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Due to the unobservable nature of underground construction and the destructive nature of horizontal directional drilling rigs with high power, this type of construction has become one of the most important causes of failure of long-distance natural gas pipelines. In recent years, horizontal directional drilling construction has caused pipeline accidents frequently. Once the accident occurs, the normal operation of natural gas pipelines cannot be ensured. Therefore, studying the damage mechanism of buried natural gas pipelines under horizontal directional drilling loads is important for the safe operation of pipelines. This paper combines the construction characteristics of horizontal directional drilling and the actual situation of natural gas pipelines to explore the relationship between horizontal directional drilling and pipelines. The force situation of pipelines after contacting directional drilling bits is analyzed by the drill bit-soil-pipe finite element model created in the ABAQUS software. The Johnson–Cook ductile damage model was utilized to determine the pipe's damage condition. The sensitivity analysis results show that he order of the impact of key parameters on the dynamic response of the pipe is bit thrust > wall thickness > bit diameter > pipe diameter > bit speed > number of bit teeth > pipe operating pressure. Therefore, priority should be given to controlling the size of the drilling thrust and the speed of the drill bit to reduce the damage to pipelines by horizontal directional drilling construction. In addition, appropriately reducing the pipeline operating pressure can also reduce the risk of the pipeline being damaged by horizontal directional drilling construction. [ABSTRACT FROM AUTHOR]

Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA). Specifically, ESA enhances high-frequency texture features in the feature maps, and MLSKA executes the further extraction. The rich and fine-grained high-frequency information are extracted and fused from multiple perceptual layers, thus improving super-resolution (SR) performance. To validate FECAN’s effectiveness, we evaluate it with different complexities by stacking different numbers of high-frequency enhancement modules (HFEM) that contain FECA. Extensive experiments on benchmark datasets demonstrate that FECAN outperforms state-of-the-art lightweight SR networks in terms of objective evaluation metrics and subjective visual quality. Specifically, at a × 4 scale with a 121 K model size, compared to the second-ranked MAN-tiny, FECAN achieves a 0.07 dB improvement in average peak signal-to-noise ratio (PSNR), while reducing network parameters by approximately 19% and FLOPs by 20%. This demonstrates a better trade-off between SR performance and model complexity. [ABSTRACT FROM AUTHOR]

Molnupiravir (MO) is a pyrimidine nucleoside anti‐SARS‐CoV‐2 drug. MO treatment could cause mild liver injury. However, the underlying mechanism of MO‐induced liver injury and the metabolic pathway of MO in vivo are unclear. In this study, metabolomics analysis and molecular biology methods were used to explore these issues. Through metabolomics analysis, it was found that the homeostasis of pyrimidine, purine, lysophosphatidylcholine (LPC), and amino acids in mice was destroyed after MO treatment. A total of 80 changed metabolites were detected. Among these changed metabolites, 4‐ethylphenyl sulfate, dihydrouracil, and LPC 20:0 was related to the elevation of alkaline phosphatase (ALP), interleukin‐6 (IL6), and nuclear factor kappa‐B (NF‐κB). The levels of 4‐ethylphenyl sulfate, dihydrouracil, and LPC 20:0 in plasma were positively correlated with their levels in the liver, suggesting that these metabolites were associated with MO‐induced liver injury. MO treatment could increase NHC and cytidine levels, activate cytidine deaminase (CDA), and increase LPC levels. CDA and LPC could increase the mRNA expression level of toll‐like receptor (TLR). The current study indicated that the elevation of hepatic TLR may be an important reason for MO leading to the liver injury. [ABSTRACT FROM AUTHOR]

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline. Residents in the area use rototillers for planting and harvesting; however, the depth of the rototillers into the ground is greater than the depth of the pipeline, posing a significant threat to the safe operation of the pipeline. Therefore, it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe operation of pipelines. This article focuses on the Shanxi-Beijing natural gas pipeline, utilizing finite element simulation software to establish a finite element model for the interaction among the machinery, pipeline, and soil, and analyzing the dynamic response of the pipeline. At the same time, a decision tree model is introduced to classify the damage of pipelines under different working conditions, and the boundary value and importance of each influencing factor on pipeline damage are derived. Considering the actual conditions in the hemp yam planting area, targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region. [ABSTRACT FROM AUTHOR]

Object detection in remote sensing images has become a crucial component of computer vision. It has been employed in multiple domains, including military surveillance, maritime rescue, and military operations. However, the high density of small objects in remote sensing images makes it challenging for existing networks to accurately distinguish objects from shallow image features. These factors contribute to many object detection networks that produce missed detections and false alarms, particularly for densely arranged objects and small objects. To address the above problems, this paper proposes a feature enhancement feedforward network (FEFN), based on a lightweight channel feedforward module (LCFM) and a feature enhancement module (FEM). First, the FEFN captures shallow spatial information in images through a lightweight channel feedforward module that can extract the edge information of small objects such as ships. Next, it enhances the feature interaction and representation by utilizing a feature enhancement module that can achieve more accurate detection results for densely arranged objects and small objects. Finally, comparative experiments on two publicly challenging remote sensing datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the existence of structural and material property discontinuities in the ring-welded joints of large-diameter, high-steel-grade pipelines, which are prone to the risk of fracture. Therefore, in this paper, computer-aided engineering techniques, based on finite element analysis and numerical calculations, have been used to establish a model of pipeline containing internal surface crack defects for simulation, and J-integral algorithms have been applied to evaluate the effect of crack parameters on fracture behavior. This research highlights the critical role of computers in pipeline engineering and provides an important theoretical and methodological basis for further optimizing pipeline design and maintenance. [ABSTRACT FROM AUTHOR]

Tourists’ spatial-temporal behavior has complex and dynamic randomness, which affects the intelligent management of scenic spots. Understanding and mastering the spatial-temporal behavior of tourists within the peak period of scenic spots is one of the key means to improving the competitiveness of scenic spots and enhancing tourism satisfaction. This paper explores how to effectively optimize the tourists’ spatial-temporal behavior and improve the overall efficiency of scenic spots. Firstly, we built a simulation model and found the transfer change law between attractions through historical data. Then, we predicted the spatial-temporal distribution of the number of tourists to their subsequent attractions and selected the minimum load attraction as the next destination for tourists. Finally, the simulation experiment proves that the dynamic selection optimization based on individual tourist behavior can balance the scenic spot load and alleviate the congestion level.

As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. This study established two-dimensional hydrodynamic and oil spill models for the Lancang River crossing pipeline basin and verified the model's accuracy. The oil spill transport process under different scenarios was simulated, and the oil spill transport state data set was established. The effects of river flow, wind, and leakage mode on the transport behavior of oil spills were studied. The results show that an increase in flow rate accelerates the migration, diffusion, and longitudinal extension behavior of oil spills; Changes in wind speed have less effect on the transport behavior of oil spills under downwind and headwind conditions. The mode of leakage mainly affects the diffusion and longitudinal extension of the oil spill. The oil spill transport state prediction model was established using machine learning combination algorithms. The three combined machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and the length of the oil spill contamination zone, respectively, with the coefficient of determination (R2) and the 1-Mean Absolute Percentage of Error (1-MAPE) above 0.971, and the prediction model has excellent accuracy. This study can provide support for the rapid development of emergency response plans for river crossing pipeline oil spill accidents. [ABSTRACT FROM AUTHOR]