In order to obtain the location of erosion gullies in Bayan County, understand the spatial relations between multi-features and erosion gullies, assessing the occurrence risk, and provide information and method reference to erosion gully management and precaution, high-resolution satellite imagery, digital elevation model, soil, and precipitation data were used to acquire erosion gully locations and topographical, soil, hydrological, meteorological features. Subsequently, the spatial relations between erosion gullies and multi-feature were analyzed, and erosion gully occurrence risk was assessed using the random forest method. As results, towns like Waxing, Dexiang, and Xinglong could be paid more attention on the erosion gully management. Elevation relief, slope, slope factor, slope length factor, water flow density, and distance to water flow tend to show more spatial relations with erosion gullies, and erosion gully would participate in the redistribution of soil nutrition. On the other hand, for natural or data spatial resolution reasons, slope aspect, terrain curvature, catchment quantity, rainfall erosivity factor, soil bulk density, and soil erodibility factor show little spatial relations with erosion gullies. Assessment indicates that the erosion gully risks are mainly in arable lands with slopes between 1.5°~6°. Results indicates that topographical and hydrological features are closely related to erosion gullies, and diagonal ridge is an economical and effective measure.

Taking a three-level, multi product, and dual channel supply chain as an example, the inventory control problem in the supply chain under stochastic demand was explored. A dual channel supply chain simulation model was established based on the independent control, information sharing, and pre warehouse replenishment model of “single manufacturer-dual distributor-dual retailer-dual customer”. In node enterprises, the Pull/Push strategy was adopted for ordering decisions, and information entropy was used to measure the uncertainty of nodes. Finally, the whale optimization algorithm was used to adjust the inventory control parameters in the simulation model. The results show that in the case of interruption, the pre warehouse replenishment mode can increase customer satisfaction in the interrupted channel by 80%. The whale optimization algorithm can ensure customer satisfaction while controlling total costs and reducing uncertainty in the supply chain system.

To solve the problem of deterioration of concrete performance caused by low temperature in cold regions. Based on the theory of nanomaterials to improve the properties of concrete, the effect of nano-silica on the properties of concrete was studied from the macro and micro scales. The results of the compressive strength test show that the compressive strength of ordinary concrete is attenuated by about 10% under low temperature curing. After being mixed with nano silica, the compressive strength of concrete is increased by about 20%, and the optimal dosage is 2%. The improvement mechanism of nano-silica on concrete properties was explored through microscopic test data such as mercury intrusion, X-ray diffraction and scanning electron microscopy. The results show that nano-silica can promote cement hydration at room temperature and low temperature curing, consume calcium hydroxide generated by hydration, and produce more hydrated calcium silicate and hydrated calcium aluminate gel, thereby reducing the porosity of concrete, optimizing the microstructure of concrete, and improving the performance of concrete. Compared with the room temperature environment, the improvement effect of nano-silica on concrete at low temperature is slightly reduced, but it completely overcomes the adverse effects of low temperatures on the performance of ordinary concrete.

A large number of nonlinear components are used in the AC microgrid of photovoltaic grid-connected system which is equipped with a certain capacity of energy storage devices. When there is a large number of nonlinear loads in the microgrid system, the current waveform of the microgrid system is prone to distortion, resulting in harmonic pollution. In order to reduce the interference of grid-connected microgrid system on the receiving grid, based on the LCL grid-connected inverter structure, PI+ repeated control compound control strategy was adopted to realize the tracking and control of command current on the basis of meeting the tracking speed and accuracy, which can effectively suppress harmonic current and compensate reactive power. Based on the composite control, it is considered that when the photovoltaic output power and load power change, the multi-function grid-connected inverter under the composite control can still realize dual functions when the researched microgrid and the grid transmit different power in different modes. Finally, the simulation results show that the strategy realizes harmonic compensation and reactive power compensation, and transmits power to the grid at the same time.

The issue of comfort in subway stations is typically analyzed using the predicted mean vote (PMV) and the predicted percent dissatisfied (PPD) indices. Based on the PMV-PPD comfort indices calculation model, the weight proportions of different environmental parameters on comfort were studied. The PMV calculation model was improved by considering the spatiotemporal characteristics of passengers’ clothing and activity levels. The PPD calculation model was enhanced by taking into account the impact of drastic environmental temperature changes on comfort. The environmental comfort of public areas in stations was then analyzed using the improved PMV-PPD calculation model. On this basis, the feasibility of predicting environmental comfort using long short-term memory (LSTM) networks was explored. The research results indicate that the weight proportions of metabolic rate, air temperature, clothing thermal resistance, and humidity on environmental comfort are 0.558, 0.260, 0.113, and 0.069, respectively. At a given time, the maximum differences in PMV and PPD at different monitoring points on the platform are approximately 15% and 60%, respectively. The improved PMV-PPD calculation model is found to be more universally applicable compared to the traditional PMV-PPD calculation model. The neural network is shown to accurately predict PMV and PPD values, with a maximum error of 8% for PMV and 14% for PPD between the actual and predicted values.

In order to solve the problem of poor forecasting effect due to the large number of influencing factors of aviation material consumption and small amount of sample data. A prediction model for aircraft spare parts demand based on principal component analysis (PCA), improved particle swarm optimization (IPSO), and least squares support vector machine (LSSVM) was proposed. Firstly, the principal component analysis method was used to screen the main influencing factors of aviation spare parts, and then the improved particle swarm optimization algorithm was used to optimize the least square support vector machine parameter combination, and finally the selection results and optimization parameter combination were used to complete the PCA-IPSO-LSSVM aviation spare parts demand prediction model training. The results show that compared with the other four prediction models, the PCA-IPSO-LSSVM model has the highest prediction accuracy, and the RMSE and MRE of the test set are 3.24 and 4.23%, respectively, indicating that the model has good prediction precision and fitting effect.

In order to address the issues of low accuracy and high missed detection rates in existing pavement crack detection algorithms, an improved pavement crack detection algorithm based on YOLOv8n, named YOLO-CD (YOLO-crack detection), has been proposed. The scale sequence feature fusion (SSFF) module and triple feature encoder (TFE) module from the ASF-YOLO architecture were utilized by the YOLO-CD algorithm to enhance the detection performance for multi-scale cracks and the perception capability of target features. Additionally, the coordinate attention(CA) mechanism was introduced at the end of the backbone network and in the neck network, with positional information embedded into channel attention, thereby strengthening the extraction capability of crack features. Furthermore, an additional P2 small object detection layer was added on top of the original three output layers of YOLOv8n, increasing the multi-scale receptive field of the network, allowing both global and local context information to be captured simultaneously, thereby improving the detection capability for small cracks in complex scenes. The original YOLOv8n detection head was replaced by the DyHead detection head, achieving the integration of scale, spatial, and task attention mechanisms, and further enhancing the network’s detection performance for cracks. Experimental results show that in the self-built PD-Dataset, the mAP50 of the improved YOLO-CD algorithm is increased by 4.1% compared to the original YOLOv8n algorithm. In the public dataset RDD2020, the mAP50 of the improved YOLO-CD algorithm is increased by 1.5% compared to the original YOLOv8n algorithm. Moreover, the algorithm’s detection speed is found to reach 89.9 frames/s, meeting the real-time requirements of pavement crack detection.

With the increasing number and aging of in-service oil and gas pipelines in China, issues such as corrosion, aging, and geometric deformation have become increasingly apparent. Due to the limitations of manual inspections, such as complex spatial environments and narrow pipe diameters, the use of pipeline robots for inspection and maintenance has emerged as a dominant trend in both domestic and international research. To address the risks of pipeline failure after prolonged service, understanding the latest advancements in pipeline robotics is crucial for setting forward-looking development goals and minimizing redundant research efforts. A comprehensive review of recent developments in in-service pipeline robotics was provided, these robots were divides into two main types based on their movement mechanisms and working environments: internal (passive and active) and external pipeline robots. By examining specific examples of each type, their overall performance were compared and highlighted key considerations for field applicability. Furthermore, the future directions were explored for pipeline robotics, emphasizing the importance of multi-parameter integration in ensuring the safe operation of oil and gas pipelines in the future.

At present, China's offshore floating photovoltaic is in its exploratory stage, which ensures the dynamic stability of floating photovoltaic foundation under varying environmental loads becoming a key research priority. In order to more effectively solve the above-mentioned difficulties and the shortage of land resources in the photovoltaic industry, a floating photovoltaic PE floating block foundation structure design, hydrodynamic calculation and optimization were completed through numerical simulation calculation based on the environmental conditions of a sea area in Rushan City, Weihai. The results indicate that wave height, wave period, and wave incidence angle have varying degrees of influence on the motion response and internal force values of the structure. Based on the analysis, practical applications of the new floating foundation in specific projects are guided, showing promising results that verify the feasibility and stability of this foundation structure's application.

Semantic segmentation of remote sensing images plays a crucial role in agriculture production, urban planning, and other fields. However, due to factors like imaging distance, lighting conditions, objects, and environment, there is a problem of semantic ambiguity in remote sensing images, which leads to uncertainty in segmentation. A multi-scale context attention (MSCA) method that combined pyramid pooling with attention mechanisms to better utilize contextual information was proposed for this problem. Additionally, this method significantly reduced the computational complexity and memory usage of attention methods. Experimental results on the ISPRS Potsdam dataset demonstrate that the MSCA method achieves superior segmentation performance for target classification with ambiguous semantic information in remote sensing images while almost not increasing memory consumption and maintaining consistent inference speed.