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The SF6 circuit breaker is an essential piece of high-voltage equipment in ensuring the safe operation of the power grid. Regarding the arc-extinguishing chamber, as the most essential component, its performance is directly related to the breaking capacity of the circuit breaker. This study applies the Double Distribution Function Lattice Boltzmann Method (DDF-LBM), combined with the Smagorinsky sub-grid scale (SGS) model, to systematically simulate the dynamic breaking process of a 252 kV SF6 arc-extinguishing chamber under 50 kA breaking current conditions. Two independent distribution functions are employed to describe the fluid field and the temperature field, respectively, thereby simulating the physical flow–heat coupling process. A dynamic simulation framework is constructed using the D2Q9 model to describe the mechanical motion of the contacts and the fluid flow. The description of contact movement is achieved by dynamically updating the geometric mesh, thereby realizing fluid–solid transformation. The research results indicate that the proposed method can simulate the pressure variation of the fluid field during the breaking process. The value of the Smagorinsky constant (Cs) exhibits a non-negligible influence on the pressure field predictions. The optimal value of Cs = 0.10 is determined through analysis, and the peak pressures at the upstream and throat measurement points reach 1.11 MPa and 1.37 MPa, respectively. Numerical simulations are conducted on the dynamic breaking process of the arc-extinguishing chamber, revealing the evolution of the pressure field upstream of the nozzle and at the throat regions. This study provides new numerical simulation methods for the investigation of SF6 arc-extinguishing chambers and establishes a foundation for the application of the Lattice Boltzmann Method in the field of high-voltage electrical appliances.

The SF[sub.6] circuit breaker is an essential piece of high-voltage equipment in ensuring the safe operation of the power grid. Regarding the arc-extinguishing chamber, as the most essential component, its performance is directly related to the breaking capacity of the circuit breaker. This study applies the Double Distribution Function Lattice Boltzmann Method (DDF-LBM), combined with the Smagorinsky sub-grid scale (SGS) model, to systematically simulate the dynamic breaking process of a 252 kV SF[sub.6] arc-extinguishing chamber under 50 kA breaking current conditions. Two independent distribution functions are employed to describe the fluid field and the temperature field, respectively, thereby simulating the physical flow–heat coupling process. A dynamic simulation framework is constructed using the D2Q9 model to describe the mechanical motion of the contacts and the fluid flow. The description of contact movement is achieved by dynamically updating the geometric mesh, thereby realizing fluid–solid transformation. The research results indicate that the proposed method can simulate the pressure variation of the fluid field during the breaking process. The value of the Smagorinsky constant (Cs) exhibits a non-negligible influence on the pressure field predictions. The optimal value of Cs = 0.10 is determined through analysis, and the peak pressures at the upstream and throat measurement points reach 1.11 MPa and 1.37 MPa, respectively. Numerical simulations are conducted on the dynamic breaking process of the arc-extinguishing chamber, revealing the evolution of the pressure field upstream of the nozzle and at the throat regions. This study provides new numerical simulation methods for the investigation of SF[sub.6] arc-extinguishing chambers and establishes a foundation for the application of the Lattice Boltzmann Method in the field of high-voltage electrical appliances.

To address the challenges of detecting small targets in complex underwater environments, an efficient and lightweight model, SFESI-YOLOv8n, is proposed. The model improves small target recognition by incorporating a dedicated detection layer and reduces parameter count by removing the large target detection layer. Furthermore, the introduction of the C2f-F module eliminates redundant information from consecutive convolution operations in the bottleneck, further simplifying the model. The integration of a lightweight mixed local context attention (MLCA) mechanism within the small target fusion layer increases sensitivity to small targets. The dynamic upsampler (DySample) employs point sampling to preserve enhanced edge and detail information in feature maps, resulting in clearer feature representations. In addition, the novel In-NWD loss function, utilizing Wasserstein distance and auxiliary bounding boxes, improves small target detection performance. On the UPRC2020 public dataset, SFESI-YOLOv8n achieved an mAP@0.5 of 83.7%, which is a 1.1% improvement over the baseline model. The parameter count and size of the model were reduced by 49.2% and 45.7%, respectively. The frame rate reached 227 FPS, indicating a 9 FPS increase compared to the baseline. On the NVIDIA Jetson TX2 edge device, inference latency decreased from 65 ms to 24 ms with TensorRT acceleration, thereby meeting real-time detection requirements. The SFESI-YOLOv8n model provides a viable and efficient solution for the autonomous detection of small underwater targets, demonstrating significant practical value.

As one of the specific practices of natural resource index trading, the forest ecological bank policy (FEB) is essentially a market-based tool. With the deepening of ecological governance, the FEB policy has also become the main method chosen to solve the economic development problems in ecologically rich “low-lying” areas. However, in the process of implementing the FEB policy, the differences in the demands of various stakeholders were found to have led to a complex game phenomenon, resulting in deviations in policy implementation. This study constructs a multiplayer evolutionary game model between local governments and enterprises of different scales and analyzes the evolutionary stabilization strategy (ESS) in the implementation of the FEB policy. The results show that, under different conditions, there are three stabilization strategies in the evolutionary game system, these correspond to F1 (0, 0, 0), F4 (0, 1, 1), and F5 (1, 0, 0), respectively, the implications are that the strict government role with an active regulatory strategy leads to companies of different sizes refusing to participate (i.e., F5) and the lax government role with a negative regulatory strategy leads to companies of different sizes refusing to participate (i.e., F1) or choosing to participate (i.e., F4). Among them, the strict government role stimulates the companies to participate in the FEB policy through the high intensity of government regulation. In addition, as the policy continues to be implemented, the influence of the strict regulation on the “participation” behavior of the companies decreases. Conversely, the lax government role allows the companies to give full play to their autonomy and obtain higher ecological and environmental benefits.

Southwest Yunnan, located in the southern segment of the north-south seismic belt, is one of the regions with strong tectonic movement and seismic activity in China. Study on the characteristics of tectonic setting and deep geophysical field in the region is an important issue in basic science. In 2013, we conducted a 600-km-long Menghai-Gengma-Lushui profile of deep seismic wide-angle reflection/refraction and high-resolution seismic refraction in Southwest Yunnan. In this paper, we use 6 groups of clear intracrustal P-wave phases picked from the seismic record sections of 11 shots to build a velocity structure model of basement and 2D crustal P-wave of the region by using finite difference inversion and ray travel time forward fitting technology. The results show that, from south to north, the crust gradually thickens along the profile and its basement shows a significant lateral heterogeneity. In the vicinity of the Nanting River fault, the basement structure shows the character of alternate depressions and uplifts, and the shallowest basement is about 1.0 kin. In the vicinity of Tengchong and Lancang, the basement is about 5.0 km deep. The velocity of the middle and lower crust in the region generally increases with the increasing of depth. At the block boundary and beneath the fault tectonic belt, the velocity contours show apparent irregularity and the P-wave velocity changes sharply. In this region, the Moho gradually deepens from south to north with relatively large lateral undulations. The shallowest point of the Moho is located near Menghai at a depth of about 32.0 km. The deepest point of the Moho is located near Tengchong at a depth of about 40.5 km. Between Gengma and Yongde, the Moho shows significantly fast uplifting and depressing with an amplitude of about 4.0 km. Beneath the Nanting River fault, Longling-Ruili fault, Dayingjiang fault and Tengchong volcano, the basement velocity structure, 2D crustal P-wave velocity structure, distribution of average profile velocity and intracrustal interface spreading also show significant changes from the basement to the top of the Moho, indicating that the crustal velocity and medium physical properties beneath the fault tectonic belt are apparently different from the crustal materials on its both sides, which suggests that these faults should be in a certain scale and may extend to the lower crust or the top of the upper mantle. The earthquakes in the region mainly occurred at a depth of 10-20 km, and the seismic activity is related to the intracrustal medium velocity difference and fault belt distribution. The results can serve as the important data of the crust-mantle structure for the analysis of the deep tectonic setting, earthquake precise positioning, seismogenic structure modeling of the seismic activities in Southwest Yunnan, as well as the important reference for the evaluation of seismic hazard and the planning of earthquake disaster mitigation of this region.

Concentrations of suspended particle material （SPM）, dissolved silicate （DSi）, biogenic silica （BSi）, phytoliths （plant produced siliceous microscopic structures）, and other parameters were analyzed to examine the influence of both natural processes and human activities on silica delivery to the estuary of the Huanghe River （Yellow River）. Our results indicate that the concentrations of DSi in the river decreased significantly since 1986. Approximately 34% of dissolved silica was trapped in the basin between 1986 and 2010 due to a reduction of soil erosion. Phytoliths comprised 67.2%-96.3% of BSi, with the smoothing bar type being the dominant form. Concentrations of BSi are significantly higher in the Huanghe River compared to other major rivers throughout the world due to its high sediment yield. We also found that the ratios of BSi/（BSi＋DSi） and BSi/SPM were approximately 0.5 and 0.003 at Lijin near the river mouth, indicating that BSi carried in suspension by the Huanghe River was an important component of the rivers silica load. Significant amounts of BSi were also composed of phytoliths in Bohai Sea sediments near the Huanghe River estuary with the smoothing bar form again being the most abundant. The relatively high specific fluxes of BSi in the Huanghe River reflect its high turbidity and high erosion rates in the basin. The high sediment load originating on the Loess Plateau is likely responsible for the higher BSi flux, in agreement with a general trend of increasing BSi flux with increasing sediment flux in global river systems. This study demonstrates that BSi transported by rivers can be composed largely of phytoliths originating from the erosion of topsoils. The flux of phytoliths in river＇s suspended sediment load may therefore represent a significant contribution to the biogeochemical cycle of silica in coastal waters.

Shrimp diseases are one of the primary causes of economic losses in shrimp aquaculture. To prevent disease transmission and enhance intelligent detection efficiency in shrimp farming, this paper proposes a lightweight network architecture based on YOLOv8n. First, by designing the RLDD detection head and C2f-EMCM module, the model reduces computational complexity while maintaining detection accuracy, improving computational efficiency. Subsequently, an improved SegNext_Attention self-attention mechanism is introduced to further enhance the model's feature extraction capability, enabling more precise identification of disease characteristics. Extensive experiments, including ablation studies and comparative evaluations, are conducted on a self-constructed shrimp disease dataset, with generalization tests extended to the URPC2020 dataset. Results demonstrate that the proposed model achieves a 32.3% reduction in parameters compared to the original YOLOv8n, with a mAP@0.5 of 92.7% (3% improvement over YOLOv8n). Additionally, the model outperforms other lightweight YOLO-series models in mAP@0.5, parameter count, and model size. Generalization experiments on the URPC2020 dataset further validate the model's robustness, showing a 4.1% increase in mAP@0.5 compared to YOLOv8n. The proposed method achieves an optimal balance between accuracy and efficiency, providing reliable technical support for intelligent disease detection in shrimp aquaculture.

Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW module are designed to improve the backbone network, enhancing the model's ability to perceive micro-lesions. Secondly, a fea-ture-focused diffusion pyramid network FDPN is designed to fully integrate multi-scale context information, further improving the model's ability to perceive micro-lesions. Finally, a lightweight shared detection head GSDHead is designed to reduce the model's parameter count, making it more deployable on re-source-constrained devices. Experimental results show that compared with the base model YOLOv8n, the improved model reduces the parameter count by 20.7%, increases mAP@0.5 by 4.1%, and improves the recall rate by 7.9%. Compared with single-stage mainstream algorithms such as YOLOv5n and YOLOv10n, YOLO-KFG demonstrates significant advantages in both detection accuracy and efficiency.

Shenmai injection （SMI） is a Chinese patent-protected injection, which was mainly made of Red Ginseng and Radix Ophiopogonis and widely used for treating coronary heart disease and tumors by boosting Qi and nourishing Yin. In this study we examined whether SMI could produce direct synergetic effects on the cytoxicity of adriamycin （ADR） and paclitaxel （PTX） in colorectal cancers in vivo and in vitro, and explored the underlying pharmacokinetic mechanisms. BALB/c nude mice with LoVo colon cancer xenografts were intraperitoneally injected with ADR （2 mg.kg-1.3d-1） or PTX （7.5 mg.kg-1.3d-1） with or without SMI （0.01 mL.g-1.d-1） for 13 d. Co-administration of SMI significantly enhanced the chemotherapeutic efficacy of ADR and PTX, whereas administration of SMI alone at the given dosage did not produce visible anti-cancer effects, The chemosensitizing action of SMI was associated with increased concentrations of ADR and PTX in the plasma and tumors. In Caco-2 and LoVo cells in vitro, co-treatment with SMI （2 μL/m L） significantly enhanced the cytotoxicity of ADR and PTX, and resulted in some favorable pharmacokinetic changes in the subcellular distribution of ADR and PTX. In addition, SMI-induced intracellular accumulation of ADR was closely correlated with the increased expression levels of P-glycoprotein in 4 colon cancer cell lines （r2=＋0.8558）. SMI enhances the anti-cancer effects of ADR and PTX in colon cancers in vivo and in vitro by improving the subcellular distributions of ADR and PTX.

Three comprehensive surveys were performed in the Changjiang（Yangtze River） Estuary（CJE） to understand the biogenic silica（BSi） composition, behavior and budget. It is indicated that the BSi is composed of phytoliths,phytoplankton and sponges; phytolith BSi has 16 forms and account for 23% to 83% of the bulk BSi in the maximum turbidity zone. The budget shows that the major exogenous BSi source in the water column of the CJE is the riverine input, accounting for 95% of the total BSi input. Dominant processes that maintain BSi levels in the water column are the primary production（55 Gmol/a） and the subsequent BSi sedimentation（46 Gmol/a）; and the BSi pool produced by the primary production represents two point three times the BSi loading of the Changjiang River and 63% of the BSi output, respectively. The net export（26 Gmol/a） of BSi from the CJE to the East China Sea and Yellow Sea roughly equals the riverine BSi loading. The observed total accumulation of BSi is one point seven times larger than the loading of total BSi output, with 53% to 88% of phytolith BSi and their assemblage, indicating that there has already been a ＂filter＂ of terrestrial BSi. The reverse weathering in sediments is an important process for the reactive silica removal in the CJE due to authigenic alterations. It is indicated that the phytolith fluxes in the suspended load represent a significant BSi source in the estuary, and the CJE would act as a net BSi sink.