This paper tackles key challenges in interactive design systems: high latency, weak user control, and the aesthetic-function trade-off. We propose an optimization method integrating lightweight generative networks with dynamic modeling. First, a feedforward network architecture based on the MobileNetV3 encoder and the AdaIN (Adaptive Instance Normalization) decoder is designed to achieve millisecond-level style transfer. Second, based on probabilistic state-space modeling theory, a human-machine collaborative state machine is constructed. This Markov decision process describes the transition probabilities of user operation sequences and integrates hard constraints such as readability and layout rationality. Then, a user-system co-simulation framework is proposed. A virtual user behavior simulator generates diverse interaction sequences, driving the NSGA-III (Non-dominated Sorting Genetic Algorithm III) algorithm to perform multi-objective optimization on style quality, response latency, and constraint satisfaction. Experimental results demonstrate significant improvements over baseline methods (AdaIN, WCT 2 ) in the system's consistency of style expression, real-time interaction, and design usability. After 50 generations of optimization, the average FID (Fréchet Inception Distance) value drops from 20.4 to 13.5; the interaction latency decreases from 286 ms to 187 ms; and the constraint violation rate drops from 22.3% to 5.7%, a decrease of 16.6 percentage points, validating the effectiveness of the “modeling-simulation-optimization” methodology. This method achieves a closed-loop collaboration between art generation and engineering design, providing a modeling, simulation, and optimization solution for intelligent interactive design systems.

Marine high-salt spray particles significantly accelerate vessel corrosion, with chloride ions being the primary corrosive component. This study presents a funnel-shaped intelligent sensor system integrating ion-selective electrode technology with enhanced neural network algorithms. The proposed design employs an improved Sparrow Search Algorithm-optimized Back Propagation Neural Network for temperature compensation, addressing the critical challenge of thermal drift in marine environments. Experimental results demonstrate the system's superior performance: maximum relative error of 1.786% across 0.001–1 mol/L chloride concentrations, with average error reduced to 0.972%–59.3% lower than conventional compensation methods. The sensor maintains near-theoretical sensitivity while achieving 0.02 mV/°C temperature coefficient through the proposed compensation mechanism. This advancement enables precise real-time monitoring of salt spray corrosion factors, providing a technical foundation for extending marine vessel service life through proactive maintenance strategies. A new funnel-shaped intelligent sensor is developed, which combines ion-selective electrode technology with an improved neural network algorithm. By introducing the sparrow search algorithm for optimization, the detection accuracy of chloride ions and the temperature compensation capability are enhanced, providing an efficient solution for the real-time monitoring of Marine high salt spray particles.

When the direct-drive electro-hydraulic servo system is used for position control of hydraulic actuators, load unbalance or external disturbances can cause flow imbalance, leading to poor speed stability of the actuator and thus reduced position accuracy. To address this issue, a flow stabilizing valve with a special structure is proposed, which suppresses flow fluctuations through a mechanical passive compensation mechanism. This paper elaborates on the structural innovations, working principle, and operation circuit of the flow stabilizing valve, and compares it with traditional flow control valves to highlight its advantages in low complexity and high reliability. Simulation models with and without the flow stabilizing valve are built in AMESim to analyze speed and displacement characteristics under different load disturbances. The results verify that the valve significantly improves speed stability but introduces a response lag of approximately 3 s. A test platform is constructed to test low-frequency (0.1–0.5 Hz) and low-amplitude (≤30 mm)signals, clarifying the system's applicable scenarios. The results show that the flow stabilizing valve can control the speed deviation caused by load disturbances within 0.0016 m/s by quickly adjusting the flow rate of the hydraulic cylinder, effectively improving position accuracy.

To solve the problem of user visual attention distribution and spatial misalignment of interface elements in current user interface layout design due to reliance on subjective experience, this paper proposes an optimization method for fashion brand e-commerce user interface design that uses eye tracking simulation and integrates brand characteristics. This paper builds a fashion-specific visual behavior database through multimodal eye movement data collection, and uses the spatiotemporal attention mechanism of the Transformer-XL (Transformer with Extra Long Context) model to predict the gaze hotspots and scanning paths of users in dynamic tasks; then, this paper designs a multi-agent reinforcement learning optimizer, encodes aesthetic rules such as brand logo size and main color ratio as hard constraints, and generates an interface space distribution plan through element competition-collaboration game; finally, this paper develops a real-time interactive prototype based on the Unity engine to achieve dynamic layout closed-loop optimization driven by the collaborative efforts of brand constraints and eye movement simulation heat maps. Experimental results show that this method reduces the task completion time by 32% in the optimization of the light luxury clothing homepage, the overlap between the simulated and real eye movement hot spots reaches 88%, and the brand consistency score increases by 46% (from 3.2 to 4.7), significantly reducing the user's cognitive load and improving the user experience. The conclusion confirms that by integrating eye movement behavior quantification with brand characteristics, it is possible to break through the traditional design's reliance on static aesthetics, provide a “cognitive adaptation-brand expression” dual-goal collaborative intelligent design paradigm for fashion interfaces, and promote the simultaneous improvement of user conversion rate and brand value.

Current digital media art interactive devices are difficult to adapt to complex environments due to fixed perception mechanisms, and are easily affected by noise interference and low light, resulting in reduced recognition accuracy and interaction delay, affecting the immersive experience. This study constructs a perception and sharing optimization mechanism for digital media art interactive devices driven by adaptive Artificial Intelligence (AI). A multi-channel data fusion perception network is proposed, and the Transformer encoder is used to fuse Red-Green-Blue (RGB) image, infrared depth map, and speech spectrum map features to improve perception robustness. A user behavior temporal modeling module based on Bidirectional Graph Recurrent Transformer (Bi-GRT) is designed to achieve dynamic recognition of continuous actions, gestures, and speech emotions. Environmental simulation and adaptive control mechanisms are applied, and reinforcement learning is combined to dynamically adjust sensor parameters to adapt to environmental changes. A multi-user interactive sharing framework is constructed through federated learning to ensure privacy while improving model consistency. Finally, a real-time feedback optimization and content rendering collaborative engine is developed to achieve dynamic scheduling and optimization of multi-dimensional output content based on Graph Attention Network (Bi-GRT). Experiments show that the system has an accuracy rate of over 80% in multi-action sample recognition; the environmental adaptability achieves a response delay of 210 ms in a noisy environment; the sequence similarity of each terminal in federated learning is above 0.91; the user immersion peak is 9.2 points, Compared with existing systems, this framework has achieved breakthrough progress in environmental adaptability, multi-user collaboration, and privacy protection, providing a quantifiable performance improvement path for digital media art interactive devices.

Brain tumors, abnormal growths in the brain or spinal canal, can be benign or malignant, causing symptoms like headaches, seizures, and cognitive decline by disrupting brain function. Therefore, developing reliable predictive models for diagnosis and prognosis is crucial. In this paper, the prediction of brain tumors is made using machine learning models enhanced by an optimizer, namely Escaping Bird Search Optimization. Optimized models incorporate Ada Boost Classifier (ADEB), Gaussian Process Classifier (GPEB), and Support Vector Classifier (SVC) which, after being tested on a few databases, were named ADEB, SVEB, and GPEB, respectively, and their predictive power was assessed. The best single model performance overall on all databases is the SVC with an average accuracy of 0.981, while among enhanced models, the optimized model, called SVEB, using SVC, attained the highest accuracy for all models and reached as high as 0.990. These findings underscore the role of optimization techniques and demonstrate the effectiveness of machine learning in predicting brain cancers. The improved performance of the enhanced SVC model, SVEB, suggests it could offer a reliable approach for accurate brain tumor prediction. Enhanced patient outcomes and early diagnosis could be an implication of this in the field of neuro-oncology.

Current humanistic design of urban public spaces focuses on specific design elements while ignoring the conflicts and couplings between multiple user needs. This leads to spatial strategies stuck in local optima and lacking overall balance and adaptability. This paper constructs a multi-objective optimization model that integrates user preferences, multidimensional spatial indicators, and behavioral simulation. This model collects field data such as heat maps, path trajectories, and dwell time, identifies user types through K-means clustering, and models their spatial preferences using fuzzy membership functions. Design variables are set in Grasshopper; an optimization function is constructed; the optimal solution is searched using NSGA-III. Finally, pedestrian simulation is performed in AnyLogic, and the optimization results are corrected for function deviation to improve the coordination and adaptability of the design. Experimental results show that this strategy framework significantly improves spatial coordination, increasing weighted average satisfaction from 0.61 to 0.81 (+32.8%), reducing safety risks by 30.8% to 63.2%, and increasing interaction promotion by 71.2%. Multi-dimensional indicators verify the effectiveness of the optimization strategy in balancing user needs, alleviating local conflicts, and enhancing spatial adaptability, providing a quantitative basis and practical path for systematically solving the local optimal problem of humanized design of public spaces.

With the rapid development of information technology, the application of VR to English language teaching has gradually become an emerging trend. As the driving force of English learning, the establishment of ESL vocabulary corpus is of great significance for making great progress in teaching in colleges and universities. By analyzing the characteristics of VR technology, the study establishes a data-driven teaching platform for ESL vocabulary corpus in colleges and universities based on this technology. Then, based on the improved graph neural network, the text classification of the ESL vocabulary corpus was carried out to achieve the integration of ESL vocabulary corpus resources. The results show that the improved graph neural network vocabulary text classification model converges at 145 iterations, and the convergence speed is improved by up to 100 generations; Among different datasets, the recognition accuracy of the SADE-GNN model on the MR, R8, SST1, and SUBJ datasets is 90%, 99%, 63%, and 96%, respectively. In the recognition and classification of ESL vocabulary corpora in universities, the accuracy of this model is stable at around 95%, with a maximum value of 97%. In practical teaching applications, students' English listening, reading, writing, and speaking scores have all increased to varying degrees, with the top 50% of students achieving 95 or above in both reading and speaking. The above results indicate that the teaching platform under the research model has high accuracy in text classification, can significantly improve the effectiveness of English teaching, providing new reference ideas for the construction of ESL vocabulary corpus and teaching method reform in universities.

In order to have a stronger bond with the surrounding bone, the bone prosthesis needs to have interconnecting pores for bone cells to grow and more importantly to avoid stress shielding. At the same time, human bones have different composition and structure of bone tissue in different parts of the body due to different physical factors of the person, so the elastic modulus of the bones that need to be supported and replaced are not the same. And additive manufacturing has the advantages of rapid, efficient and precise manufacturing of complex shapes and high-quality three-dimensional structures, which can manufacture porous scaffold bone prosthesis, and achieve more accurate mechanical property requirements by controlling the design parameters. To study the effect of design strut length and design strut cross-section diameter size on the elastic modulus of tetrahedral titanium alloy scaffold unit, and with the help of UG NX, several digital models of porous titanium alloy scaffolds were constructed with the strut length and the strut cross-section diameter size as the parameters of variation, and then the elastic modulus of each porous titanium alloy scaffold was measured by ANSYS Workbench 2022, and the elasticity modulus of each porous titanium alloy scaffold was further derived from the relationship between the strut length and strut cross-section diameter size and the porous titanium alloy scaffold. Then the elastic modulus of each porous titanium alloy bracket was measured by ANSYS Workbench 2022, and the mathematical model between the strut length, strut cross-section size and elastic modulus of the porous titanium alloy bracket was further derived. Then, ANSYS Workbench 2022 was used to measure the elastic modulus of each porous titanium alloy bracket and further derive the mathematical model between strut length, strut cross-section diameter size and elastic modulus of the porous titanium alloy bracket, with the help of which the elastic modulus of the porous titanium alloy bracket with specific diameters and strut lengths was finally deduced to validate the correctness of the above predicted mathematical model, and to make reasonable explanations and corrections for the deviations. explanation and correction of deviations. As a result, the rapid prototyping technology can be used to design the required porous titanium alloy bracket in a more detailed way.

This article proposes a method for matching parameters of hydro-pneumatic suspension with the goal of improving the vehicle ride comfort of mine cars. Considering the nonlinear characteristics of the hydro-pneumatic suspension, a 7-DOF dynamic model is established for the entire vehicle, The Kepler optimization algorithm is applied for parameter matching and simulation analysis of the hydro-pneumatic suspension based on vehicle ride comfort. Comparative verification shows that compared with the fitness functions of other algorithms, the KOA algorithm converges the fastest and has the highest precision in obtaining the optimal solution. After matching, there is a significant reduction in the suspension dynamic deflection, with the degree of attenuation for the four wheels at approximately 55.13%, the attenuation of vehicle body acceleration reduced by 12.7%, and the reduction in wheel dynamic load at around 10%, with a minimum of 3.16% for the right rear wheel and a maximum of 16.62% for the right front wheel. The KOA optimization algorithm exhibits significant advantages in parameter matching of hydro-pneumatic suspensions, resulting in a noticeable improvement in vehicle ride comfort.