This paper makes use of the RBF to calculate the interpolated displacement of vertexes on a 3D human body mesh. Through the integration of points of human body landmarks, lines, and customized measurements, a new 3D human body deformation model is created. At the same time, an RBF control point set is formed for the deformation of the 3D human body model. In addition, parametric cubic splines and bicubic surfaces patches are utilized to form a 3D garment model. This approach controls the constraints between 3D model parameters, enabling predictable control over the 3D prototype. Finally, garment design solutions under different methods are compared using SSIM, PSNR, and FID image evaluation metrics. The results show that 3D garment prototypes created using the method successfully imitate prototype designs, proving the usefulness of computer-aided garment designing solutions. The images produced through this method perform extremely well in all aspects and produce the best visual effects in garment designing.
Insects are highly adaptable and efficient in their ability to synthesize and metabolize diverse chemical compounds. The firefly Pyrocoelia spp., (Coleoptera: Lampyridae), is confronted with various chemical assaults throughout its life cycle, the defensive toxins secreted by snails and slugs during firefly larval predation, synthetic pesticides during adult stage, and the risk of auto-toxicity from endogenously produced lucibufagins. Despite the ecological, cultural and economic significance and conservation concerns surrounding fireflies, a clear understanding of their detoxification processes at gene and molecular level is lacking. This review discusses molecular basis of major detoxification enzyme superfamilies, viz., Cytochrome P450 monooxygenases (P450s), Glutathione S-transferases (GSTs), UDP-glycosyltransferases (UGTs), Carboxylesterases (CarEs), and ATP-binding cassette (ABC) transporters. Further the transcriptional regulatory networks, including various pathways that contribute to the induction and expression of these defense genes are discussed in detail. This provides a rationale and a roadmap for developing molecular biomarkers for ecotoxicological risk assessment, deriving conservation strategies for declining firefly populations, and helps in understanding the evolution of chemical adaptation in non-model insects.
For solving the problems which exist when building correct mathematical models for strong coupling, nonlinear, time-changing systems such as robot operating arms, and these problems often make traditional control methods have not good enough decoupling results and bad dynamic performance, therefore we put forward one improved RBF neural network inverse model based decoupling control method. Firstly, the particle swarm optimization algorithm is utilized by us to carry out offline optimization for initial neighborhood clustering radii, therefore obtaining relatively better network parameters. These optimized radius values are then employed for real-time nearby region grouping to dynamically build the RBF neural network reverse model, thus overcoming the restriction of traditional methods which need pre-set network structures. Second, the inverse model that has been found is connected in series with the system being controlled to construct a pseudo-linear system, therefore it enables dynamic decoupling between the joints of robotic manipulators. In the end, one PD controller is combined into a compound closed-loop control system for reducing inverse model errors and strengthening robustness. The simulation which uses a two-degree-of-freedom manipulator to carry out validation proves that our strategy can effectively realize dynamic decoupling and high-precision track following, whose tracking accuracy and robustness all exceed the traditional PD control methods.
Under the background of city-station combination, the different interaction between "fast going to work" and "slow staying" passenger streams inside big city railway hubs causes serious time-space conflicts; The traditional passive, static control approaches have difficulty in dealing with the impulse surges which are generated by incoming trains. For the solving of the zero-sum game problem that lies between traffic evacuation and commercial attraction, this thesis puts forward a dynamic collaboration optimization method for passenger flow on the basis of Model Predictive Control (MPC). Firstly, the complicated space arrangement of the hub is by us abstracted to be a direction queuing network. An innovative multidimensional Macroscopic Fundamental Diagram (MFD) evolution model—incorporating the proportions of heterogeneous passenger flows—is then constructed to precisely quantify the nonlinear frictional impedance that commercial lingering behaviors impose on main-line traffic flow. Second, a multivariable feedforward MPC architecture is established to implement proactive flow restrictions and dynamic path guidance by simulating the trajectories of congestion shockwaves through a rolling prediction mechanism. Simulation results, based on the Chongqing Shapingba Hub as a case study, demonstrate that this system can reduce the maximum queue length at core ticket gates by 58.1% and narrow the variance in travel delays by 67.6%. Furthermore, by identifying a balanced solution on the Pareto frontier—trading a marginal increase in travel delay for a substantial boost in commercial attraction—the system achieves a deeply adaptive collaboration between anti-stampede safety protocols and the economic efficiency of the urban micro-center.
To tackle such problems as the failure to consider diverse behaviors of students and improper dynamism of the curriculum system when teaching the topic of Fundamentals of Accounting, this paper presents a hybrid algorithm approach as the approach to optimize the teaching material and curriculum system. Through the integration of Gaussian mixture models and clickstream data analysis, this can be used to determine the characteristics of student learning behaviors and the model parameters are optimized using the Expectation-Maximization (EM) algorithm. According to the ARCS motivation model, the learning design is based on an all-encompassing teaching evaluation system including both formative and summative evaluations. With the help of empirical mode decomposition, students multi-dimensional behavioral time-frequency features were extracted with the use of 2022 Basic Accounting course at H University School of Accounting as an empirical subject. The connection between patterns of behavior and academic achievement was confirmed and compared the results of teaching in experimental and non-experimental classes. Results show that: The hybrid algorithm is capable of identifying the multimodal distribution features of student behavior. The non-experimental class mean (63.15), median (67), and pass rate (0.77) were significantly lower than the respective measures in the experimental class full sample. There was a significant correlation (0.201) between learning performance and formative assessment at the 1% statistical level.
