Search

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earths complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the black-box nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earths complexities and further advance geoscience exploration.

The widespread integration of high-speed trains has significantly enhanced the quality of human life. However, the intricate electromagnetic environment within carriages has raised notable concerns, especially for passengers with pacemakers. This work assessed the electromagnetic exposure of three-chamber pacemaker wearers with standed and seated posture by a traction transformer and its cable system. The analysis included evaluating the distribution of induced electromagnetic fields within passengers’ pacemakers and heart tissue, calculating the induced voltage on electrodes to determine the potential health risks. The results indicated that the B $_{in~max}$ , E $_{in~max}$ of the passengers’ heart tissue are $7.53~\mu $ T and 47.626 mV/m. All the B $_{in~max}$ , E $_{in~max}$ well within the basic limits established for public electromagnetic exposure set by the International Commission on Non-Ionizing Radiation. The Ein on the pacemakers of both passengers mainly appeared at the metal parts, and the B $_{in~max}$ of passengers’ pacemakers is $20.754~\mu $ T, which achieves the minimum requirements for pacemakers to anti-electromagnetic interference as specified by the International Organization for Standardization. The $U_{in~max}$ at the unipolar and bipolar electrodes of passengers are 0.409 mV and 0.005 mV, which are smaller than the perception sensitivity. The above results illustrated that the electromagnetic fields generated by the traction transformer are within the recognized accepted limits for passengers with pacemaker, and we recommended that individuals with heart diseases should try to choose a pacemaker with better anti-interference ability in bipolar pacing mode and keep a distance from windows as possible when riding on high-speed trains.

Abstract Background Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. Methods Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. Results Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. Conclusions Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

With a conviction that general education should play a significant role in fostering the positive development of university students' character, several educators integrated various activities that could contribute to such development into general education courses in a technical university in Taiwan. This study surveyed the takers and non-takers of those courses and compared the differences in the scores of eight aspects of character development between the two groups of students. Results revealed that the implemented courses brought up small gains, particularly for male students in the second year, in most of the aspects including "logic and innovation," "social practices," "citizenship," "growth in learning," and "proactiveness and autonomy." Female students were found to score significantly higher than male counterparts in all of the aspects and the scores of most aspects exhibited a declining trend with the increasing university years. These results were discussed from the perspectives of the teaching strategies advantageous for character development as well as the mental difference between first-year and third- or fourth-year students.

Abstract 5G base stations (BSs) are potential flexible resources for power systems due to their dynamic adjustable power consumption. However, the ever‐increasing energy consumption of 5G BSs places great pressure on electricity costs, and existing energy‐saving measures do not fully utilise BS wireless resources in accordance with dynamic changes in communication load, resulting in flexible resource waste and seriously limiting electricity cost savings for 5G BSs. A multi‐BS cooperation self‐optimising sleep strategy for 5G BSs that consists of an initial user association stage based on multi‐BS cooperation (MBSC) and a self‐optimising variable‐threshold sleep stage (SVTS). First, a heterogeneous cellular network (HCN) model is established. Then, a 5G BS economic optimisation model is constructed, which aims at minimising the electricity cost of the BSs and takes the BS and user equipment (UEs) states in the HCN model as constraints to clarify the optimisation objective and constraints for the proposed strategy. Furthermore, BSs are initially associated with UEs through MBSC, and idle and lightly loaded BSs are then maximally put to sleep through SVTS to reduce power and energy consumption and thereby realise economic optimisation of the BSs. Finally, simulations are conducted to validate the proposed strategy and illustrate the ability of 5G BSs to provide flexible resource regulation for power systems.