A low-carbon optimal operation model of an integrated energy system that takes demand response and double-layer power-to-gas conversion into account was proposed to increase system energy utilization and lower carbon emissions. Firstly, the optimization model of dual-layer electric-gas multi-energy complementary integrated energy system with high efficiency of hydrogen was established to study the advantages of hydrogen energy in many aspects. Secondly, the demand response model was modeled, which was divided into price type and alternative type according to the characteristics of flexible load. Thirdly, a stepped carbon trading mechanism was introduced to curb the carbon emissions of the system. Finally, taking Nanning Jiangnan industrial park as an example, the model was solved in CPLEX environment of MATLAB, and verified by scene comparison analysis. The results show that the model can fully mobilize the demand side to participate in the system optimization and achieve the effect of energy saving and emission reduction.

Qiandongnan is the largest and best-preserved Miao settlement area in China, holding significant ethnic cultural heritage. Traditional settlements form an essential part of this heritage. Studying their spatial characteristics and influencing factors is crucial for the sustainable development and protection of cultural heritage in this region. By comprehensively utilizing ArcGIS spatial analysis, boundary morphology index, spatial syntax, and geographic detector methods, the spatial characteristics of settlements were deconstructed from the perspective of regional pattern and case feature analysis, and their influencing factors were explored. The results indicate that the spatial distribution of traditional Miao settlements in Qiandongnan is characterized by significant agglomeration and hierarchy. The highest nuclear density is at the intersection of Leishan, Taijiang and Jianhe. The overall spatial pattern shows a “dense in the southwest and central-south, sparse in the northeast” distribution. Constrained by natural geography, the settlements are mainly distributed in the Qingshui River and Duliu River valleys at altitudes of 500~1 000 m, with undulations of 10~20 m, gradients of 2°~5°, and sunny slopes of 90°~270°. The settlements’ spatial structure exhibits a “clustered” distribution with finger-like external boundaries, and the center shows differentiated traffic flow within and at the edges of the settlements. The geodetector study reveals that Miao traditional settlements are regional spatial carriers of a natural-economic-social complex system. The natural geographic environment fundamentally shapes spatial patterns, the social environment guides and controls internal spatial organization and evolution, and economic development decisively influences spatial development and protection. The study enhances the understanding of this complexity, which is vital for appreciating Miao culture, developing strategies for protecting and developing this cultural heritage, and implementing rural revitalization strategies.

Accurate prediction of wind speed along high-speed rail lines is a fundamental requirement for railway disaster warning systems. To enhance the capability to respond to and handle sudden events caused by strong winds, a short-term wind speed prediction method based on the subtraction average based optimizer (SABO) algorithm optimized long short-term memory (LSTM) neural network was proposed. Firstly, considering the nonlinearity and non-stationarity of wind speed, the min-max (MM) method was used to normalize the wind speed data. Secondly, the “-v” method in the SABO algorithm was employed to search and optimize the key parameters of the LSTM model, constructing a wind speed prediction model. Finally, the effectiveness of the model was tested using measured wind speed data collected from wind speed collection points along the Baoji-Lanzhou high-speed railway in China. Experimental results show that the SABO algorithm’s optimization effect is better, and the prediction accuracy is higher. The average absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the constructed model are 11.96%, 1.23%, and 16.47%, respectively, with a coefficient of determination (R²) of 0.995. Compared to other models, the LSTM neural network optimized by the SABO algorithm exhibits better fitting performance and higher prediction accuracy in short-term wind speed prediction, providing a new method and approach for wind prediction and warning along high-speed railway.

Reclaimed asphalt pavement (RAP) has been utilized in numerous road projects. However, considering safety and service life, further in-depth investigation is required into the road performance of high dosage RAP hot-mix/warm-mix recycled asphalt mixtures under traffic loads. To investigate this, indoor tests and mathematical analysis were employed to examine the impact of design parameters, including the waste cooking oil (WCO) content, RAP dosage, and mixing method, on the road performance of recycled asphalt mixtures. The results indicate that in most cases, the VMA of R-100RAP mixes exceeds 14%, and the VV exceeds 3%. Moreover, both the VV and VMA of asphalt mixtures with 60% RAP content, 10% WCO, and 1.0% asphalt fall within specified ranges. Additionally, the split strength ratios of the majority of R-100RAP, R-80RAP, W-80RAP, and R-60RAP mixes surpass 80%, indicating excellent resistance to water damage. The addition of WCO proves beneficial in enhancing the water damage resistance of recycled asphalt mixtures. Although the rutting resistance of the W-80RAP asphalt mixture was inferior to that of the recycled asphalt mixtures containing R-80RAP and R-60RAP, all three exhibited commendable rutting resistance. When comparing the performance of W-80RAP and R-60RAP, it is observed that W-80RAP exhibits a smaller phase angle and a larger stiffness value, indicating better aging resistance. In order to maximise performance and facilitate the effective building of high dosage RAP hot-mix/warm-mix recycled asphalt mixes, the research provides a theoretical framework.

The utilization of offshore floating photovoltaic has became a hotspot in major coastal countries in complex marine environment. Offshore floating photovoltaic is currently in the phase of the experimental and pilot test. In order to explore the main components and current technological status of offshore floating photovoltaic technology, and to assist in the large-scale and commercial development of offshore floating photovoltaic in China, by sorting out the main components of offshore floating photovoltaic, based on the research and application status of offshore floating photovoltaic at home and abroad, focusing on the structural types of floating platforms, and analyzing the characteristics of various types of floating platforms. The results reveal that the maturity level of zero-gap and non-zero-gap floating platform concepts is not high, and the novel body materials should be considered to reduce engineering costs. The electrical equipment that is suitable for the complex marine environment needs to be further improved. The business modes of integrating floating solar power with other marine scenarios should be actively developed. The strategy, security, and means of operation and maintenance need to be given special attention. The research aims to provide the current status and challenges of key technologies, and offer reference experience and research directions for the development and design of offshore floating photovoltaic.

The layout of the distribution path of urban logistics terminals is the key to controlling transportation costs. In order to solve the path planning problem of urban low-carbon logistics, a brainstorming-adaptive large neighborhood search algorithm (BSO_ALNS) was proposed. Firstly, a low-carbon vehicle path model with capacity and time window constraints based on vehicle fuel consumption (CVRPTW) was established with the optimization goal of minimum total vehicle transportation cost. Secondly, the brainstorming algorithm (BSO) was used to improve the quality of the initial solution, and the heuristic crossover strategy was introduced to improve the quality of the global search. Using the adaptive large neighborhood search (ALNS) local search, ten kinds of damage and repair operators were designed, and the adaptive weighting mechanism was introduced, combined with the simulated annealing criterion to avoid falling into the local optimum. The performance of the BSO_ALNS algorithm was tested by selecting C, R, CR and other types of instances of different scales in Solomon. Taking the shortest path distance as the goal, the error between the BSO_ALNS algorithm solution and the historical optimal solution is within 1.5%. With the goal of minimizing the total cost of vehicle transportation, the optimal solution is obtained BSO_ALNS compared with BSO and ALNS. It is proved that the proposed algorithm can effectively solve the problem of urban low-carbon logistics path optimization.

Most of Triassic strata in Guizhou are distributed with bedding rock landslides with weak interbeds, which are characterized by strong destructiveness, complex slip mechanism and difficult treatment. Taking the West Second Ring landslide in Guiyang City as an example, the disaster mechanism and instability evolution process were explored by means of field geological investigation, theoretical analysis and discrete element numerical simulation. The results show that the West Second Ring landslide is mainly composed of dolomitic layered cataclastic rock mass with good permeability and water retention, showing a “sponge” structure, and continuous rainfall is the direct cause of the landslide. The landslide is first cut out by the front sliding body and gradually pulled upward, and then the tensile cracks at the trailing edge of the sliding body developed further, which led to slip-tensile crack failure under the action of gravity. The simulation results are basically consistent with the actual situation. The landslide in West Second Ring Road can be divided into four stages of deformation and instability: natural dissolution and cracking, excavation and unloading expansion, saturated and weakened shear, and slow sliding and accumulation. The conclusions have certain reference significance for the disaster mechanism analysis and engineering prevention of Triassic bedding rock landslide with weak interlayer in Guizhou.

The geological conditions of fault-controlled carbonate volatile oil reservoirs in Shunbei oilfield and the relationship between production wells are complex, and conventional methods have poor applicability in calculating dynamic reserves. Considering that the reservoir has the characteristics of fracture and cavity development, multi-phase seepage and inter-well interference, a volatile oil reservoir pseudo-pressure function was proposed and the functional relationship between saturation and pressure was given. The multi-phase flow material balance well group dynamics of the volatile oil reservoir material balance theory were established. The proposed method utilizes bottomhole flow pressure to calculate dynamic reserves, while there is no need for static pressure testing in the well group. The results show that the multiphase flow material balance equation describes the linear relationship between oil production rate and cumulative production. A partial correlation analysis was conducted on the main controlling factors of the well group’s dynamic reserves, and it was concluded that the main factors affecting the production dynamic characteristics are average oil production, production decline rate and formation energy. Quantitatively evaluate the impact of errors in important parameters of the formation and fluid on the calculation results of dynamic reserves. It is believed that the compression coefficient and porosity of the formation have a great influence on the accuracy of dynamic reserve calculations. The well group dynamic reserve calculation method was applied to a typical well group in the Shunbei oilfield, and the dynamic reserve decrease in the calculation results of a single well was compared to quantify the decrease in dynamic reserves in the Shunbei oilfield due to inter-well interference. The method can accurately calculate the dynamic reserves of well groups in the Shunbei fault-controlled volatile oil reservoirs.

For gearboxes, variations in fault characteristics under different operating conditions, susceptibility to noise interference in fault diagnosis, lead to poor generalization and low recognition accuracy of fault diagnosis models. An end-to-end convolutional block attention module-sparse temporal convolutional network with soft thresholding(CBAM-STCN) was proposed for gearbox fault diagnosis. Firstly, the Hilbert transform was employed to convert the gear fault vibration signal into an envelope spectrum signal. Then, this signal was input into the CBAM-STCN fault diagnosis model. The model integrates a hybrid attention mechanism module, the convolutional block attention module (CBAM), which adaptively learns the weights of channel and spatial attention to extract information sensitive to fault features. The embedded soft thresholding function minimizes the discrepancy between the model’s output and the original input. Finally, the proposed method was utilized to identify and classify various types of gear faults under two different conditions. The results indicate that the CBAM-STCN model achieves an average accuracy of 98.95% in intelligent gear fault diagnosis, demonstrating its potential value for gearbox fault diagnosis.

In order to solve the problem of automatic sorting of garbage, an intelligent sorting method for household garbage was designed based on the artificial intelligence computing platform Jetson NANO and YOLOv8 algorithm. Firstly, the lightweight YOLOv8 multi-target detection algorithm was used to classify garbage into four categories and achieve accurate recognition in the presence of occlusion. Secondly, the CoreXY coordinate transformation algorithm was used to determine the optimal angle for mechanical claw grasping and achieve precise garbage grasping. Finally, based on TensorRT optimization technology, the lightweight YOLOv8 multi-objective detection algorithm was deployed on the NANO hardware platform, completing the overall construction of the four major categories of garbage intelligent sorting system based on YOLOv8-NANO. The results show that in the self-made garbage test consisting of four major categories and a total of 13 sub categories, the sorting system can effectively achieve detection of single and multiple targets. The detection mAP0.5:0.95 value is 97.3%, and the target detection speed reaches 30.6 frames/s. The fastest garbage sorting speed could reach 6 pieces/min.