To enhance the efficiency of carbon monoxide (CO) emission control during drill-and-blast construction in high-altitude tunnels and to improve the working environment for personnel, a tunnel currently under construction at a high altitude was investigated. Utilizing the computational fluid dynamics simulation software Fluent, three factors were examined under forced ventilation conditions: the distance between the air duct and the tunnel face, the position of the air duct, and the varying elevations. To model the diffusion characteristics of harmful CO gases. The simulation results indicate that when the air duct is positioned too close to the tunnel face, vortices form, causing CO accumulation near the tunnel face, which is detrimental to the effective dispersal of CO from this area. Conversely, when the duct is positioned too far, the airflow loses a significant amount of kinetic energy before reaching the tunnel face, which also hinders the effective removal of harmful CO gases in the vicinity of the tunnel face. Optimal removal efficiency of harmful CO gases near the tunnel face is achieved when the air duct is placed at a distance of 25.2 m from the tunnel face and located at the top of the tunnel. This configuration significantly improves the working environment for personnel within a short period. Compared to plain regions, the distance between the duct and the tunnel face in high-altitude areas should be approximately 3 \sqrt{S} (S is the cross-sectional area of the tunnel) that of the plain regions. The trend of CO movement within the tunnel is generally consistent across different altitudes. As the altitude increases, the concentration of CO also increases, and the speed of CO movement within the tunnel decreases, necessitating longer ventilation times in high-altitude areas.

To investigate the contribution and temporal variability of lake-groundwater interactions in the water balance of flood-controlled lakes, Honghu Lake in the middle reaches of the Yangtze River was selected as a case study. Based on the analysis of water level dynamics of Honghu Lake and surrounding groundwater, the recharge-discharge relationship between Honghu Lake and groundwater were identified and calculated by a water balance equation. The contribution of groundwater-lake interactions to the water balance of Honghu Lake and its temporal variability were examined. The results indicate that there is a significant water level difference between Honghu Lake and the adjacent phreatic groundwater, with dynamic changes in both showing a significant positive correlation, correlation coefficient r=0.729. The aquifer in the Honghu Lake area is permeable, which indicates that there are interaction conditions between Honghu Lake and groundwater. On an inter-annual scale from 2017 to 2022, Honghu Lake generally infiltrate into the groundwater, with an average annual discharge volume of 6.43×10⁸ m³, accounting for 14.11% of the lake's outflow. On a multi-year average monthly scale from 2017 to 2022, Honghu Lake infiltrates into the groundwater during the dry season (September to February of the following year), with an average monthly discharge volume of 0.8×10⁸ m³. During the rainy season (April to August), groundwater-lake interactions are dynamic, with an average monthly exchange volume of -1.17×10⁸~0.88×10⁸ m³. Honghu Lake presents an opposite seasonal variation characteristic of the lake water-groundwater interaction compared with the reported Yangtze-connected lakes such as Poyang Lake and Dongting Lake. This is mainly because the water level of Honghu Lake, affected by water conservancy regulation, has altered the natural interaction process between the lake and groundwater. These findings provide new insights into groundwater-lake interactions in flood-controlled lake systems and hold significance for the management of water resources and ecological protection in the Honghu Lake region.

Lithium slag holds significant potential for recycling and reuse. The barriers in the recycling process were addressed, such as surface protectants and surface by-products (diffusion pump oil and lithium hydroxide), that impeded efficiency, and the risk of uncontrolled reactions leading to explosions. A method of using water jet impact to desorb surface protectants and by-products from lithium slag and prevent reactive explosions was proposed. Based on the binding relationship between the lithium slag, surface protectants, and lithium hydroxide by-products, a bridging model for oil-lithium-hydroxide lithium particles was proposed. Subsequently, a water jet computational fluid dynamics-discrete element method (CFD-DEM) triple-component coupled depolymerization action model was proposed to explore the depolymerization characteristics of the oil-lithium-hydroxide lithium bridging model under different water jet pressures. The results show that the destruction time of particle adhesive bonds in the oil-lithium-hydroxide lithium model is inversely proportional to the jet pressure. At a jet pressure of 0.1 MPa, a particle adhesive bond destruction rate of over 95% can be achieved within 0.05 s. When the jet pressure exceeds 0.5 MPa, the time to reach a 95% bond destruction rate is just 0.015 s. This method effectively removes the surface protectant of the lithium slag and promptly eliminates lithium hydroxide and the foam it forms while ensuring safety, efficiency, and continuous digestion operations. These findings provide significant guidance for the application of water jet technology in the recovery of reactive metals and can be specifically applied to the high-efficiency, controlled, and safe recycling field of lithium slag.

During the manufacturing and application of fiber-reinforced composites (FRP), issues such as impact damage and fatigue accumulation cause irreversible subtle damage to the internal structure. Acoustic emission (AE) technology, with its high precision and real-time property, has become an important means to monitor the damage evolution and failure mechanisms of FRP. The applications of acoustic emission technology in the damage characterization of FRP in recent years was reviewed. By conducting research on AE technical means such as parameter analysis, waveform analysis, pattern analysis, and deep-learning analysis, the results showed that parameter analysis and waveform analysis could complement each other in terms of signal characteristics during the detection process, achieving a qualitative description of damage behaviors such as the deformation and fracture of composite structures. Methods such as deep-learning analysis provided important theoretical support for the health monitoring and life prediction of fiber-reinforced composites. Overall, acoustic emission technology can monitor and evaluate the composite structures in operation in real-time. It has great development potential for maintaining the health of FRP materials and preventing sudden failures. In the future, it can be further combined with artificial intelligence technology to improve the accuracy and efficiency of damage identification.

To effectively address the contradiction between the uncertainty of the internal and external environment in the construction industry and the complexity and vulnerability of the construction supply chain, as well as to promote the overall security and stability of the construction supply chain network, and to prevent and mitigate the risk of disruption among node enterprises, a construction supply chain network invulnerability analysis method was proposed based on the complex network theory and cascade failure model. Firstly, from the perspectives of business, resources and information flow, the TOPSIS(technique for order preference by similarity to an ideal solution ) method was used to assess the importance of node enterprises based on multiple complex network centrality indicators. Secondly, combined with the operational characteristics of the construction supply chain, an improved load-capacity-elasticity cascade failure model was established to measure the impact of enterprise disruption from the perspective of network loss under intentional attack, and to analyze and explore the network invulnerability improvement strategy from the perspectives of node capacity, load, and resilience. After numerical simulation and analysis, the results show these as follows. When the upstream node enterprises of the construction supply chain network suffer from the impact of disruption risk, the supply chain network can show strong network invulnerability, but it should focus on the downstream supplier enterprises, so as to avoid the network as a whole suffering from more losses due to the shortage of the supply of construction materials or basic services. To keep the small difference in the business capacity among node enterprises, the large difference in the business load and an appropriate high level of risk remediation cost investment can effectively reduce the loss of the supply chain network when the node enterprises are interrupted, thus improving the level of network invulnerability. Among the multiple types of strategies, the node capacity strategy is better than the node resilience strategy and the node load strategy in order to improve the network invulnerability. The results of the study can provide scientific references for improving the security level of construction supply chain and proposing disruption risk management strategies.

To design and prepare high-quality one-dimensional hexagonal quasicrystal nano-composites, the interface and interface phase models were applied to study the infinite one-dimensional hexagonal quasicrystal anti-plane fracture problem with cylindrical inclusions containing nano coatings by using the complex function method and Gurtin-Murdoch's surface/interface elasticity theory. Under two different models, the series form expressions of phonon and phason field stress fields in matrix, coating and inclusion were obtained, respectively. Numerical examples were used to analyze the effects of interface elastic constants and size effects on the stress field around inclusions. The results showed that the positive or negative values of interface elastic constants would affect the stress field around nano-inclusions. As the size of nano-inclusions increased, the stress field exhibited significant size dependence, and surface effects had significant differences in their effects on the stress fields of dimensionless phonon and phason fields. The relevant results provide a certain theoretical reference for studying the mechanical behavior of quasicrystalline nano-inclusions.

Corn is one of the important grain reserve crops in China, and its yield directly impacts national food security. The chlorophyll content of corn is closely related to its photosynthetic capacity and significantly affects the photosynthetic rate of the leaves and vegetation productivity. It is an important crop parameter for monitoring crop growth, pest and disease surveillance, and maturity prediction. Real-time and accurate monitoring is of great significance for corn parameters and yield prediction. This study was conducted in the typical black soil area of Lishu County, Siping City, Jilin Province. To solve the problem of missing effective images that may occur during the revisit period of Sentinel-2 satellites, a method for retrieving corn leaf chlorophyll based on the fusion data of Sentinel-2 and MODIS images was proposed. Using fused imagery, three machine learning algorithms were employed: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBOOST) to construct a model for estimating corn leaf chlorophyll content, and the accuracy of the model was verified. The conclusions obtained were as follows. The data simulated using the ESTARFM data fusion algorithm maintained a high correlation with the real imagery. Among the leaf chlorophyll inversion models for missing image dates, where input variables included fused image band reflectance and vegetation index, the XGBOOST model showed good fitting accuracy The research demonstrates that accurate estimation of leaf chlorophyll content can be achieved even on days with missing imagery, when fusion image feature bands are integrated with machine learning algorithms. This notably improves the temporal precision of corn chlorophyll content measurement, presenting a novel method for daily or large-scale inversion studies of leaf chlorophyll content, particularly in scenarios involving image gaps. Furthermore, it illuminates the potential for refined monitoring of physiological and biochemical parameters across a wider range of crops, with shortened time intervals.

Geothermal tail water reinjection is the main bottleneck that restricts the development and utilization of geothermal resources in Lanzhou Basin. In order to break through the technological gap of geothermal tail water reinjection in Lanzhou Basin sandstone-type thermal storage, relying on the geothermal heating demonstration project in Pengjiaping, Lanzhou City, for the first time, the natural reinjection experiment with graded flow rates of 15, 20, 25, 28 m³/h and graded pressure pressurised reinjection experiment of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 MPa were designed and carried out, the maximum stable natural reinjection volume of 27.96 m³/h and the stable reinjection volume of 51.68 m³/h for 0.667 MPa pressure were firstly obtained from the sandstone-type thermal storage of Pengjiaping, Lanzhou Basin, and the impact of geothermal tail water reinjection on the water level, water temperature, and temperature field of the extraction wells was also investigated. After a heating season of productive reinjection experiment verification, a set of suitable and feasible sandstone-type thermal storage geothermal tail water reinjection technology process has been successfully explored in Lanzhou Basin, which is of great reference and significance for the large-scale and high-quality development and utilization of geothermal resources in Lanzhou Basin.

In order to find out the susceptibility of debris flow after fire at different time points, the burned land where a serious fire occurred in Lushan Mountain, Changshou Township in March 2020 was selected as a demonstration research area. Based on the idea of “space for time”, the whole study was carried out. Through laboratory experiments, the root soil mechanical parameters of the study area at different time after fire were obtained. By using the experimental parameters obtained, the slope instability coefficient of different years after fire was obtained through the slope instability model. According to the slope stability division standard, the slope instability area of different years after fire was obtained. Finally, the source strength indexes of different years after fire were extracted. The dynamic evaluation index system of post-fire debris flow susceptibility at small watershed scale was established by taking source strength as static evaluation index and topographic and geomorphic index as static evaluation index. Using the entropy weight method to calculate the weight of index factors combined with the comprehensive index method, the dynamic susceptibility assessment of debris flow was carried out on the burned land of Lushan Mountain in Changshou Township. Based on the results, targeted remediation of watersheds that remain highly susceptible for many years after a fire and those that are highly susceptible within a short period of time can effectively prevent and reduce the probability of mud slides, while also saving economic costs and achieving truly effective disaster prevention and mitigation.

The oscillating water column buoy utilizes wave energy by channelling waves into an air chamber, inducing oscillations within the water column. However, wave impact also causes buoy oscillation and rocking, which reduces the relative water column displacement. To address this, a double-layer concave damping plate with a weight-enhancing ring was implemented to stabilize buoy movement. A double-concave damping plate with a weight-enhancing ring was used to inhibit the movement of the floating buoy and increase the pressure of the air chamber. Based on the small amplitude wave theory and Newton's second law, a theoretical model was developed to analyze wave energy capture by the damping plate-enhanced oscillating water column buoy, calculating buoy oscillations and air chamber pressure characteristics. The finite element simulations, conducted using AQWA software, replicated the wave-induced hydrodynamic effects on the buoy. Air chamber pressure was simulated via Fluent software's fluid volume method and open channel wave-making method, and the model's accuracy was validated against theoretical calculations. The simulation results show how effective the damping plate is in limiting buoy motion, raising mass and buoy inertia, and improving water column stability in the air chamber. The theoretical calculation of the air chamber air pressure parameters provides a basis for the design of the damping plate oscillating water column buoy wave energy harvesting system and the green low-carbon energy conversion structure.