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Solid propellant fires, characterized by uncontrolled combustion behaviors, are a common occurrence in industries that utilize solid propellants and can lead to significant consequences. However, there has been a lack of comprehensive studies on modeling and simulating the flame characteristics of NEPE solid propellant fires. This research aims to investigate the flame behaviors of large NEPE solid propellants at different altitudes using computational fluid dynamics (CFD). The flame's shape and temperature were examined based on experimental data under atmospheric conditions to validate the numerical model. The findings reveal that the simulated flame temperature slightly surpasses the experimental values, while the simulated flame length and width align well with the experimental measurements. Morewover, increasing altitudes result in shorter flame lengths and lower combustion temperatures. Furthermore, the study scrutinizes the influence of turbulence models on the flame behavior of NEPE propellant fires.

The structure containing explosives may encounter different fire scenarios in application. The influence factors of flame radiant heat flow are numerous and the control mechanism is complex due to the complexity and diversity of fire scenes. It is necessary to identify key physical quantities, analyze the controlling mechanism, and establish boundary heat flow load model. Several factors were considered to comprehend and assess the thermal response characteristics of ammunition in a real fire environment, including the boundary heat flow load, the combined heat transfer effect of the air inter-layer, and the thermal decomposition of energetic explosive. Numerical model and calculation method were developed, and the thermal response characteristics of ammunition exposed to fire were investigated. The results demonstrate that the ammunition's temperature rise increases non-linearly as the thickness of flame engulfment increases. Conversely, the temperature rise decreases non-linearly as distance from the fire source increases. In terms of initiation delay time, it decreases non-linearly as the thickness of flame engulfment increases. However, initiation delay time increases non-linearly as distance from the fire source increases. A comprehensive model that considers real fire environment is crucial for prompt and accurate interventions, when assessing the thermal safety of ammunition in complex fire scenarios.

The widespread application of mobile robots holds significant importance for advancing social intelligence. However, as the complexity of the environment increases, existing Obstacle Avoidance Path Planning (OAPP) methods tend to fall into local optimal paths, compromising reliability and practicality. Therefore, based on the Spider-Wasp Optimizer (SWO), this paper proposes an improved OAPP method called the LMBSWO to address these challenges. Firstly, the learning strategy is introduced to enhance the diversity of the algorithm population, thereby improving its global optimization performance. Secondly, the dual-median-point guidance strategy is incorporated to enhance the algorithm’s exploitation capability and increase its path searchability. Lastly, a better guidance strategy is introduced to enhance the algorithm’s ability to escape local optimal paths. Subsequently, the LMBSWO is employed for OAPP in five different map environments. The experimental results show that the LMBSWO achieves an advantage in collision-free path length, with 100% probability, across five maps of different complexity, while obtaining 80% fault tolerance across different maps, compared to nine existing novel OAPP methods with efficient performance. The LMBSWO ranks first in the trade-off between planning time and path length. With these results, the LMBSWO can be considered as a robust OAPP method with efficient solving performance, along with high robustness.

Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

The relationship between microstructure and mechanical properties of multi-component solid waste low-carbon cementitious materials has been widely pay attention to. However, industrial solid waste is a complex multi-component system with many variable factors, which makes it difficult to design the formulation of cementitious materials. This paper pioneered the application of machine learning (ML) models, algorithms and error rates to analyze the compressive and flexural strength of fly ash-based pastes. Coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) and a20-index were used to evaluate robustness. X-ray diffraction (XRD), scanning electron microscope (SEM) and Brunauer-Emmett-Taylor (BET) were carried out to analyze evolution of cementitious materials. The evaluation results of ML models exhibited that the Gradient boosting regression (GBR) model had the best determination parameters and a steep normal distribution fitting curve with an a20-index of 0.861. GBR model exhibited the best robustness. The key factors of fly ash-based cementitious materials were identified by Pearson's coefficient, which was benefit to determine the formulation of multi-component solid waste low-carbon cementitious materials. Furthermore, experiments also demonstrated that the optimum ratio of multi-component solid waste low carbon cementitious material was 10 % gypsum, 10 % metakaolin, 45 % fly ash, 15 % slag and 20 % cement, respectively. It was worth noting that the compressive strength of this kind of multi-component solid waste low-carbon cementitious materials reached 35 MPa, which was superior to the mechanical properties of P·O 32.5 cement. The results of phase, SEM images and pore structure distribution showed that the synergistic effect of the multi-component solid waste materials effectively filled the material voids and also facilitated the formation of a variety of gelatinous materials through gelling reactions in the late stage (14–28 d). This work will promote the resource utilization of industrial solid waste, contribute to carbon reduction, and can accelerate the green revolution of concrete.

Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by synovial inflammation, causing substantial disability and reducing life quality. While macrophages are widely appreciated as a master regulator in the inflammatory response of RA, the precise mechanisms underlying the regulation of proliferation and inflammation in RA-derived fibroblast-like synoviocytes (RA-FLS) remain elusive. Here, we provide extensive evidence to demonstrate that macrophage contributes to RA microenvironment remodeling by extracellular vesicles (sEVs) and downstream miR-100-5p/ mammalian target of rapamycin (mTOR) axis. Results We showed that bone marrow derived macrophage (BMDM) derived-sEVs (BMDM-sEVs) from collagen-induced arthritis (CIA) mice (cBMDM-sEVs) exhibited a notable increase in abundance compared with BMDM-sEVs from normal mice (nBMDM-sEVs). cBMDM-sEVs induced significant RA-FLS proliferation and potent inflammatory responses. Mechanistically, decreased levels of miR-100-5p were detected in cBMDM-sEVs compared with nBMDM-sEVs. miR-100-5p overexpression ameliorated RA-FLS proliferation and inflammation by targeting the mTOR pathway. Partial attenuation of the inflammatory effects induced by cBMDM-sEVs on RA-FLS was achieved through the introduction of an overexpression of miR-100-5p. Conclusions Our work reveals the critical role of macrophages in exacerbating RA by facilitating the transfer of miR-100-5p-deficient sEVs to RA-FLS, and sheds light on novel disease mechanisms and provides potential therapeutic targets for RA interventions. Graphical abstract

Expansion of renewable energy could help realize the goals of peaking carbon dioxide emissions and carbon neutralization. Some existing grid dispatching methods integrating short-term renewable energy prediction and reinforcement learning (RL) have been proven to alleviate the adverse impact of energy fluctuations risk. However, these methods omit long-term output prediction, which leads to stability and security problems on optimal power flow. This paper proposes a confidence estimation Transformer for long-term renewable energy forecasting in reinforcement learning-based power grid dispatching (Conformer-RLpatching). Conformer-RLpatching predicts long-term active output of each renewable energy generator with an enhanced Transformer to ensure stable operation of the hybrid energy grid and improve the utilization rate of renewable energy, thus boosting dispatching performance. Furthermore, a confidence estimation method is proposed to reduce the prediction error of renewable energy. Meanwhile, a dispatching necessity evaluation mechanism is put forward to decide whether the active output of a generator needs to be adjusted. Experiments carried out on the SG-126 power grid simulator show that Conformer-RLpatching achieves great improvement over the second best algorithm DDPG in security score by 25.8% and achieves a better total reward compared with the golden medal team in the power grid dispatching competition sponsored by State Grid Corporation of China under the same simulation environment. Codes are outsourced in https://github.com/BUPT-ANTlab/Conformer-RLpatching.

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials’ structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.