In the context of rising global CO₂ content, Carbon Capture, Utilization and Storage (CCUS) has come into people's attention because it can effectively reduce atmospheric CO₂ content. CCUS is mainly divided into three steps: capture, utilization and storage. Researchers have developed corresponding technologies that can improve efficiency and reduce losses in each link, and mature demonstration projects have emerged. However, the current development of CCUS still faces many difficulties, such as the technology is not mature enough, the integration level is low, the investment cost is high, the relevant talent shortage, and the business model is not yet formed and unclear. However, researchers have proposed some feasible solutions. This paper focuses on the CCUS process, outstanding related technologies, and lists some demonstration projects. In addition, it also summarizes the problems and solutions facing the current development of CCUS. Finally, the future of CCUS is prospected.

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Hydrogen sulfide (H₂S), as a critical gasotransmitter, plays a dual role in regulating tumor growth depending on its concentration and tumor type. In nasopharyngeal carcinoma (NPC), systematic reviews have demonstrated the involvement of H₂S in multiple cellular processes, including cell proliferation, apoptosis, and metastasis. This study systematically explores the molecular mechanisms of H₂S in NPC, focusing on its role in modulating signaling pathways, for example, PI3K/AKT, Wnt/β-catenin, and ROS-mediated pathways. The findings indicate that H₂S exerts its effects by regulating the expression of oncogenes and tumor suppressor genes, altering the tumor microenvironment, and influencing mitochondrial function. These insights provide a foundation for novel therapeutic strategies targeting H₂S metabolism in NPC.

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In this paper, a Random Forest model and an ARIMA model are established to predict stock closing prices. The data from six Chinese stocks—Shandong Gold Mining, WanYi Technology, iFLYTEK, Space-Time Technology, TianYue Advanced Materials Technology, and Harmontronics Intelligent Technology—spanning from January 1, 2024, to December 30, 2024, are used. The results show that these two models are more accurate in short-term stock price prediction. By combining and comparing these two models, we conclude that the Random Forest model predicts the next day's closing price highly accurately and has obvious advantages in ultra-short-term trading. This offers insights for investors and related researchers in pursuit of short-term profits.

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This paper proposes a multi-frame dual-stream 2DCNN-LSTM model (MF-DS-2DCNN-LSTM) for automatic modulation recognition. The model discretizes long sequences into two-dimensional frame structures and uses 2D CNN and LSTM together to model the spatiotemporal features of multi-channel IQ/AP signals. By employing a frame-based strategy, the original signal is reshaped into” small images,” with the 2D CNN extracting intra-frame spatial structures and inter-channel interaction features, while the LSTM captures the temporal evolution between frames. This approach integrates hierarchical modeling concepts from image processing and video analysis, and utilizes the Crested Porcupine Optimizer for hyperparameter tuning. Simulations show that, when recognizing nine modulation types, the model significantly outperforms methods such as CLDNN, achieving an average accuracy of 91.4% under high-SNR conditions (SNR above 2 dB). Moreover, the model maintains an accuracy of over 90% in small-sample training scenarios for SNRs above 4 dB. After optimization with the Crested Porcupine Optimizer, the model’s performance improved by 2.2%, and a 20.7% reduction in parameters was achieved.

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