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The accurate prediction of dissolved oxygen (DO) concentration in rivers is very important for the management of aquatic ecosystems, However, the hybrid model of ' modal decomposition + prediction ' for predicting the nonlinear change of dissolved oxygen in rivers is still insufficient. In this paper, a frequency division prediction framework based on the optimal ensemble of Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed. The dissolved oxygen sequence was decomposed into multiple components by CEEMDAN, and the long short-term memory network (LSTM), support vector regression (SVR) and multi-layer perceptron (MLP) models were constructed to predict each component independently. An innovative grid search algorithm with constraints is constructed, and the advantages of each model are complemented by dynamic combination. The optimal ensemble scheme is obtained with the goal of minimizing the mean absolute error ( MAE ) of the training set. The empirical study of monitoring sections A and B in the Ganjiang River Basin shows that : in the prediction task, the prediction of the training set, the MAE of the integrated model is 18.6–35.5% lower than that of the ensemble model, the root mean square error ( RMSE ) is 22.1–22.8% lower, and the determination coefficient ( R2 ) reaches 0.954 and 0.972. In particular, the error accumulation of MAE in the 3-day prediction is 27.2–81.4% lower than that of the mixed model. This framework enables the modes of multi-component dissolved oxygen series prediction to be effectively aliasing, and provides an extensible technical path for the intelligent management of the basin.

Rockburst present substantial hazards in both deep underground construction and shallow depths, underscoring the critical need for accurate prediction methods. This study addressed this need by collecting and analyzing 69 real datasets of rockburst occurring within a 500 m burial depth, which posed challenges due to the dataset's multi-categorized, unbalanced, and small nature. Through a rigorous comparison and screening process involving 11 machine learning algorithms and optimization with KMeansSMOKE oversampling, the Random Forest algorithm emerged as the most optimal choice. Efficient adjustment of hyper parameter was achieved using the Optuna framework. The resulting KMSORF model, which integrates KMeansSMOKE, Optuna, and Random Forest, demonstrated superior performance compared to mainstream models such as Gradient Boosting (GB), Extreme Gradient Boosting (XBG), and Extra Trees (ET). Application of the model in a tungsten mine and tunnel project showcased its ability to accurately forecast rockburst levels, thereby providing valuable insights for risk management in underground construction. Overall, this study contributes to the advancement of safety measures in underground construction by offering an effective predictive model for rockburst occurrences.

This study investigated wetting body migration and blind area distribution variations under different height differences (Δh) using indoor experiments and numerical simulations. Results show that the Δh of the injection hole shifts the wetting body intersection backward. Due to the increase in Δh, the vertical migration of the wetting peak at the No. 1 liquid injection hole accelerates, and the horizontal migration tends to be stable, which indicates that the Δh promotes the vertical seepage by changing the hydraulic gradient, which is beneficial to accelerate the leaching process. The migration of the wetting peak presents the characteristics of ‘fast first and then slow’, and it is easy to form a blind area in the later stage of leaching. When Δh is 0 and 3 cm, the blind area is concentrated between the two holes in the upper part of the ore heap. When Δh increases to 5 and 7 cm, the blind area expands to the top of the No. 1 hole. The simulation results show that although the increase in Δh can accelerate the recovery of water pressure in the near-end injection hole, it will increase the difference in leaching efficiency between ‘near-end’: when Δh is small, the wetting body diffuses symmetrically and the blind area is easy to eliminate; the increase in Δh leads to the asymmetric migration of the wetting body, and the remote area faces a significant risk of a blind area due to a low water pressure and low concentration.

In in situ leaching, fine particles can be stripped and transported with the leach solution, significantly altering the particle size distribution and pore structure of each layer of the rare earth ore body. In this study, water and magnesium sulfate were used as leaching agents. Based on indoor column leaching experiments, particle gradation experiments, and pore structure tests, this study investigates and analyzes the patterns of particle migration and changes in pore structure in rare earth ores with varying fine particle contents under leaching conditions. The results indicate that during the leaching process, the degree of change in particle gradation follows the order of upper layer > middle layer > lower layer. As the depth increases, the soil becomes denser, leading to reduced permeability, a slower seepage rate of the leaching solution, and a higher fine particle content, making the effect more pronounced. During magnesium sulfate leaching, the overall trend of porosity in the rare earth ore structure initially increases and then decreases. Additionally, a higher fine particle content corresponds to higher porosity. In the early and late stages of leaching, pore size changes involve the transformation of larger pores into smaller ones, followed by the conversion of smaller pores into larger ones. Moreover, the higher the fine particle content, the greater the degree of transformation between the pore sizes.

At present, the daily operation and maintenance of large-scale data centers such as banks in China, due to a variety of reasons, often brings about the problem of unexpected events that are difficult to locate. In order to ensure that the systems running in the data center work efficiently, this paper proposes a method for improving the operation, maintenance, and monitoring of financial data centers based on the cloud-native model. First, we sequentially cleanse and process the financial center data to eliminate any negative impact and generate a time-trending correlation of financial attributes. We then apply association mining to data center operation and maintenance, using stock information as an example to analyze the operational results in stock trading transactions. The result of correlation mining is component B index (up)⇒ component A index (up), support = 12/100, confidence = 12/19, which indicates that in 100 trading days, the number of days that the component B index and the component A index rise together is 12 days, while the number of days that the component B rises alone is 19 days. In the case study examining the impact of association mining in stock trading, on March 15, 2022, the stock price experienced a rise from 11.456 to 11.498 within a mere 0.1s. The financial data operation and maintenance system, using association mining, identified this as “abnormal,” demonstrating the model’s successful detection of abnormal behavior.

Since the first synthesis of 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105) in Lawrence Livermore National Laboratory (LLNL) of America in 1993, efforts of investigation into LLM-105 have never ended due to its premium performance. Therefore, with the goal to further emphasize the importance of this material and to provide reference to the application of LLM-105 in propellants and explosives, the properties and performance of LLM-105 are briefly introduced at first and then the application of LLM-105 is reviewed in various areas, including polymer bonded explosives, double-base rocket propellants with low signature and low sensitivity, propellants for perforating cartridges in oil and gas fields and gun propellants.

Planning an automatic detection system of the air tightness of medicine box is very necessary for growing demand for storage of pharmaceutical products. In this study, the high-fidelity CFD study is performed for the air tightness of the medicine box, using the LBM algorithm. The high-fidelity results indicate that after about 5.24 s, the stable value of the average static pressure of the ring cavity remains at about -9414 Pa. The slit flow in the early stage of micro-leakage is very complicated, especially for the initial stage. The location of the micro-leakage is concentrated in the directions of 1 o'clock, 4 o'clock, 7 o'clock and 11 o'clock along such edge of the end cap, therefore, the sealed end cover needs to be further improved. Additionally, the idea of this research work can provide a good basis and reference for the subsequent series of related airtightness studies.

Despite advances in cancer treatment, immune checkpoint blockade (ICB) only achieves complete response in some patients, illustrating the need to identify resistance mechanisms. Using an ICB-insensitive tumor model, here we discover cisplatin enhances the anti-tumor effect of PD-L1 blockade and upregulates the expression of Ariadne RBR E3 ubiquitin-protein ligase 1 (ARIH1) in tumors. Arih1 overexpression promotes cytotoxic T cell infiltration, inhibits tumor growth, and potentiates PD-L1 blockade. ARIH1 mediates ubiquitination and degradation of DNA-PKcs to trigger activation of the STING pathway, which is blocked by the phospho-mimetic mutant T68E/S213D of cGAS protein. Using a high-throughput drug screen, we further identify that ACY738, less cytotoxic than cisplatin, effectively upregulates ARIH1 and activates STING signaling, sensitizing tumors to PD-L1 blockade. Our findings delineate a mechanism that tumors mediate ICB resistance through the loss of ARIH1 and ARIH1-DNA-PKcs-STING signaling and indicate that activating ARIH1 is an effective strategy to improve the efficacy of cancer immunotherapy. Loss of the E3 ubiquitin-protein ligase ARIH1 has been associated with cancer escape from anti-tumor immunity. Here the authors show that ARIH1 mediated ubiquitination and degradation of DNA-PKcs trigger activation of STING pathway in tumor cells, sensitizing tumors to immune checkpoint blockade.

The accurate prediction of soil organic matter (SOM) content is essential for promoting sustainable soil management and addressing global climate change. Due to multiple factors such as topography and climate, especially in mountainous areas, SOM spatial prediction faces significant challenges. The main novelty of this study lies in proposing a geographic positional encoding mechanism that embeds geographic location information into the feature representation of a Transformer model. The encoder structure is further modified to enhance spatial awareness, resulting in the development of the Geo-Positional Transformer (GPTransformer). Furthermore, this model is integrated with a 1D-CNN to form a dual-branch neural network called the Geo-Positional Transformer-CNN (GPTransCNN). This study collected 1490 topsoil samples (0–20 cm) from cultivated land in Longshan County to develop a predictive model for mapping the spatial distribution of SOM across the entire cultivated area. Different models were comprehensively evaluated through ten-fold cross-validation, ablation experiments, and uncertainty analysis. The results show that GPTransCNN has the best performance, with an R2 improvement of approximately 43% over the Transformer, 19% over the GPTransformer, and 15% over the 1D-CNN. This study demonstrates that by incorporating geographic positional information, GPTransCNN effectively combines the global modeling capabilities of the GPTransformer with the local feature extraction strengths of the 1D-CNN, which can improve the accuracy of SOM mapping in mountainous areas. This approach provides data support for sustainable soil management and decision-making in response to global climate change.

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.