The reactive power optimization and reconfiguration of traditional distribution network are mostly studied separately, lacking the coordination and cooperation of different optimization techniques. A mathematical model of reactive power and reconfiguration collaborative optimization of active distribution network was established. Combined with the two optimization methods of reactive power optimization and reconfiguration of distribution network, the coordinated operation of the two was realized according to the actual situation of distribution network. Taking the minimum annual comprehensive cost as the objective function, the improved grey wolf algorithm was used to solve the problem under the constraints of network power balance, node voltage amplitude and network radial operation. Aiming at the problems of low population diversity, easy to fall into local optimal solution and slow running speed of traditional grey wolf algorithm, it is proposed to increase the explosion mechanism of fireworks algorithm on the basis of grey wolf update strategy. At the same time, in order to improve the computational efficiency and solution accuracy, the fireworks algorithm was used for integer solution optimization, and the nonlinear programming algorithm was introduced to optimize the continuous solution. The IEEE33 node distribution network was taken as an example to verify four different scenarios. The results show that the proposed collaborative optimization model can effectively reduce the network loss and annual comprehensive cost, suppress the node voltage fluctuation level, and show the superiority of the improved algorithm in convergence speed and calculation accuracy.

Oil-based drilling fluids, favored for their superior stability and inhibitive properties in complex deep oil and gas strata, is constrained by a scarcity of efficient materials for leak prevention and plugging, thereby limiting their utilization. In response to this challenge, a homogenous and stable polymer, SMHDVD, was synthesized via solution polymerization, using acrylate monomers as the primary chain and incorporating functional monomers that offer resistance to high temperatures and enhanced bonding with the formation. The impact of different concentrations (0.1%, 0.3%, and 0.5%) of the crosslinking agent divinylbenzene on the polymer SMHDVD was also investigated. In-house plugging experiments have shown that SMHDVD significantly contributes to the plugging efficacy of oil-based drilling fluid systems, with a maximum reduction in cumulative drilling fluid loss of up to 68.7%, surpassing the performance of conventional plugging materials. It was observed through comparative studies that the incorporation of a crosslinking agent diminishes the sealing capacity of SMHDVD, and an increase in the crosslinking agent's concentration leads to a decline in the polymer's plugging performance. The polymeric plugging agent developed in this research offers a novel perspective for the prevention of oil-based drilling fluid leakage, with promising prospects for practical field application.

To explore the impacts of various factors on the performance of concrete, the response surface methodology was adopted to optimize the concrete mix proportion. In the experiment, the water-binder ratio, the dosage of steel slag, and the content of desert sand were taken as variables, with a focus on analyzing the main performance indicators such as the slump of concrete, compressive strength, and splitting tensile strength. The experimental results indicate that the content of desert sand has the most significant influence on the slump of concrete, while the compressive strength and splitting tensile strength are mainly affected by the variation of the water-binder ratio. With the increase of the content of desert sand, the slump, compressive strength, and splitting tensile strength of concrete exhibit a trend of initially increasing and then decreasing. When the content of desert sand reaches 30%, the performance is optimal. The addition of steel slag can enhance the fluidity of desert sand concrete (DSC). As the amount of steel slag increases, the compressive strength of DSC shows a decreasing trend, and the tensile strength increases initially and then decreases. The addition of steel slag interacts with desert sand, particularly on the tensile strength of DSC. Through response analysis, the optimal mix proportion was obtained as a water-binder ratio of 0.39, a dosage of steel slag of 10%, and a content of desert sand of 30%, at which the comprehensive performance of DSC is the best. Finally, non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) was utilized for multi-objective optimization, which yielded a more complete solution set, thereby providing certain technical support for the application of DSC.

Molecular dynamics method is adopted to investigate the effect of different NaCl solutions concentrations on the bonding properties of the calcium silicate hydrate/γ-FeOOH(C-S-H/γ-FeOOH) interface. The effect mechanism of NaCl solution concentration is revealed from the interface ion evolution, radial distribution function, particle strength distribution, interaction energy and mechanical properties. The results show that as the concentration of NaCl solution increases, interlayer {\mathrm{Ca}}_{\mathrm{h}}^{2+} ions separate from the surface of C-S-H and diffuse to the interlayer solution, Na⁺ ions enter the C-S-H layer. {\mathrm{Ca}}_{\mathrm{h}}^{2+} ions adsorb Cl^- ions in the solution, resulting in the ion clusters of {\mathrm{Ca}}_{\mathrm{h}}^{2+} and Cl^- on the surface of C-S-H. In addition, the γ-FeOOH surface hydroxyl oscillation provides adsorption points for {{\mathrm{Na}}^{+}}^{}ions, resulting in the increase of Na⁺ ions on the γ-FeOOH surface. When the NaCl solution concentration increased, the RDF peak of Ca_h—O_s gradually decreased and the radial distribution function(RDF) peak of Ca_h—O_w, Ca_h—Cl, and Na—O_s gradually increases, consistent with the ionic strength distribution. Where, O_s is the oxygen on the silicon chain in C-S-H, and O_w is the oxygen in the interlayer solution water. {\mathrm{Ca}}_{\mathrm{h}}^{2+} ions form ionic bonds with O_w in water, leading to a reduction of Ca_h—O_s ionic bonds on the C-S-H surface. Since the strength and stability of Ca_h—O_s ionic bond are better than that of Ca_h—O_w,therefore, the C-S-H/γ-FeOOH interfacial interaction energy and peak stress both show a decreasing trend with the increase of NaCl solution concentration.

The terrain in western Sichuan is complex and varied, and the geological structure is active, which makes the construction and maintenance of the traffic trunk line face the challenge of frequent geological disasters. Ensemble learning algorithm can optimize the shortcomings of the algorithm in geological hazard susceptibility assessment and improve the accuracy of the model, which has significant advantages in geological hazard susceptibility assessment. Taking the riverside high-speed as an example, 12 feature variables such as slope and relief were selected to construct the geological hazard susceptibility evaluation system. The forecasting performance of the modeling of the integrated algorithm and a single algorithm was compared and analyzed. The main control factors of the geological disasters along the riverside high-speed were discussed and the practicability of the model was verified. The results show that the proportion of high and extremely high geological hazard prone areas along the Yangtze River high speed is 18.21% and 9.85%, respectively, which are concentrated in the Leibo section and Jinyang section. The area under curve (AUC) of the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve in the integrated model. The AUC of ROC curve (0.84~0.86), the AUC of P-R curve (0.81~0.85) and the F₁ score (0.78~0.79) of the three single machine learning models are significantly higher, and the prediction performance is better than that of a single machine learning algorithm. The development of high-speed geological hazards along the Yangtze River is controlled by topographic and geomorphic factors. The new damage points are located in the highly prone areas of the model, which verifies the accuracy and reliability of the Stacking model.

The application of green NH₃-fuel on board has been widely regarded as a feasible way to realize the green and low-carbon transformation of the global shipping industry. However, the N₂O emission problem of marine NH₃-fuel engines has become one of the key technical bottlenecks hindering the development of ammonia-powered ships. To solve this problem, a series of TiO₂-supported transition metal oxide catalysts were prepared by impregnation method. The effect of transition metal element types on the N₂O removal performance of the catalysts was investigated, and the N₂O removal performance of Cu_x/TiO₂ catalysts was optimized. The results show that compared with Fe₅/TiO₂, Mn₅/TiO₂, Co₅/TiO₂ and Ni₅/TiO₂ catalysts, Cu₅/TiO₂ catalyst shows excellent catalytic activity, the N₂O conversion efficiency can reach 100% at 350 ℃. In addition, Cu₅/TiO₂ catalyst also has good water resistance. The experimental results show that 5% is the best Cu loading amount. X-ray diffraction, N₂ adsorption-desorption, H₂ temperature programmed reduction, O₂ temperature programmed desorption, and in-situ diffuse reflectance infrared Fourier transform spectroscopy were used to characterize the physicochemical properties and surface reaction intermediates of Cu₅/TiO₂ catalyst, and the relevant catalytic reaction mechanisms were discussed in depth from multiple perspectives. The characterization results show that compared with other Cu_x/TiO₂ catalysts, Cu₅/TiO₂ catalyst has higher dispersion of active species, specific surface area, oxygen vacancy content and stronger redox performance, which is conducive to its better catalytic activity. The main active species on the surface of Cu₅/TiO₂ catalyst are Cu²⁺ and Cu⁺ species, and the adsorption and deionization of N₂O is a key step in the catalytic reaction.

The high-altitude & dense-vegetation landslide is difficult to investigate and lack of data, its appearance and subsurface information also difficult to get. Therefore it is difficult to identify and threat the road greatly. A case study of the Xiaojiapo landslide was presented. Detailed on-site geological surveys were performed, and drone-based oblique and orthophoto imaging techniques were employed to get the landslide's characteristics. Historical deformation of the landslide was analyzed using satellite image, airborne LiDAR was used to collect point cloud and digital imagery data of the landslide surface. A digital elevation model and centimeter-level three-dimensional model of the landslide surface were created following vegetation removal to assess the surface characteristics. The combined investigations reveal that the Xiaojiapo landslide is situated at an elevation of 3 030 m with a vegetation coverage reaching up to 90%. It is a typical landslide with high-altitude and dense vegetation. The key internal factors contributing to the landslide including the unique alpine valley topography, the slope structure with advantageous free faces, and easily erodible geological layers. Precipitation induces internal water infiltration, which reduces the strength of the rock and soil mass. Additionally, freeze-thaw cycles further diminish slope strength, while earthquakes and road constructions disturb the internal structure, weakening it further. These combined internal and external factors drive the landslide deformation. This study offers technical insights for the landslide identifying, early-warning, and mitigation of road landslides in high-altitude regions with dense vegetation.

An adaptive predefined-time prescribed performance backstepping fault-tolerant control strategy is presented based on radial basis function (RBF) neural networks, event-trigger mechanism and hysteresis quantizer for the attitude control problem of quadrotor unmanned aerial vehicle (UAV) with actuator faults. Firstly, the dynamic model of the quadrotor UAV system was constructed, and the attitude model was reconstructed by incorporating the actuator fault model. Secondly, by designing a class of time-varying functions, the error variables required for backstepping control were transformed. Thirdly, the nonlinear function approximation capability of RBF neural networks was utilized to estimate derivatives of virtual control laws and the actuator fault with unknown parameters. Finally, to reduce the update frequency of the actuator, a combination of event-trigger mechanism and hysteresis quantizer was used to design the control input. Stability of the closed-loop system was demonstrated through Lyapunov stability theory. The effectiveness of the proposed algorithm was verified through MATLAB. It is concluded that the designed event-triggered quantized controllers have a lower update frequency compared to controllers designed using only event-triggered techniques.

During tunnel construction, the deformation of surrounding rock and the mechanical response of the supporting structures are significantly influenced by the lateral pressure coefficient λ. Accurate determination of the on-site lateral pressure coefficient is essential for guiding tunnel design and construction. Firstly, the impact of the lateral pressure coefficient on settlement displacement of the tunnel vault and horizontal displacement of the side walls was analyzed theoretically. Secondly, the ratio between horizontal displacement of the side walls and settlement displacement of the vault was monitored, and a numerical simulation was employed to establish a mathematical relationship between the horizontal-vertical displacement coefficient K and the lateral pressure coefficient λ, enabling the inversion of the lateral pressure coefficient. Finally, the inverted lateral pressure coefficient was applied to optimize tunnel cross-section design. The results indicate that, under the same geological conditions, an approximately linear relationship exists between K and λ. Regardless of changes in tunnel depth or surrounding rock conditions, a proportional relationship between horizontal and settlement displacements is maintained, which can be used to invert the lateral pressure coefficient at the tunnel site. By adjusting the tunnel axis ratio m to gradually approach λ^-1, deformation is effectively controlled and the proportion of lining damage is reduced.

“Large-difference annulus” is a common characteristic encountered in complex wellbore structures with “varying diameters” during (ultra-)deep well drilling. This characteristic leads to slow drilling fluid velocity in the upper large-diameter annulus, posing challenges for cuttings removal, while the higher velocity in the lower small-diameter annulus results in significant circulating pressure loss. To address these issues, a novel flow diverter tool was designed to carry cuttings in the upper section and the loss of circulating pressure in the lower section. However, the lack of a specific wellbore flow model tailored for this tool in current research has hindered its design and optimization. Based on the fundamental principles of fluid flow and hear transfer, a valid mathematical model was proposed to be compatible with the flow diverter tool. Then, via a case study on Well ZS102, the tool was proved to be effective and perform well in practice. The results show that with the installation of the flow diverter tool, the bottomhole pressure is lowered from 85.08 MPa to 80.30 MPa, the standpipe pressure is significantly reduced from 20.97 MPa to 7.22 MPa, and the annulus pressure loss is decreased from 7.16 MPa to 2.40 MPa. The research presents a novel approach for optimizing cuttings removal parameters and preventing leaks in complex wellbore structures during deep and ultra-deep well drilling operations, contributing significantly to the advancement of related drilling technologies.