The structural order of a histidine⁃intercalated hydrotalcite⁃montmorillonite composite was regulated by an external electric field， and the influence of the electric field on its lithium adsorption performance was systematically investigated. First⁃principles calculations were carried out using the Perdew⁃Burke⁃Ernzerhof （PBE） functional within the generalized gradient approximation （GGA） framework， and the HSE06 hybrid functional was combined to improve the accuracy of the band structure calculations. The results show that when a uniform external electric field of -0.6 V/Å to 0.2 V/Å is applied in the Z⁃axis direction， -0.2 V/Å can be regarded as the critical threshold for the “structure⁃electron” coupling behavior. At this point， the hydrogen bond interaction between the montmorillonite layer and histidine is the strongest， and the thermodynamic stability of the system is the poorest. When the electric field strength is in the range of -0.6 V/Å to -0.2 V/Å， the composite material exhibits metallic properties. When the electric field strength ranges from -0.2 V/Å to 0.2 V/Å， the composite material retains semiconductor characteristics， and the band gap increases with the variation of the external electric field strength （from -0.2 V/Å to 0.2 V/Å）. Furthermore， lithium adsorption exhibits significant site dependence， with the octahedral sites of montmorillonite showing the best adsorption stability. Applying an external electric field along the negative Z⁃axis can significantly increase the absolute value of the lithium adsorption energy of the material. When the electric field strength is -0.5 V/Å， the adsorption energy is approximately 82.8% higher than that in the absence of an electric field. A density of states analysis indicates that after lithium adsorption， the electronic band gap of the composite material expands from 0.51 eV to 2.39 eV， providing evidence of its potential application in solid electrolytes. This study provides new insights into the structure⁃activity relationship of composite materials under regulation by electric fields， and also opens up new ideas for designing high⁃performance lithium adsorption materials. It holds significant scientific value and promising application prospects.

The interaction model parameters are the key thermodynamic parameters in activity coefficient models such as Wilson and non⁃random two⁃liquid （NRTL） models. Their accuracy directly determines the reliability of the results of vapor–liquid phase equilibrium and separation process calculations. The core bottlenecks of traditional parameter acquisition methods relying on experimental data regression and universal functional activity coefficient （UNIFAC） model estimation include limited data coverage， unstable solution of nonlinear equations and limited scope of application. To address these issues， we constructed a dual⁃branch graph neural network （GNN） model based on molecular structure characterization enabling efficient and direct prediction of the parameters of the Wilson interaction model. This model takes molecular diagrams as input， separately extracts the molecular features of solutes and solvents， and， through the message⁃passing mechanism， deeply learns the potential structural correlations between molecules. At the same time， we introduce a parallel residual strategy to reduce the prediction error. Using 4 068 binary system interaction model parameter data for training and testing， our proposed parallel residual GNN model significantly outperforms the UNIFAC method across multiple error indicators， and compared with the single GNN model， the prediction accuracy is further improved. The predicted parameters A_ij， A_ji， B_ij， B_ji of the Wilson interaction model were substituted into the Wilson equation to calculate the infinite dilution activity coefficient γ {}_i^{\infty }. The results were highly consistent with the calculated value in the literature， verifying the thermodynamic rationality and engineering applicability of our model.

To address the issues of low production efficiency and poor product quality caused by the uneven flow field within a C₅ petroleum resin reactor， the computational fluid dynamics （CFD） method was employed， combined with the standard k⁃ε turbulence model， to conduct a numerical simulation of the flow field within the reactor. The results show that the velocities at the inlet and outlet of the reactor are relatively high， while the distribution within the chamber exhibits high velocity at the center and low velocity at the walls. To improve the uniformity of the flow field， three improvement schemes were proposed： optimizing the opening structure of the diffuser， reducing the diameter of the distributor holes， and adding guide cones. The results show that when the number of the diffuser opening is changed to 4 and 6， the standard deviation of the velocity at the cross⁃section at a height of 17 m in the reactor is reduced by 51.1% and 84.6%， respectively， compared to the original model. When the orifice diameter of the distributor is reduced from 19 mm to 13 mm， the standard deviation of the cross⁃sectional velocity at a height of 21 m decreases by 32.2%， and the vortices in the middle section of the chamber are reduced. Adding a single group of guide cones effectively disperses the central high⁃speed area， reducing the velocity standard deviation at a height of 21 m from 0.001 45 m/s in the original model to 0.000 559 m/s， a reduction of 61.3%. Moreover， it reduces the vortices in the streamline and improves velocity uniformity and flow stability. These results provide a scientific basis for the structural optimization of a C₅ petroleum resin reactor and may enable improved production efficiency and product quality.

The wastewater from the surface rinsing of steel products is complex in composition and has a high pollution load， posing significant environmental hazards. To enable precise supervision and traceability of the wastewater from steel surface rinsing， the rinsing wastewater from steel surfaces at a typical enterprise was selected as the research object. Aqueous fluorescence fingerprint technology based on three⁃dimensional fluorescence spectroscopy （3D⁃EEM） was employed to investigate the fluorescence characteristics of the rinsing wastewater and the influence of pH and humic acid on the fluorescence spectra. The results show that the fluorescence fingerprint spectrum of the rinsing wastewater presents six characteristic fluorescence peaks： peaks A， B， C， D， E， and F. Their excitation wavelength/emission wavelengths （Ex/Em） are 225/295 nm， 275/305 nm， 230/340 nm， 280/340 nm， 240/395 nm， and 305/395 nm respectively. Peaks A， B， C， and D are protein⁃like fluorescence peaks， and peak B has the highest fluorescence intensity. Peaks E and F are fluorescence peaks of humic substances. pH has a significant impact on the fluorescence peaks of the rinsing wastewater. As the pH value decreases， peaks D， E， and F gradually disappear. The fluorescence intensity of peak B is most sensitive to changes in pH， and reaches a maximum at neutral pH. When the mass concentration of humic acid is low （0-10 mg/L）， the number and position of the fluorescence peaks do not change significantly with concentration. When the mass concentration of humic acid is high （≥12.5 mg/L）， a new humic⁃like fluorescence peak （G peak） appears at an Ex/Em of 265-275/395 nm. As the concentration of humic acid increases， the fluorescence intensity of peak A initially increases and then stabilizes， while the fluorescence intensities of peaks E and F initially decrease and then increase. These results not only expand the fluorescence fingerprint database of the rinsing wastewater from steel surfaces， but also provide a scientific basis for the precise pollution source identification of such wastewater in complex water environments.

Using polymer polyols with polystyrene （PS） side chains （POP-S）， polyether polyol， 4，4′⁃diphenylmethane diisocyanate （MDI）， and styrene as the main raw materials， a PS⁃modified polyurethane⁃grafted interpenetrating network microporous elastomer was prepared via stepwise addition polymerisation of polyurethane （PU） and radical graft polymerization of styrene.With increasing styrene content， the tensile strength， elongation at break， and tear strength of the microporous elastomer all increased， and the material exhibits a low dynamic-static stiffness ratio and good compression set performance. When the apparent density of the microporous elastomer is 0.5 g/cm³ with a PS mass fraction of 47%， the compression set value is as low as 5%， the dynamic-static stiffness ratio is as low as 1.08， the impact resilience is 76.3%， the tensile strength is 3.14 MPa， and the elongation at break is 483%. This microporous elastomer has great potential for use in large-scale building vibration damping pads.

Polyurethane elastomers were prepared in a one-step method with polyolefin polyols bearing long side chains， liquefied diphenylmethane diisocyanate， and 2⁃ethyl-1，3-hexanediol as raw materials. The effect of polyolefin polyol content on the chemical structure， mechanical properties， water resistance， thermal stability， and thermal transition behavior of the polyurethane elastomers was investigated. The results showed that as the polyolefin polyol content decreased， the hardness of the polyurethane elastomers gradually increased， the tensile strength first increased and then decreased， and the elongation at break gradually decreased. When the polyolefin polyol content was 60%， the tensile strength reached a maximum value of 6.2 MPa， with a corresponding elongation at break of 283%. The polyurethane elastomers exhibited excellent water resistance， with a water absorption rate below 0.5% and a water contact angle greater than 98°. The initial thermal decomposition temperature （T _d5%） gradually decreased with decreasing polyolefin polyol content reaching a minimum value of 291 ℃. The soft and hard segments in the polyurethane elastomers exhibited independent glass transition temperatures that showed little variation （the differences between the maximum and minimum values were 1.3 ℃ and 0.7 ℃， respectively）， demonstrating the presence of a clear microphase⁃separated structure in the polyurethane elastomers.

Oven cooking leads to grease adhesion on inner walls and emission of oil fumes. Cleaning the walls by pyrolysis at 500 ℃ involves high energy consumption and requires long times， which remain significant challenges. A high⁃performance noble metal catalyst and catalytic self⁃cleaning coating which enables low⁃temperature oven self⁃cleaning was synthesized in this work. Pt/γ⁃Al₂O₃ catalysts with 0.2-1.0 wt.%Pt loadings were synthesized via an impregnation method using γ⁃Al₂O₃ as the support. The valence state of Pt was regulated by varying the calcination atmosphere. Using toluene as the probe gas， the H₂⁃reduced 0.2Pt/γ⁃Al₂O₃⁃R catalyst demonstrated optimal performance， achieving 90% toluene conversion at 149 ℃. The effects of catalyst type and content on grease decomposition were investigated by comparison of grease weight loss. By optimizing slurry composition and ratios， a uniform catalytic coating was applied to stainless steel substrates using a doctor blade. The effects of varying the coating thickness， catalyst content， and reaction temperature were systematically evaluated， and the results showed that the 0.2Pt/γ⁃Al₂O₃⁃R coating with 150 μm thickness heated at 250 ℃ exhibited the best grease decomposition efficiency.

The surface tribological behavior of stainless⁃steel gripping claws exerts a decisive influence on their service life. A plane-to-plane friction and wear tester was employed to simulate the surface tribological behavior of the gripping claws during actuation. Methylcellulose （MC） was applied to the contact interfaces of five stainless steel pairs to replicate the operating conditions of the gripping claws， followed by tribological testing and characterization analysis. This study investigated the effect of MC in mitigating wear of different steel materials and revealed its friction-reducing and anti-wear mechanisms. The results demonstrate that wear on the gripping claw surface is primarily concentrated at the claw tip and the front segment of the claw body. Tribological test results indicate that the friction coefficient exhibits a decreasing trend with increasing load； when the load reaches 25 N， the friction coefficient between 321 stainless steel and MC is as low as 0.014. Additionally，the steel material itself has a significant impact on the friction coefficient， with Ti-containing 321 stainless steel demonstrating the optimal friction-reducing performance. The mechanism involves friction-induced effects that facilitate the formation of adsorbed and molten transfer films of MC on the stainless steel surface. These transfer films effectively isolate the direct contact between the upper and lower friction pairs， thereby enhancing lubrication efficiency while significantly attenuating abrasive wear and adhesive wear.

Automatic fault localization techniques play a critical role in software development，but existing techniques lack in-depth characterization of program flow and code syntactic structure， making it difficult to understand deep logic and contextual information. They also lack universality in dealing with code entities of different granularity， limiting the information interaction of different levels of error localization and the comprehensive understanding of error characteristics.To addres these issues， this study proposes a graph-based fault localization framework utilizing multi-modal code representations， aiming to enhance fault localization effectiveness in two ways： firstly， leveraging multi-source information such as syntactic structure， semantic information， execution logic， and test coverage details to construct multi-modal code representation graphs using both large and small models， enhancing the ability to identify faults in complex code structures； secondly， forming multi-granularity localization models by sharing parameters between method and statement-level localization models to improve fault localization method accuracy. Experimental results conducted on 395 faults from the Defects4J dataset demonstrate that compared to the best baseline technique， our framework results in an increase of 25 correctly localized faults at the method level with a 1.12% improvement in mean first rank （MFR）， and an increase of 34 correctly localized faults at the statement level with a 5.34% improvement in MFR.

A coordinated sliding mode-fuzzy control strategy for vehicle lateral， longitudinal， and vertical dynamics has been investigated in this work. A 14-degree-of-freedom full vehicle model incorporating a steering system and a semi-active suspension system was developed. A filtered white noise method was employed to establish a four-wheel random road excitation model. A sliding mode control system for the semi-active suspension was designed with ideal sky-hook damping as a reference； optimal damping coefficients were selected by traversing specific working conditions， and anti-chattering parameters were determined through Matlab/Simulink co-simulation. A sliding mode-fuzzy coordinated controller was established by introducing fuzzy control theory. The fuzzy controller， with vertical acceleration and pitch angular acceleration as inputs and suspension damping force fine-tuning as output， adjusted the sliding mode control output to achieve three-dimensional coordination. Simulations under both random and deterministic road conditions demonstrated that compared with passive suspension， the proposed strategy optimized vertical acceleration by 28.2%， pitch angular acceleration by 24.7%， longitudinal slip ratio by 19.4%， and lateral velocity by 23.5%.

Safety education in chemical laboratories in colleges and universities suffers from problems such as weak safety awareness of students， insufficient ability to detect hazards， and a disconnection between traditional training and practice. To address these issues， we have developed a gamified training system based on virtual simulation. A high⁃fidelity chemical laboratory virtual environment was constructed based on the Unity 3D engine， and a hazard knowledge base covering six modules based on the “University Laboratory Safety Inspection Project Table （2025）” was compiled. Centered on the core training cycle of “detection⁃identification⁃action”， an interactive training system with hazard detection as the core gameplay was formed by comprehensively using gamification mechanisms such as task⁃driven mode， point reward， instant feedback and hazard randomization. The system has a total of 11 hazard inspection tasks and includes knowledge consolidation mini⁃games such as jigsaw puzzles， waste liquid handling， memory flip cards， password guessing， and whack⁃a⁃mole for the five types of core hazards. By creating immersive and interactive virtual experimental scenarios， the system transforms the passive acquisition of safety knowledge into active exploration through interactive tasks. This provides a novel and effective digital tool for cultivating students’ ability to actively discover， identify and avoid safety risks. It has practical significance for promoting innovation in university laboratory safety education models and for strengthening campus safety culture.

To address the problems of local minimum traps and unreachable targets in the traditional artificial potential field method for unmanned aerial vehicle （UAV） path planning， we propose a phased strategy of artificial potential fields. We use the traditional artificial potential field guidance in the early stage， and then integrate the global optimization characteristics of the simulated annealing algorithm to overcome the local minima. Then， at a later stage， we use improved artificial potential field guidance with an increased repulsive adaptive correction factor to address target unreachability. Finally， the key nodes of the path are extracted， and the path nodes are optimized and smoothed using cubic B-spline curves. Experimental results in a 3D environment show that the method can solve the UAV local⁃minimum trap and unreachable⁃target problem and plan a relatively smooth and safe path without colliding with obstacle. The results show that our method is feasible， effective and has potential application value.

The power generation energy efficiency of a hydropower plant is a crucial factor that significantly impacts its performance and sustainable development. Accurate and efficient evaluation of power generation efficiency is necessary if hydropower enterprises are to improve their energy efficiency. We propose a method for evaluating the power generation efficiency of hydropower plants based on integrated data envelopment analysis （DEA） and DEA cross-efficiency， combining self-evaluation and peer-evaluation paradigms to assess the efficiency of hydropower plants. Based on the main factors affecting hydropower generation， an evaluation model for power generation efficiency is constructed. The inputs of this model include monthly inflow water volume， monthly inflow water flow， monthly reservoir water storage volume， monthly reservoir water level， and the average water consumption rate for power generation， while the output is monthly power generation volume. Subsequently， the energy efficiency of the hydropower plant is analyzed and evaluated using this model. The results show that integrating of the DEA model and the DEA cross-efficiency model faciliates effective evaluation of the power generation energy efficiency of hydropower plants. Furthermore， by means of the reasonable allocation of input slack variables and output surplus variables， the optimal benchmark for the power generation status of hydropower plants can be determined and improved. This enable less effective months of the power plant to also achieve effective power generation.

In this paper， we use the cubic B⁃spline splitting method to solve the nonlinear Schrödinger equation with a quintic term. We discretize the time direction using the Strang-type splitting method and the spatial direction using the cubic B⁃spline approximate solution. It is then shown that the scheme is unconditionally stable. Finally， we show the numerical results， illustrating the charge conservation in discrete form. We also compare the exact solution with the numerical solution， verifying that the convergence order of this format is O ( {\tau }^{\mathrm{2}} + h^{\mathrm{2}} ).

The initial-boundary value problem for a one-dimensional compressible Navier-Stokes-Allen-Cahn system with the van der Waals equation of state has been investigated in this work. By addressing the challenges posed by the non-convexity of the pressure function and the singularity in density， we have established the global existence and uniqueness of strong solutions for initial data with non-vacuum density satisfying strict upper and lower bounds， as well as higher regularity conditions on the velocity and phase variables. The proof is based on energy estimates and maximum principles.