Model for Choosing the Shape Parameter in the Multiquadratic Radial Basis Function Interpolation of an Arbitrary Sine Wave and Its Application

In multiquadratic radial basis function (MQ-RBF) interpolation, shape parameters have a direct effect on the interpolation accuracy. The paper presents an MQ-RBF interpolation technique with optimized shape parameters for estimating the parameters of sine wave signals. At first, we assessed the impact of basic sinusoidal parameters on the MQ-RBF interpolation outcomes through numerical experiments. The results indicated that the angular frequency of a sine wave is a crucial determinant of the corresponding MQ-RBF interpolation shape parameters. A linear regression method was then used to establish the optimal parameter selection formula for a single-frequency sine wave, based on a large volume of experimental data. For multi-frequency sinusoidal signals, appropriate interpolation shape parameters were selected using the random walk algorithm to create datasets. These datasets were subsequently used to train several regression models, which were then evaluated and compared. Based on its operational cost and prediction accuracy, the random forest algorithm was chosen to establish the shape parameter selection model for multi-frequency sinusoidal signals. The inclusion of the Bayesian optimizer resulted in a highly accurate model. The establishment of this model enabled the adaptive selection of the corresponding shape parameters for any sine wave signal, providing a convenient means of selecting MQ-RBF interpolation shape parameters. Furthermore, the paper proposes an MQ-RBF interpolation subdivision least squares method that significantly improves the estimation accuracy of sine wave parameters. The practicality of the method was validated by successfully applying it in the calibration of the clock delay mismatch of a time-interleaved analog-to-digital converter system

An Adaptive Selection Method for Shape Parameters in MQ-RBF Interpolation for Two-Dimensional Scattered Data and Its Application to Integral Equation Solving

The paper proposes an adaptive selection method for the shape parameter in the multi-quadratic radial basis function (MQ-RBF) interpolation of two-dimensional (2D) scattered data and achieves good performance in solving integral equations (O-MQRBF). The effectiveness of MQ-RBF interpolation for 2D scattered data largely depends on the choice of the shape parameter. However, currently, the most appropriate parameter is chosen by empirical techniques or trial and error, and there is no widely accepted method. Fourier transform can linearly represent 2D scattering data as a combination of sine and cosine functions. Therefore, the paper employs an improved stochastic walk optimization algorithm to determine the optimal shape parameters for sine functions and their linear combinations, generating a dataset. Based on this dataset, the paper trains a particle swarm optimization backpropagation neural network (PSO-BP) to construct an optimal shape parameter selection model. The adaptive model accurately predicts the ideal shape parameters of the Fourier expansion of 2D scattering data, significantly reducing computational cost and improving interpolation accuracy. The adaptive method forms the basis of the O-MQRBF algorithm for solving one-dimensional integral equations. Compared with traditional methods, this algorithm significantly improves the precision of the solution. Overall, this study greatly facilitates the development of MQ-RBF interpolation technology and its widespread use in solving integral equations

Polynomial Noises for Nonlinear Systems with Nonlinear Impulses and Time-Varying Delays

It is known that random noises have a significant impact on differential systems. Recently, the influences of random noises for impulsive systems have been started. Nevertheless, the existing references on this issue ignore the significant phenomena of nonlinear impulses and time-varying delays. Therefore, we see the necessity to study the influences of random noises for impulsive systems with the above two factors. Stimulated by the above, a polynomial random noise is introduced to suppress the potential explosive behavior of the nonlinear impulsive differential system with time-varying delay. Fortunately, the stochastically controlled impulsive delay differential system admits a unique global solution, is bounded, and grows at most in the polynomial form

Polynomial Noises for Nonlinear Systems with Nonlinear Impulses and Time-Varying Delays

It is known that random noises have a significant impact on differential systems. Recently, the influences of random noises for impulsive systems have been started. Nevertheless, the existing references on this issue ignore the significant phenomena of nonlinear impulses and time-varying delays. Therefore, we see the necessity to study the influences of random noises for impulsive systems with the above two factors. Stimulated by the above, a polynomial random noise is introduced to suppress the potential explosive behavior of the nonlinear impulsive differential system with time-varying delay. Fortunately, the stochastically controlled impulsive delay differential system admits a unique global solution, is bounded, and grows at most in the polynomial form

Comparative Transcriptomics Analysis Reveals Rusty Grain Beetle’s Aggregation Pheromone Biosynthesis Mechanism in Response to Starvation

Pheromones are the basis of insect aggregation, mating, and other behaviors. Cucujoid grain beetles produce macrocyclic lactones as aggregation pheromones, yet research on their biosynthesis at the molecular level remains limited. The rusty grain beetle, C. ferrugineus, is an important economic species in China. Although two aggregation pheromone components have been identified, their suspected biosynthesis via the MVA pathway and the FAS pathway lacks molecular elucidation. Previous evidence supports that starvation affects the production of aggregation pheromones. Therefore, we constructed comparative transcriptome libraries of pheromone production sites in C. ferrugineus under starvation stress and identified genes related to pheromone biosynthesis and hormone regulation. A total of 2665 genes were significantly differentially expressed, of which 2029 genes were down-regulated in starved beetles. Putative C. ferrugineus genes directly involved in pheromone biosynthesis were identified, as well as some genes related to the juvenile hormone (JH) pathway and the insulin pathway, both of which were depressed in the starved beetles, suggesting possible functions in pheromone biosynthesis and regulation. The identification of genes involved in macrolide lactone biosynthesis in vivo holds great significance, aiding in the elucidation of the synthesis and regulatory mechanisms of cucujoid grain beetle pheromones

Remote monitoring of stored grain insect pests: Poster

A number of remote sensing methods were developed and tested in commercial grain warehouses; probe pitfall traps attached to vacuum lines, surface pit fall traps equipped with video cameras and white boards on grain surface monitored with video cameras. These methods were compared with detecting insects using grain samples. Warehouse trials by trapped methods were carried out in bins with 8520 t of wheat from 23 May until 8 August 2016.Grain temperatures were from 22.7 to 31.6?. Psocids,Liposcelis bostrychophila Badonnel,were detected by grain samples, but there were higher number of pscoids trapped with the probe pitfall traps and pitfall traps than found in grain samples. Plodia interpunctella (Hübener), Sitophlius zeamais Motchulsky and Cryptolestes ferrugineus (Stephens) were detected by probe pitfall trap, but not in the grain samples. S. zeamais was detected by the pit fall traps. Using the remote controlled video camera in the warehouse head space, we were able to distinguish and count S. zeamais, C. ferrugineus and psocids on white boards. The video from pitfall traps can be sent to mobile phones. With all these methods, data can be collected remotely, and could be analyzed by imagine analysis allowing for rapid real time monitoring of insect pests.A number of remote sensing methods were developed and tested in commercial grain warehouses; probe pitfall traps attached to vacuum lines, surface pit fall traps equipped with video cameras and white boards on grain surface monitored with video cameras. These methods were compared with detecting insects using grain samples. Warehouse trials by trapped methods were carried out in bins with 8520 t of wheat from 23 May until 8 August 2016.Grain temperatures were from 22.7 to 31.6?. Psocids,Liposcelis bostrychophila Badonnel,were detected by grain samples, but there were higher number of pscoids trapped with the probe pitfall traps and pitfall traps than found in grain samples. Plodia interpunctella (Hübener), Sitophlius zeamais Motchulsky and Cryptolestes ferrugineus (Stephens) were detected by probe pitfall trap, but not in the grain samples. S. zeamais was detected by the pit fall traps. Using the remote controlled video camera in the warehouse head space, we were able to distinguish and count S. zeamais, C. ferrugineus and psocids on white boards. The video from pitfall traps can be sent to mobile phones. With all these methods, data can be collected remotely, and could be analyzed by imagine analysis allowing for rapid real time monitoring of insect pests

A Heat and Mass Transfer Model of Peanut Convective Drying Based on a Two-Component Structure

In order to optimize the convective drying process parameters of peanuts and to provide a theoretical basis for the scientific use of energy in the drying process, this study took single-particle peanuts as the research object and analyzed the heat and mass transfer process during convective drying. In addition, a 3D two-component moisture heat transfer model for peanuts was constructed based on the mass balance and heat balance theorem. Moreover, the changes in the internal temperature and concentration fields of peanut pods during the whole drying process were investigated by simulations using COMSOL Multiphysics. The model was validated by thin-layer drying experiments, compared with the one-component model, and combined with low-field NMR technology to further analyze the internal moisture distribution state of peanut kernel drying process. The results show that both models can effectively simulate the peanut thin-layer drying process, and consistency is found between the experimental and simulated values, with the maximum errors of 10.25%, 9.10%, and 7.60% between the simulated moisture content and the experimental values for the two-component model, peanut shell, and peanut kernel models, respectively. Free water and part of the weakly bound water was the main water lost by peanuts during the drying process, the change in oil content was small, and the bound water content was basically unchanged. The results of the study provide a theoretical basis to accurately predict the moisture content within different components of peanuts and reveal the mechanism of moisture and heat migration during the drying process of peanut pods