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.

As large-scale fracturing in the development of deep shale gas results in rapid production decline, the accurate understanding of gas-liquid flow patterns is considered essential for stabilizing gas well production. Gas well models with two different wellbore trajectory structures were established, and OLGA software was applied to conduct transient calculations on models with varying tubing depths. The results indicate that in deep shale gas well A₁, slug flow only occurs in the build-up section and above, while in well B₁, slug flow appears in the horizontal section and near the build-up section. Considering cumulative gas production and liquid loading, the optimal tubing depth for deep shale gas wells A₁ and B₁ is at the heel of the horizontal section, while for conventional shale gas wells A and B, the optimal tubing depths are at the heel of the horizontal section and one-third of the horizontal section. Deep shale gas wells are more favorable for drainage and production compared to conventional shale gas wells.The optimal tubing setting depths for conventional shale gas wells with two deep formation wellbore configurations are at the horizontal section heel and the one-third point of the lateral, respectively. It is determined that deep shale gas wells are more advantageous for drainage and production compared to conventional shale gas wells. As the tubing size decreases, both the gas production and the corresponding critical gas flow rate for liquid carryover are reduced. It is also found that the greater the light hydrocarbon content in the shale gas composition, the higher the gas production. This study is intended to provide a reference for determining the rational tubing placement in the drainage and gas production processes of deep shale gas wells.

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.

In dense scenes, the frequent occurrence of occluded or small-scale pedestrian objects poses significant challenges to traditional object detection models, frequently leading to a high number of missed detections and false positives. In order to solve the problem of high false negative rate and false positive rate in pedestrian detection in such dense scenes, a novel dense scene pedestrian detection framework called ST-YOLO was proposed. Firstly, the low-level small object detection layer in YOLOv5's backbone network was integrated into the feature pyramid network and path aggregation network structure, adding a pedestrian detection layer for detecting small objects. Secondly, the neck network of YOLOv5 was improved by utilizing multi-scale global information based on Swin Transformer and local information extracted by convolutional neural networks (CNN) to construct aggregated features and enhance the network's feature extraction capability. And the SIoU (scalable IoU) loss function was introduced in the prediction process to accelerate the convergence speed of the model and improve detection capability. Finally, Soft NMS (soft non maximum suppression) was used instead of the original non maximum suppression (NMS) algorithm to reduce the problem of mistakenly deleting detection boxes during the non maximum suppression stage and lower the false alarm rate of the detection algorithm. A large number of experiments on the Wide Person dataset have shown that the improved ST-YOLO algorithm has improved accuracy and mAP_0.5 by 5.7% and 3.6% respectively compared to the current mainstream YOLOv9 algorithm.

In order to alleviate the collision risk of non-intersecting runways simultaneous operation, it is necessary to apply a control strategy conforming to the operation characteristics. A collision risk assessment model was constructed by using event tree analysis and Monte Carlo method. Based on event tree analysis, the event to be solved was determined. The probability of related events was calculated by Monte Carlo method. By statistical and fitting the collision event data obtained by the experiment, the safety target level was standardized and the control strategy was put forward. Finally, taking the approach of 01L and the departure of 29R on the non-intersection runways of Daxing Airport as an example, the departure shielding window (0.41~7.39 km) was obtained. Using this strategy, the risk of aircraft collision can be controlled at an acceptable level. The proposed computational model of departure shielding window is of general applicability to the formulation of safe operation control strategies for non-intersecting converging runways.

The identification of aviation accident risk factors through the system-theoretic process analysis(STPA) method is a qualitative analysis process that does not allow for a quantitative assessment of the extent to which each factor contributes to an accident. To address the above problem, a qualitative and quantitative analysis method combining STPA and Bayesian network (BN) was proposed. Taking the JetBlue A320 aircraft flap accident as an example, the control structure model of the flap control system was constructed by STPA method, and the potential unsafe control behaviors and related causal scenarios were analyzed comprehensively. Then, the results of STPA qualitative analysis were transformed into a Bayesian network model that could be quantitatively analyzed, so as to identify the internal interaction logic and the highly influential factors in the accident, and put forward comprehensive safety recommendations. The analysis results show that the main factor leading to the accident is the failure of hydraulic source, while the failure of power transmission unit (PTU) and the leakage of hydraulic line are the main causes of hydraulic source failure, with a critical importance of 0.688 and 0.299, respectively.

A large amount of dredged silt is produced in Taihu Lake every year. In order to realize the resource utilization of solid waste, the feasibility of preparing dredged mud and attapulgite into vertical cutoff wall material was explored, and its impermeability was studied. The permeability resistance and micro-pore structure of dredged mud-attapulgite engineering cutoff wall materials were studied by flexible wall infiltration, water centrifugation and low field nuclear magnetic resonance test. The results show that with the increase of consolidation pressure, the proportion of small holes increases, the proportion of mesoporous holes decreases, the content of free water decreases, and the content of bound water is basically unchanged, and the porosity and hydraulic conductivity of dredged silt-attapulgite decrease gradually with the increase of consolidation pressure. At the consolidation pressure of 100 kPa, the porosity and hydraulic conductivity of the dredging silt-attapulgite cutoff wall material using zinc chloride and butyric acid as the contaminated permeate increase compared with the test using water as the permeate. The reason is that the addition of the polluted permeate reduces the small hole proportion, increases the mesoporous proportion, increases the free water content, and basically keeps the bound water content unchanged.

In order to effectively evaluate and control the operational risks of unmanned aerial vehicle(UAV), based on the summary of various risk factors of UAV ground impact, the possible causes of UAV ground impact were analyzed, corresponding control measures were determined, and a safety barrier model combining risk analysis and control technology was established. It can clearly display the logical relationship between the causes of UAV operation safety, mitigation measures and accident consequences. Bow-tie (BT) model was mapped to Bayesian network (BN), each element of BT model was quantified, and the probability of unsafe events was calculated. The results show that the model can clearly show the risk control process and effectively reduce the operational risk of UAV. It provides an efficient and practical method for the operational risk assessment and control of UAV.

In order to solve the safety problems encountered when aircraft land and taxi on runway surfaces with accumulated water, three-dimensional random uneven half-runway surfaces with different flatness grades were established. The distribution of accumulated water in the landing strip under the influence of runway unevenness was used to investigate the accumulated water distribution characteristic matrix. Moreover, a theoretical model for aircraft landing and taxiing on runways with accumulated water was established, and dynamic simulations were carried out using the Simulink tool to analyze the impact of runways on aircraft landing performance under different accumulated water distribution conditions. The results show that compared with “good” (international roughness index, IRI=1), the landing distance of the runway with “poor” (IRI=5) increases by about 29 m, and the landing distance of the runway with “poor” (IRI=1) increases by 3.7% compared with the ideal smooth road. Moreover, the decrease of road smoothness would aggravate the risk of tire water skiing. If the ground speed of the aircraft is increased from 62 m/s to 82 m/s, the landing distance will directly increase by about 520 m, an increase of about 87%. When the rainfall intensity of 1 mm/min increases from 1 mm/min to 3 mm/min, the landing distance increases by 6.5 m and 7.4 m for each increase in rainfall intensity of 1 mm/min. It is concluded that at the uneven position of the pavement, the greater the grounding speed, the greater the speed when reaching the same position, and the greater the reduction of the adhesion coefficient of the position, up to 11.3%. With the increase of rainfall intensity, the adhesion coefficient decreases gradually. When the rainfall intensity reaches 3 mm/min, the adhesion coefficient decreases by about 20% compared with the dry pavement.

The evaluation of expressway network resilience has been emphasized due to significant global emergencies. Utilizing complex network theory and the resilience triangle model, a dynamic system was developed to assess the comprehensive performance of nodes, incorporating local, global, and social attributes. A method for evaluating the resilience of expressway networks was proposed, consisting of four stages: initial, disruption, recovery, and stabilization. Six disruption and recovery strategies (node degree, eigenvector, betweenness, accessibility, social attributes, and random selection) were applied to analyze the network’s performance using three key indicators: the number of independent paths, network efficiency, and network connectivity. A topological map of the expressway network spanning the provinces of Shanxi, Shandong, Henan, and Hebei were constructed, and the resilience changes under various disruption and recovery strategies were analyzed. The findings indicate that in the initial stage, the expressway network exhibits a relatively high number of independent paths, suggesting robust anti-risk capabilities. During the disruption stage, network efficiency, the number of independent paths, and network connectivity decrease by 94.32%, 98.18%, and 99.63%, respectively, demonstrating the network’s ability to absorb disruptions. In the recovery stage, the accessibility restoration strategy, which results in the smallest resilience triangle area, exhibits the strongest resilience, whereas the random restoration strategy shows the slowest recovery rate, indicating that it should be avoided whenever possible. In the stabilization stage, network efficiency resilience is found to be superior to that of network connectivity and independent path resilience in the expressway network of the four provinces. It is recommended that urban nodes with higher degree values, such as Xinxiang and Puyang, be prioritized for protection to enhance the overall resilience of the expressway network.