In order to achieve accurate segmentation of surgical instruments, a dual-encoding network surgical instrument segmentation method was proposed based on improved Swin Transformer. By taking advantage of different coding advantages of Swin Transformer and convolutional neural network(CNN), the global semantic information and local details of image features can be effectively captured to improve the expression ability of the model. To compensate for the loss of feature details during the downsampling process as much as possible, the multi-resolution feature pyramid pooling(MFPP) block was constructed to obtain more scale context information by combining different dimensional features and enhance the expression of local detail information. Finally, a coordinate attention block was added in the skip connection to fuse target position information with feature information for precise perception of the surgical instrument targets. The experimental results show that the proposed method achieves more accurate segmentation results in both binary and parts segmentation of surgical instruments, further verifying the effectiveness and accuracy of the proposed method.

The development of hydrogen-powered aircraft is a key strategy for the aviation industry to achieve carbon neutrality. Compared to high-pressure gaseous hydrogen, cryogenic liquid hydrogen will be the main fuel for future hydrogen-powered commercial aviation. However, the occurrence of cavitation in liquid hydrogen during transport has the potential to result in an unstable or even interrupted fuel supply to the engine, which could ultimately lead to catastrophic risks to flight safety. Using numerical simulation method, based on homogeneous mixed flow model, Navier-Stokes (RANS) method and Zwart cavitation model, the cavitation flow characteristics and development law of liquid hydrogen in aircraft transport pipelines were deeply studied, and partially compared with normal temperature water. The results show that the cavitation number, the outlet/inlet pressure ratio, and the length/diameter ratio have a significant influence on the occurrence and development of cavitation. The condensation process of liquid hydrogen is considerably slower than the evaporation process. The effect of the cavitation number on the evaporation process is minimal, but it has a significant effect on the maximum condensation rate. The critical pressure ratio for the disappearance of cavitation in liquid hydrogen is lower than in water. At the same pressure ratio, water cavitates more easily than liquid hydrogen, with a greater number of cavitation bubbles and a thicker cavitation region. Reducing the length/diameter ratio can inhibit the occurrence and development of cavitation in liquid hydrogen. It is recommended that the diameter of the contraction section be increased to achieve a higher outlet flow, rather than shortening the length of the pipeline.

Under the guidance of the full life cycle design concept, a scheme of moving the existing downspout pipe scheme to the reinforced concrete peripheral column was proposed. The main feature of this scheme is that the downspouts are pre-embedded into the surrounding columns and used for roof drainage during the building's service life. At the end of the building's lifespan, the downspouts were used to remove blast holes. To demonstrate the feasibility of this scheme, first of all, it was verified that the material and diameter of the embedded downspout could simultaneously meet the drainage function and the function of removing the blast hole. Secondly, by drawing on the axial embedded hole blasting technology of reinforced concrete beams and using the new type of blasting equipment, the long bag for loading explosives, the feasibility of using downspouts for blasting demolition was demonstrated from both the charge structure and the blasting operation aspects. Finally, the finite element analysis software ABAQUS was used to analyze the stress distribution and stress-strain laws when two types of cross-sectional peripheral columns were placed in four downspouts with different diameters. The analysis results show that when the hollow rate is small, the embedded pipe has little influence on the specimens. In order to avoid significant impact on the peripheral columns, the hollow rate of the specimens should not be greater than 2%. In combination with the requirements of blasting demolition, the hollow ratio should not be less than 0.18% either. Subsequently, it was pointed out that the relay service duration of pipes and concrete holes is sufficient to reach the building's life cycle. Finally, it is clarified that after the downspout is built in, it can also enhance the aesthetic appeal of the building and avoid the safety risk of thieves climbing along the downspout. Therefore, it is feasible to insert reinforced concrete peripheral columns into the downspout pipe.

To investigate the annual variation characteristics of soil temperature around the medium-shallow coaxial tube ground heat exchanger, a two-dimensional unsteady heat transfer model for the medium-shallow coaxial tube ground heat exchanger was established. The model was solved based on the finite volume method and validated using experimental data from the project. The research findings indicate that under individual heating and cooling conditions, the circulation mode of outer-in and inner-out has a greater impact on soil temperature at a depth of 100 m, while the inner-in and outer-out mode has a more significant effect on soil temperature at a depth of 500 m. During summer conditions, reverse heat transfer is more likely to occur with the outer-in and inner-out circulation mode. At the end of the first winter (or summer) season, the thermal influence radius of the soil is less than 10 m, but this radius increases over time. At the end of one operational cycle, the soil temperature increases at depths shallower than approximately 300 m and decreases at depths deeper than approximately 300 m. Orthogonal experiments reveal that the inlet water temperature in both winter and summer has a notable impact on temperature fluctuations at a soil depth of 100 m, while the inlet water temperature in summer significantly affects temperature fluctuations at a soil depth of 500 m.

The Shenzijing-Hashituo Subsag is located in the central part of the Changling fault depression in the Songliao Basin, an area with promising oil and gas exploration prospects. It represents a favorable oil and gas accumulation zone within the Songliao Basin. Understanding the tectonic evolution of this region and its influence on hydrocarbon genesis is crucial for guiding future exploration and development efforts in the Changling fault depression. Based on the structural characteristics, ancient drop of faults, activity rate and growth index method, the structural evolution of the depression was analyzed. Combined with the characteristics of stratum erosion, it was determined that the Shenzijing-Hasituo subsag was co-deposited in the early stage, and the Shahezi Formation was affected by compression and uplift at the end, resulting in overall erosion and segmentation. The uplift on the west side of the Hasituo depression was further strengthened during the Denglouku Formation period. At the same time, it is clear that the tectonic evolution of the fault depression layer in the Shenzijing depression-Hatshituo depression of the Changling Fault depression follows the longitudinal evolution law of fault depression-co-deposition-inversion. This early rapid subsidence and late inversion and uplift form a relatively favorable accumulation combination model of lower generation and upper storage. The research results can provide theoretical references for the restoration and further exploration of the ancient structure of the Changling fault depression.

With the expansion of the Internet scale and changes in its topological structure, network management is facing huge challenges. Segment routing (SR) protocols, especially SRv6(segment routing over IPv6), have become a research hotspot due to their high programmability and scalability. The path optimization control mechanism based on SRv6 solves the problem of avoiding and relaying specific nodes in multiple demands and scenarios to improve network performance. A path transfer scheme for fully deployed SRv6 networks was proposed, and the routing overhead was reduced through the optimization of forked paths. For some deployment networks, define the critical path and design the avoidance and relay path forwarding scheme to optimize the path forwarding efficiency. The experimental results show that when SRv6 is fully deployed, the optimization scheme can effectively reduce the depth of the segment list and the routing overhead. In some deployment networks, only a small number of SRv6 nodes can achieve performance close to that of a full SRv6 network, successfully solving the problem of evading and relaying specific nodes. The research results provide effective support for the application of SRv6 in different network deployments.

The current acupoint automatic positioning technology for massage robots is faced with issues such as dress restrictions, limited application scope, and poor positioning accuracy. A new method for human back acupoint positioning, based on back morphology classification and a multilayer perceptron network morphology classification-multilayer perceptron-based accurate point location(BMC-MPAPL) has been proposed. A substantial collection of diverse human back images, along with skeletal key point positioning techniques, kernel density estimation, and the maximum interclass variance approach, was used to investigate the statistical distribution and effective categorization of back morphologies. To counteract the restrictions imposed by clothing on positioning, a dataset encompassing key back points and the Dazhui acupoint was developed based on classification outcomes, and the automatic positioning of the Dazhui acupoint was accomplished through a deep learning model of the multi-layer perceptron network. Utilizing the Dazhui acupoint's positioning results, a human coordinate system was established, and the automatic positioning of 60 common back acupoints was achieved with the integration of ancient Chinese bone measurement methods. The results show that the Dazhui acupoint positioning model, tailored to various back morphologies, has realized high-precision positioning, with an average accuracy of 94.87% at an allowable error of 20 mm, marking a 13.37% enhancement over models without back classification. For other common back acupoints, the positioning accuracy stands at 91.58% within an allowable error of 20 mm, irrespective of patient attire or background constraints. It is concluded that the method presented effectively enhances the accuracy and applicability of acupoint automatic positioning.

The complex diagenetic facies of the tight sandstone reservoir in the Shaximiao Formation, located in the Jinqiu gas field to Tianfu gas area in the central Sichuan region, pose significant challenges to reservoir evaluation and natural gas exploration and development. Traditional diagenetic facies identification methods are often low in accuracy, heavily reliant on specialized personnel, and time-consuming. There is an urgent need for a diagenetic facies identification method that is highly accurate, cost-effective, and fast. Firstly, based on cast thin section identification data, the lithology of the tight sandstone was determined using a ternary plot of components. Image processing techniques were then used to identify the types and proportions of pores and cements, and the diagenetic facies of the tight sandstone were classified. Secondly, the corresponding 1 019 depth-based well log data for core-divided diagenetic facies were analyzed in terms of distribution range, median, uniformity, and skewness. These 6 types of well log data were standardized to a 0-1 range, and data imbalance was addressed using synthetic minority over-sampling technique (SMOTE). Finally, 10 traditional machine learning algorithms and ensemble learning algorithms were selected for model training and performance comparison. The study found that ensemble learning algorithms, especially the extreme randomized trees (ET) algorithm, performs best in diagenetic facies identification, achieving higher accuracy and F₁ scores than traditional machine learning algorithms. This significantly improved identification accuracy and stability. The ET model was then used to predict the diagenetic facies of the JQ8 well, validating the feasibility of the method. This study provides effective technical methods and references for diagenetic facies research in tight sandstones.

Image segmentation is a fundamental problem in medical image analysis, the typical deep learning based UNet architecture (UNet) and its variants are widely used in retinal vessel segmentation. However, the UNet network extracts feature information from images through local convolution modules, which makes the global information of the images difficult to be correlated and the long-distance dependencies between pixels difficult to be effectively captured. Considering the problems with the UNet network model and the characteristics of retinal vascular images, an attention module was added to the skip connections of UNet to capture long-distance dependencies between blood vessels. In addition, to enhance the segmentation ability of the network, the group normalization(GN) was used instead of the original batch normalization (BN) of the UNet network model, and the corresponding groups were selected for different channels. To update parameters and optimize the network, the final cross entropy loss function was designed using the side output layer and the final output layer. Experiments are implemented on the DRIVE dataset and CHASEDB1 dataset, and the experimental results show that the proposed model has better image segmentation performance.

The decomposition of submerged plants such as Potamogeton crispus releases a large amount of nutrients into the water body, which has a negative impact on aquatic ecosystems. To investigate the slowing effect of filter feeding benthic animal, the Hyriopsis cumingii, on the deterioration of water quality after the decomposition of submerged plants, a 45 day experimental chamber simulation experiment was conducted from May to July 2023 using different specifications and densities of clams and seagrass combinations to monitor changes in water quality indicators and phytoplankton community structure. It was found that filtration through the Hyriopsis cumingii can reduce the total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and chlorophyll a (CHLA) in the water to a certain extent. To investigate the density effect and size effect of its Hyriopsis cumingii, low, medium, and high-density triangular sail clams were released. The experimental results show that different sizes and densities of Hyriopsis cumingii can significantly control the biomass of phytoplankton, and Hyriopsis cumingii have a significant impact on the community structure of phytoplankton. Among them, releasing small-sized (shell length 4 cm) low-density (1 g/L) Hyriopsis cumingii has the best effect on improving water quality and controlling phytoplankton biomass.