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This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six hot springs, located within an active fault zone along the southeastern coast of China, were carefully chosen as hydrochemical monitoring sites for an extended period of two and a half years. Using this data, a prediction model integrating six algorithms was developed to forecast M ≥ 5 earthquakes in Taiwan. The model's performance was validated against recorded earthquake events, and the factors influencing its predictive capability were analyzed. Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre‐earthquake anomalies from post‐earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods. Plain Language Summary: This study explores the potential of utilizing machine learning algorithms for earthquake prediction based on hydrothermal fluid chemical anomaly data. An earthquake prediction model integrating six machine learning algorithms was developed using continuous hot spring hydrochemical monitoring data. The model's prediction performance was validated using actual earthquake event data, and various factors influencing its predictive capability were analyzed. The results demonstrate that the machine learning algorithm's prediction performance exceeds that of traditional methods, and the selection of hot springs, indicator types, and model parameters significantly impacts the model's prediction performance. This study successfully showcased the potential of machine learning algorithms in earthquake prediction, while also indicating new avenues for further research and improvement of prediction methods. Key Points: An earthquake prediction model based on machine learning using hydrochemical data from hot springs is proposed with sufficient accuracyThe earthquake prediction accuracy of the local outlier factor algorithm outperformed the traditional statistical methodThe predictive performance of the model varies across different hot springs, indicators type, and model parameters [ABSTRACT FROM AUTHOR]

Hyaluronic acid (HA) is a glycosaminoglycan and the main component of the extracellular matrix (ECM), which has been reported to interact with its receptor CD44 to play critical roles in the self‐renewal and maintenance of cancer stem cells (CSCs) of multiple malignancies. Melatonin is a neuroendocrine hormone with pleiotropic antitumor properties. However, whether melatonin could regulate HA accumulation in the ECM to modulate the stemness of head and neck squamous cell carcinoma (HNSCC) remains unknown. In this study, we found that melatonin suppressed CSC‐related markers, such as CD44, of HNSCC cells and decreased the tumor‐initiating frequency of CSCs in vivo. In addition, melatonin modulated HA synthesis of HNSCC cells by downregulating the expression of hyaluronan synthase 3 (HAS3). Further study showed that the Fos‐like 1 (FOSL1)/HAS3 axis mediated the inhibitory effects of melatonin on HA accumulation and stemness of HNSCC in a receptor‐independent manner. Taken together, melatonin modulated HA synthesis through the FOSL1/HAS3 axis to inhibit the stemness of HNSCC cells, which elucidates the effect of melatonin on the ECM and provides a novel perspective on melatonin in HNSCC treatment. [ABSTRACT FROM AUTHOR]

Trace elements in hot-spring waters are indicators for tracing hydrochemical processes, such as the deep circulation of geothermal water, the degree of water–rock interaction, redox reactions, the contamination of shallow water by thermal water, etc. In this study, 74 hot springs in the Xianshuihe–Xiaojiang Fault Zone (XSHF-XJF) were chosen for an investigation of the geochemical characteristics of trace elements using classic geochemical tools and multivariate statistical analysis. The results indicated (1) the hot-spring waters were mainly derived from atmospheric precipitation as indicated by δD and δ18O values that generally increased with decreasing elevations; (2) the high concentrations of B, As, Fe, and Mn in the waters, as well as the values of the Water Quality Index (WQI), indicated that the water quality was spatially heterogeneous and the hot-spring waters are not entirely suitable for drinking; and (3) B/Cl ratios showed that hot springs have different reservoir sources in the XSHF and XJF, respectively. The leaching of surrounding rock during water circulation contributed predominantly to the trace elements of the study's waters. Diversity of lithology was the main factor affecting their concentrations. In addition, deep circulation controlled by the fault could influence trace-element enrichment. Our results offer a guide for the exploration and use of geothermal resources. [ABSTRACT FROM AUTHOR]

The energy inside the Earth can not only be released outward through earthquakes and volcanoes but also can be used by humans in the form of geothermal energy. Is there a correlation between different forms of energy release? In this contribution, we perform detailed seismic and geothermal research in the Beijing area. The results show that the geothermal resources in Beijing belong to typical medium-low temperature geothermal resources of the sedimentary basin, and some areas are controlled by deep fault activities (e.g., Xiji geothermal well (No. 17)). The heat sources are upper mantle heat, radioactive heat in granite, and residual heat from magma cooling. The high overlap of earthquakes and geothermal field locations and the positive correlation between the injection water and earthquakes indicate that the exploitation and injection water will promote the release of the earth's energy. The energy releases are partitioned into multiple microearthquakes, avoiding damaging earthquakes (ML ≥ 5) due to excessive energy accumulation. Therefore, the exploitation of geothermal resources may be one way to reduce destructive earthquakes. Furthermore, the use of geothermal resources can also reduce the burning of fossil energy, which is of great significance in dealing with global warming. [ABSTRACT FROM AUTHOR]

Despite its ultrahigh theoretical capacity and ultralow redox electrochemical potential, the practical application of lithium metal anodes is still hampered by severe dendrite growth and unstable solid electrolyte interphase (SEI). Herein, a self‐assembled lithiophilic interface (SALI) for regulating Li electroplating behavior is constructed by introducing a meticulously synthesized Ni‐bis(dithiolene)‐based molecule (NiS4‐COOH) into a hybrid fluorinated ester‐ether electrolyte. The NiS4‐COOH molecules with carboxyl functional groups can spontaneously anchor on the Li metal surface to form a SALI, whose abundant Ni‐bis(dithiolene) sites can effectively reduce the initial Li deposition overpotential and guide the subsequent uniform Li electrodeposition. Moreover, due to the interaction between the coordination unsaturated Ni atom and the negatively charged PF6−, the NiS4‐COOH additive can significantly change the ionic coordination environment in the electrolyte, which is greatly conducive to suppressing PF6− decomposition, optimizing SEI composition and accelerating Li‐ion transfer. Consequently, the NiS4‐COOH‐modified electrolyte leads to impressive electrochemical performance of Li||LiFePO4 and Li||LiNi0.8Co0.1Mn0.1O2 batteries, delivering ultrahigh Coulombic efficiencies, considerable capacity retention, and good rate performance even at high areal active material loadings. This study presents the great potential of SALIs derived from multifunctional metal‐organic hybrid electrolyte additives toward high‐specific‐energy Li metal batteries. [ABSTRACT FROM AUTHOR]