks. A global compilation of detrital zircon yields continental alkaline magmatic flare‐ups between 1,050−850, 650−500, 250−200, and 50−0 Ma. Our estimates indicate relatively elevated contributions of total magmatic carbon outgassing from alkaline magmatism during the aforementioned magmatic flare‐ups. We infer that anomalous alkaline magmatism may influence global warming during specific intervals of geologic time, but when they are not that voluminous or persistent extensive arc magmatism may drive warming conditions. Plain Language Summary: The potential of volcanic CO2 emissions to modulate atmospheric CO2 levels and affect the environment of our planet has been recognized. Some specific volcanic types, such as continental arc volcanism, have been hypothesized to play a dominant role in driving long‐term climate change. Recently, the efficiency of continental alkaline magmatism in releasing carbon from the deep mantle to the atmosphere and its ability to influence Earth's climate is proposed for certain timescales and supported by empirical data. However, the quantitative estimation of the alkaline magmatic activity over geologic time and its general link to the global climate change remains poorly constrained. Here, we assess the alkaline magmatic variations based on the detrital zircon record and a novel machine‐learning model which could discriminate zircon from carbonatites, kimberlites and other related alkaline silicate rocks. The predictive result shows several peaks at 1,050−850, 650−500, 250−200, and 50−0 Ma, which is considered a minimum estimation due to the preservation bias of the detrital zircon record. Our estimates indicate that continental alkaline magmatism may influence global warming during specific intervals of geologic time such as the early Paleozoic and early Mesozoic. Key Points: Frequency of magmatic events can be assessed by a machine‐learning analysis of zirconContinental alkaline magmatism peaked between 1,050−850, 650−500, 250−200, and 50−0 MaAnomalous alkaline magmatism may contribute to global warming at certain times [ABSTRACT FROM AUTHOR]

Carbonates or carbon-bearing materials may release gases under high pressure and high temperature (HP–HT) conditions. Characterizing the species and quantifying the volumes of these carbonaceous gases are critical for understanding carbon chemistry. However, the volatile nature of carbonaceous gas poses technical challenges in their collection, speciation, and quantification during HP–HT experiments. To address these challenges, we have developed a system that integrates sample collection, gas transportation, chemical conversion, and measurement of carbonaceous gases trapped within the large volume press capsules. The system comprises a capsule-crushing device for thorough sample pulverization, a mechanizer coupled with a flame ionization detector, a gas-sealing and transport interface, and gas chromatography for detection. To evaluate the system's capabilities, we quantified the gas volumes released from encapsulated kerogen quenched from 1.9 GPa to 873, 973, and 1073 K. The collected gas chromatography signals were compared to those obtained from standard mixed-gases. The volumes of CO2, CH4, and C2H6 in the samples were successfully derived from the signal peak area through calibration. The relative standard deviation value of two runs at 3 GPa and 1073 K is 1.956%, suggesting good reproducibility. Our system thus provides a robust solution for investigating carbon chemistry under HP–HT conditions. [ABSTRACT FROM AUTHOR]

Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, the existing methods often fail to adequately consider the spatial heterogeneity and complex nonlinearity between precipitation and surface parameters, resulting in poor downscaling performance and inaccurate expression of regional details. In this study, we propose a precipitation downscaling model based on geographically neural network weighted regression (GNNWR), which integrates normalized difference vegetation index, digital elevation model, land surface temperature, and slope data to address spatial heterogeneity and complex nonlinearity. We explored the spatiotemporal trends of precipitation in the Sichuan region over the past two decades. The results show that the GNNWR model outperforms common methods in downscaling precipitation for the four distinct seasons, achieving a maximum R2 of 0.972 and a minimum RMSE of 3.551 mm. Overall, precipitation in Sichuan Province exhibits a significant increasing trend from 2001 to 2019, with a spatial distribution pattern of low in the northwest and high in the southeast. The GNNWR downscaled results exhibit the strongest correlation with observed data and provide a more accurate representation of precipitation spatial patterns. Our findings suggest that GNNWR is a practical method for precipitation downscaling considering its high accuracy and model performance. [ABSTRACT FROM AUTHOR]

Spatiotemporal regression is a crucial method in geography for discerning spatiotemporal non-stationarity in geographical relationships, which has found widespread application across diverse research domains. This study implements two innovative spatiotemporal intelligent regression models, namely geographically neural network weighted regression (GNNWR) and geographically and temporally neural network weighted regression (GTNNWR), integrating the spatiotemporal weighted framework and neural networks. Demonstrating superior accuracy and generalization capabilities in large-scale data environments compared to traditional methods, these models have emerged as prominent tools. To facilitate the seamless application of GNNWR and GTNNWR in addressing spatiotemporal non-stationary processes, a Python-based package, GNNWR, has been developed. This article details the implementation of these models and introduces the GNNWR package, enabling users to efficiently apply these cutting-edge techniques. Validation of the package is conducted through two case studies. The first case involves the verification of GNNWR using air quality data from China, while the second employs offshore dissolved silicate concentration data from Zhejiang Province to validate GTNNWR. The results of the case studies underscore the effectiveness of the GNNWR package, yielding outcomes of notable accuracy. This contribution anticipates a significant role for the developed package in supporting future research that leverages big data and spatiotemporal regression techniques. [ABSTRACT FROM AUTHOR]

BACKGROUND: Endothelial dysfunction, the earliest stage of atherosclerosis, can be caused by smoking, but its molecular mechanism requires further investigation. OBJECTIVE: This study aimed to use bioinformatics analysis to identify potential mechanisms involved in smoking-related atherosclerotic endothelial dysfunction. METHODS: The transcriptome data used for this bioinformatics analysis were obtained from the Gene Expression Omnibus (GEO) database. The GSE137578 and GSE141136 datasets were used to identify common differentially expressed genes (co-DEGs) in endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) and tobacco. The co-DEGs were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) databases. Additionally, a protein-protein interaction (PPI) network was constructed to visualize their interactions and screen for hub genes. GSE120521 dataset was used to verify the expression of hub genes in unstable plaques. The miRNA expression profile GSE137580 and online databases (starBase 2.0, TargetScan 8.0 and DGIdb v4.2.0) were used to predict the related non-coding RNAs and drugs. RESULTS: A total of 232 co-DEGs were identified, including 113 up-regulated genes and 119 down-regulated genes. These DEGs were primarily enriched in detrimental autophagy, cell death, transcription factors, and cytokines, and were implicated in ferroptosis, abnormal lipid metabolism, inflammation, and oxidative stress pathways. Ten hub genes were screened from the constructed PPI network, including up-regulated genes such as FOS, HMOX1, SQSTM1, PTGS2, ATF3, DDIT3, and down-regulated genes MCM4, KIF15, UHRF1, and CCL2. Importantly, HMOX1 was further up-regulated in unstable plaques (p = 0.034). Finally, a regulatory network involving lncRNA/circRNA-miRNA-hub genes and drug-hub genes was established. CONCLUSION: Atherosclerotic endothelial dysfunction is associated with smoking-induced injury. Through bioinformatics analysis, we identified potential mechanisms and provided potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Fault activities modelling holds vital importance for earthquake monitoring, risk management, and early alert. Studies on laboratory earthquakes are instrumental in the modelling of natural fault ruptures and in enhancing our grasp of natural earthquake dynamics. Recently, deep learning methods have been proven effective in predicting instantaneous fault stress in laboratory settings and slow slip events on Earth. However, these methods have struggled to conduct steady future prediction lacking grasping of the complex dynamics of highly nonlinear laboratory fault slip systems. Addressing this, we introduce the Hankel Koopman Auto-encoder (HKAE), a novel method inspired by dynamical system theories. HKAE performs dynamic modelling of laboratory fault system and provides a continuous estimation of the future state of the system. It has been deployed on experiments with different slip behaviours and shows superior ability to predict shear stress variation during a slip cycle and also slip activities in longer-term seismic cycles. The HKAE model surpasses conventional time series prediction deep learning methods, showing superior statistical evaluation metrics like RMSE and R2 with two prediction horizons. Meanwhile, we find that the HKAE can model the slip dynamics better than purely statistical modelling, as evidenced by its more accurate modelling of the slip timing, slip cycle intervals and its ability to summarize the quasi-periodic dynamics as an operator from a small number of samples to generate more robust beyond-horizon prediction. The capability of HKAE to decompose and model complex temporal dynamics highlights its potential in and sparse-observed geophysical system with quasi-periodic characteristics like natural fault activities. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]